Protein Design Digest on Recep Adiyaman | Bioinformatician

Issue #91: From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design.

Fri, 17 Apr 2026 00:00:00 +0000

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From Atoms to Fragments: A Coarse Representation for Efficient and Functional Protein Design.

Although deep learning has accelerated protein design, current protein representations such as sequences or full-atom structures scale non-linearly with protein length. We propose a sparse and interpretable representation for proteins, based on evolutionarily conserved fragments. Specifically, we use a curated set of 40 functional and evolutionarily conserved fragments as an alphabet to build Fragment Graphs and Fragment Sets. These fragment-based representations are both lightweight and functionally informative, capturing up to 55% more variance using fewer than 13 of the dimensions required by traditional methods. On a dataset of 215 functionally diverse proteins, our approach creates more coherent functional clusters than traditional sequence- and structure-based methods, even among proteins with ≤30% sequence identity. Fragment-based searches of protein databases achieve accuracies comparable to traditional methods, while using 90% fewer tokens per protein. These searches execute ∼68.7× faster than RMSD-based structural methods and ∼1.64× faster than sequence-based methods, even including fragment pre-processing overhead. Additionally, we show that our representation effectively guides RFDiffusion for protein backbone generation with functional recovery rates higher than 40%. In summary, our fragment-based representation offers a scalable and interpretable alternative for the next generation of protein design tools for backbone design, sequence design, and functional similarity searches within protein structure databases. https://github.com/wells-wood-research/tessera.

Weekly Digest: Apr 13 - Apr 17, 2026

Fri, 17 Apr 2026 00:00:00 +0000

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Apr 13 - Apr 17, 2026

Issue #90: Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

Thu, 16 Apr 2026 00:00:00 +0000

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Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

De novo protein design has enabled the creation of proteins with diverse functionalities that are not found in nature. Despite recent advances, experimental success rates remain inconsistent and context-dependent, posing a bottleneck for broader applications of de novo design. To overcome this, structure and sequence prediction models have been applied to assess design quality prior to experimental testing to save time and resources. In this study, we examined the extent to which AlphaFold, Protein MPNN, and ESMFold can discriminate between experimentally successful and unsuccessful designs. We first curated a benchmark dataset of 614 experimentally characterized de novo designed monomers from 11 different design studies between 2012 and 2021. All predictive models demonstrated moderate ability to discriminate experimental successes (expressed, soluble, monomeric, and fold with the correct secondary structure) from failures. Still, many failed designs have better confidence metrics than successful designs, and confidence metrics were topology-dependent. Among all computational models evaluated, ESMFold average predicted local-distance difference test (pLDDT) yielded the best individual performance at distinguishing between successful and unsuccessful designs. A logistic regression model combining all confidence metrics provided only modest improvement over ESMFold pLDDT alone. Overall, these results show that these models can serve as an initial filtering strategy prior to experimental validation; however, their utility at accurately predicting experimentally successful designs remains limited without task-specific training.

Issue #89: Binding Affinity and Interaction Profiles of Erinacines and Erinacerins with iNOS and NF-κB Revealed by Molecular Dynamics Simulations.

Wed, 15 Apr 2026 00:00:00 +0000

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Binding Affinity and Interaction Profiles of Erinacines and Erinacerins with iNOS and NF-κB Revealed by Molecular Dynamics Simulations.

Chronic neuroinflammation driven by microglial activation is a pathological hallmark of neurodegenerative diseases, and the NF-κB/iNOS signaling axis plays a central role in propagating this damage. NF-κB-mediated iNOS transcription generates excessive nitric oxide, causing oxidative neuronal injury. The medicinal mushroom Hericium erinaceus produces cyathane diterpenoid erinacines and isoindolinone erinacerins, both reported to attenuate neuroinflammation; however, the molecular basis of their interactions with iNOS and NF-κB remains poorly characterized. We screened 21 erinacerins and 18 erinacines against both targets using validated molecular docking, then subjected top-ranked candidates and negative controls to 100 ns molecular dynamics simulations, MM-PBSA binding free energy calculations (±SEM), per-residue energy decomposition, backbone RMSD, and ligand-protein minimum distance analyses, with quercetin as reference. The analysis revealed scaffold-dependent target selectivity: erinacerins exhibited preferential stability with iNOS (erinacerin L: RMSD 0.185 nm), whereas erinacines formed more stable complexes with NF-κB (erinacines G and J: RMSD < 0.36 nm). Minimum-distance monitoring confirmed that the elevated ligand RMSD in iNOS predominantly reflected surface relocation rather than dissociation. Erinacine S emerged as the most promising dual-target candidate (ΔGbind: -24.31 ± 0.16 and -14.24 ± 0.11 kcal/mol for iNOS and NF-κB, respectively), over twofold stronger than quercetin for iNOS. Negative controls revealed that docking-based ranking was target-dependent in its discriminative capacity, underscoring the need for MD-based refinement. These results identify erinacine S as a priority candidate for experimental validation.

Issue #88: Evaluation of protein-RNA Docking Web Servers for Template-Free Docking and Comparison with the AlphaFold Server.

Tue, 14 Apr 2026 00:00:00 +0000

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Evaluation of protein-RNA Docking Web Servers for Template-Free Docking and Comparison with the AlphaFold Server.

Protein-RNA docking is a valuable tool for predicting the structures of protein-RNA complexes, which allow us to understand the structural basis for gene expression and regulation, thus facilitating drug development. Despite the development of several protein-RNA docking programs, the field remains relatively underdeveloped compared to protein-protein docking, and a systematic comparison of these programs in terms of accuracy and efficiency is still lacking. Recent advances in deep learning-based structure prediction, such as AlphaFold 3, offer a promising alternative for modeling protein-RNA complexes. Here, we have compiled a consolidated benchmark data set of 235 protein-RNA complexes (freely available at https://github.com/tanys-group/protein-rna-docking-benchmark), which were curated from PDB structures deposited up to July 2024, to assess the performance of five template-free docking web servers and the AlphaFold Server. Among the docking web servers, HDOCK performed the best, achieving success rates of 31.1% and 44.7% within the top 1 and top 5 predictions, respectively, as assessed by CAPRI (Critical Assessment of PRedicted Interactions) metrics. Although AlphaFold 3 outperformed all the docking web servers with an overall success rate of 87.0% in its top 5 predictions, it failed in nine cases where docking approaches succeeded and showed a markedly lower success rate of 40% for protein-RNA complexes outside its training set, comparable to that of HDOCK (35%). Our study provides valuable insights into the strengths and limitations of current protein-RNA docking servers and AlphaFold 3, offering practical guidance for selecting the appropriate tool for protein-RNA complex structure prediction. These results also suggest that hybrid approaches combining physics-based and machine learning methods hold significant promise for achieving higher prediction accuracy.

Issue #87: De novo protein design: a transformative frontier in clinical protein applications.

Mon, 13 Apr 2026 00:00:00 +0000

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De novo protein design: a transformative frontier in clinical protein applications.

Background Protein biologics are indispensable in disease prevention, diagnosis, and therapy, yet their development remains largely constrained by reliance on native protein scaffolds, resulting in long development timelines, limited structural and functional tunability, challenges in manufacturing consistency, and high production costs. Main body De novo protein design moves beyond the structural and functional constraints inherent to traditional approaches, enabling the direct creation of proteins with tailored structures and functions and offering a new avenue to address these challenges. In this review, we summarize the principal computational strategies underlying de novo protein design and the contribution of deep learning to its recent progress, and highlight prospective applications, major translational barriers, and the current limitations and future challenges of the field. Conclusions Despite notable methodological progress in de novo protein design, its path toward clinical application continues to be limited by a range of biological, technical, and translational considerations. Future work will need closer coordination between computational design, experimental validation, engineering optimization, and clinical needs, with clinical feasibility considered early and refined throughout development.

Issue #86: Geometric Properties of the Voronoi Tessellation in Latent Semantic Manifolds of Large Language Models

Fri, 10 Apr 2026 00:00:00 +0000

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Geometric Properties of the Voronoi Tessellation in Latent Semantic Manifolds of Large Language Models

Language models operate on discrete tokens but compute in continuous vector spaces, inducing a Voronoi tessellation over the representation manifold. We study this tessellation empirically on Qwen3.5-4B-Base, making two contributions. First, using float32 margin recomputation to resolve bfloat16 quantization artifacts, we validate Mabrok’s (2026) linear scaling law of the expressibility gap with $R^2$ = 0.9997 - the strongest confirmation to date - and identify a mid-layer geometric ambiguity regime where margin geometry is anti-correlated with cross-entropy (layers 24-28, $ρ$ = -0.29) before crystallizing into alignment at the final layer ($ρ$ = 0.836). Second, we show that the Voronoi tessellation of a converged model is reshapable through margin refinement procedures (MRP): short post-hoc optimization runs that widen token-decision margins without retraining. We compare direct margin maximization against Fisher information distance maximization across a dose-response sweep. Both methods find the same ceiling of ~16,300 correctable positions per 256K evaluated, but differ critically in collateral damage. Margin maximization damage escalates with intervention strength until corrections are overwhelmed. Fisher damage remains constant at ~5,300 positions across the validated range ($λ$ = 0.15-0.6), achieving +28% median margin improvement at $λ$ = 0.6 with invariant downstream benchmarks - a geometric reorganization that compresses the expressibility gap while preserving its scaling law. However, frequency and token-class audits reveal that gains concentrate in high-frequency structural tokens (84% of net corrections at $λ$ = 0.6), with content and entity-like contributions shrinking at higher $λ$. Fisher MRP is therefore a viable geometric polishing tool whose practical ceiling is set not by aggregate damage but by the uniformity of token-level benefit.

Weekly Digest: Apr 06 - Apr 10, 2026

Fri, 10 Apr 2026 00:00:00 +0000

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Apr 06 - Apr 10, 2026

Issue #85: BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.

Wed, 08 Apr 2026 00:00:00 +0000

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BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction.

Accurate prediction of protein-ligand bound poses and their affinities is essential in structure-based drug discovery. Here, we present an integrated deep-learning framework that disentangles the two core tasks─affinity estimation and pose evaluation─within complementary graph neural network architectures. BA-Pred is for predicting binding affinity and RMSD-Pred is for binding pose assessment, predicting the root-mean-squared deviation of ligand poses from crystal structures. Both models employ a Gated Graph Convolutional Network with Learnable Structural Positional Encoding (GatedGCN-LSPE) architecture to capture spatial and chemical dependencies across protein-ligand graphs. BA-Pred achieved state-of-the-art scoring power on the CASF-2016 benchmark with a root-mean-squared error of 1.10 p K d , while RMSD-Pred exhibited strong docking power with a top-1 success rate of 96%, comparable to the best reported deep-learning scoring functions. The robust generalization capability of RMSD-Pred was further validated on the external Astex diverse set and PoseBusters benchmarks, where it significantly improved the pose selection success rates of AutoDock-GPU by up to 33.1%. The accuracy of our methodology was demonstrated on pharmaceutical targets in the 16th Critical Assessment of Structure Prediction, where our approach ranked second in the ligand binding affinity prediction category. By using our models, an integrated pipeline was developed for virtual screening, where pose selection was performed with RMSD-Pred and binding affinities were predicted with BA-Pred. This combined approach demonstrated robust screening performance, achieving an enrichment factor (EF) 1% of 21.1 on the CASF-2016 benchmark. Furthermore, on the LIT-PCBA benchmark, rescoring poses docked by AutoDock-GPU with our pipeline significantly improved the EF 1% from 2.18 to 3.19. These various benchmark results demonstrate that our graph-neural network models show good and balanced performance in diverse protein-ligand interaction prediction tasks. Thus, we expect that our models will serve as a promising framework to accelerate the drug discovery process.

Issue #84: Salt Bridge Builder: Using Residue Distances to Predict Salt Bridge Formation.

Tue, 07 Apr 2026 00:00:00 +0000

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Salt Bridge Builder: Using Residue Distances to Predict Salt Bridge Formation.

Salt bridges contribute disproportionately to protein folding stability and protein-protein interaction energetics, yet systematic tools for engineering novel salt bridges remain limited. There are several approaches that can quantify the energetics of removing salt bridges between proteins, but no existing tools are available for adding salt bridges at protein interfaces. Here, we introduce Salt Bridge Builder (SBB), a software package that identifies candidate mutation sites for adding interprotein salt bridges using residue distance heuristics derived from large-scale structural data. Using the SKEMPI v2 database, we demonstrate that charged-to-uncharged mutations that disrupt interprotein salt bridges result in binding free energy penalties significantly larger than those of comparable mutations that do not, underscoring the stabilizing role of salt bridges at protein interfaces. We benchmark six residue distance metrics for their ability to predict salt bridge formation and show that the side-chain centroid distance (SCCD) provides the optimal balance between the predictive performance and computational efficiency. Based on these findings, we formulate an efficient algorithm that identifies putative salt bridge-forming mutations while avoiding disruption of existing electrostatic interactions. We apply SBB to the kinesin superfamily and identify kinesin-5 as uniquely enriched in potential salt-bridge-building sites at the microtubule interface. Molecular dynamics simulations of engineered kinesin-5 mutants reveal that only a subset of predicted salt bridges exhibits high occupancy, highlighting the role of local microenvironments in stabilizing engineered electrostatic interactions. Principal component analysis of the residue microenvironment distinguishes high-occupancy salt bridges, suggesting a path toward a priori stability prediction. Long-range electrostatic force calculations further show that selected mutations modulate kinesin-5-microtubule attraction. Together, this work establishes residue-distance-based salt bridge engineering as a viable protein-protein engineering strategy and provides a foundation for future extensions of SBB that incorporate microenvironment-aware stability prediction.

Issue #83: Quantum chemical, spectroscopic, molecular docking, molecular dynamics analyses and ADMET properties: Nifedipine.

Mon, 06 Apr 2026 00:00:00 +0000

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Quantum chemical, spectroscopic, molecular docking, molecular dynamics analyses and ADMET properties: Nifedipine.

Nifedipine is known as a calcium channel blocker and is used in hypertension, antianginal medication, and Raynaud syndrome. Nifedipine, which blocks voltage-dependent L-type calcium channels in the smooth muscle cells of the vessels and reduces intracellular calcium concentration, provides muscle relaxation and vasodilation. Nifedipine is used to reduce spasms in the hands and feet in Raynaud syndrome, improving blood circulation and reducing symptoms. In this study, molecular structure analyses of the nifedipine molecule began with an optimization study using the DFT/B3LYP method and continued with frequency, HOMO-LUMO, MEP, and hyperpolarizability analyses. Potential energy distribution is also presented with experimental FTIR-ATR and FT-Raman spectra. Molecular docking and molecular dynamics (MD) studies aimed to elucidate the mechanism by which nifedipine blocks voltage-dependent L-type calcium channels. Nifedipine binding stability was analyzed for 100 ns simulation time in Nifedipine-Protein Complex (Holo), protein (Apo), and Holo (POPC) system with lipid membrane media using molecular dynamics (MD) analysis. Finally, the ADMET profile of nifedipine was determined, and information on its pharmacokinetic properties was presented. This study, which includes molecular-level analyses, is a reference scientific study for drug candidates that are expected to be developed for the treatment of various diseases, such as hypertension and Raynaud syndrome.

Issue #82: AlphaFold for Docking Screens.

Fri, 03 Apr 2026 00:00:00 +0000

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AlphaFold for Docking Screens.

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Weekly Digest: Mar 30 - Apr 03, 2026

Fri, 03 Apr 2026 00:00:00 +0000

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Mar 30 - Apr 03, 2026

Issue #81: Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications

Thu, 02 Apr 2026 00:00:00 +0000

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Molecular Dynamics Simulations: Principles, Algorithms, and Emerging Applications

Molecular dynamics (MD) simulation is a fundamental technique for resolving biomolecular structures and functions at atomic resolution. Accelerated by GPU computing and machine learning-integrated force fields (FF), modern MD simulation facilitates the study of large-scale systems and rare biological events, such as protein folding, allosteric transitions, etc. While advanced sampling methods and AI integration have significantly enhanced efficiency in drug discovery and protein engineering, the field still faces challenges regarding FF accuracy, timescale constraints, and quantum effects. Continued development of hybrid quantum and molecular mechanics methods and standardized workflows is essential to further improve the predictive power and reproducibility of MD in biotechnological research. In this review, we attempted to provide the latest developments in the MD simulations.

Issue #80: A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.

Wed, 01 Apr 2026 00:00:00 +0000

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A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.

There is an urgent need to develop effective antiviral treatments against SARS-CoV-2. Despite the availability of vaccines, drug discovery remains critical for combating emerging variants. Molecular docking studies have become a vital computational tool for identifying antiviral drugs capable of inhibiting different SARS-CoV-2 proteins. This review explores the role of metal complexes as promising viral inhibitors through in silico molecular docking approaches. The binding abilities of several coordination complexes derived from iron, copper, palladium, and zinc ions have been evaluated against major viral proteins such as the spike glycoprotein, RNA-dependent RNA polymerase (RdRp), and the main protease (Mpro), which are responsible for viral infection. Comparative docking studies of specific metal-based compounds with conventional antiviral drugs highlight their superior binding affinities and inhibitory potential. Furthermore, ADME (Absorption, Distribution, Metabolism, and Excretion) analyses, molecular dynamics simulations, and drugdelivery strategies are discussed to assess pharmacokinetics and therapeutic viability. Overall, this review emphasizes the importance of molecular docking in the rational design of metal complexes as antiviral agents and its relevance for developing effective therapeutic strategies to combat COVID-19.

Issue #79: TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction

Tue, 31 Mar 2026 00:00:00 +0000

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TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction

The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value (KV) cache during inference remains a prohibitive barrier for single-GPU deployment and high-throughput generation. While 8-bit quantization is now standard, 3-bit quantization remains elusive due to severe numerical outliers in activations. This paper presents TurboESM, an adaptation of Google’s TurboQuant to the PLM domain. We solve the fundamental incompatibility between Rotary Position Embeddings (RoPE) and orthogonal transformations by deriving a RoPE-first rotation pipeline. We introduce a head-wise SVD calibration method tailored to the amino acid activation manifold, a dual look-up table (LUT) strategy for asymmetric K/V distributions, and a 1-bit Quantized Johnson-Lindenstrauss (QJL) residual correction. All experiments are conducted on ESM-2 650M, where our implementation achieves a 7.1x memory reduction (330 MB to 47 MB) while maintaining cosine similarity > 0.96 in autoregressive decoding across diverse protein families, including short peptides, transmembrane helices, enzyme active site fragments, and intrinsically disordered regions. We further implement a Triton-based fused decode attention kernel that eliminates intermediate dequantization memory allocations, achieving a 1.96x speedup over the PyTorch two-step path for the KV fetch operation alone; however, TurboESM incurs a prefill overhead of 21-27 ms relative to the original model due to KV quantization and packing, making it most suitable for memory-bound scenarios rather than latency-critical short-sequence workloads. Analysis reveals that PLMs exhibit sharper outlier profiles than large language models (LLMs) due to amino acid vocabulary sparsity, and our method effectively addresses these distributions.

Issue #78: FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories.

Mon, 30 Mar 2026 00:00:00 +0000

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FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories.

The analysis of molecular dynamics (MD) trajectories remains fragmented, requiring researchers to integrate multiple computational methods in bespoke scripts. This creates a significant barrier to reproducibility and limits analytical scope. We present FastMDAnalysis, a unified framework that establishes a reproducible, automated workflow for end-to-end trajectory analysis. The system orchestrates a comprehensive and extensible suite of core analysis modules, including root-mean-square deviation and fluctuation, radius of gyration, hydrogen bonding, solvent-accessible surface area, secondary structure assignment, dimensionality reduction, clustering, fraction of native contacts for protein folding studies, and dihedral angle analysis, within a single, consistent environment built on MDTraj, scikit-learn, and SciPy. The software natively supports all major trajectory formats, including GROMACS, AMBER, and CHARMM. We demonstrate a > 90 % $$ >90% $$ reduction in code volume for standard workflows and validate its numerical equivalence to reference implementations. FastMDAnalysis provides a methodological advance that makes rigorous, multi-analysis MD studies accessible and reproducible for the computational chemistry, biology, and biophysics communities. The software is freely available under the MIT license at https://github.com/aai-research-lab/fastmdanalysis.

Issue #77: Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

Fri, 27 Mar 2026 00:00:00 +0000

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Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

Abstract Tomato brown rugose fruit virus (ToBRFV), a tobamovirus, poses a significant threat to global tomato production due to its high infectivity, seed-borne transmission, and severe fruit symptoms. In this study, an integrative computational approach was employed to identify plant-derived phytochemicals capable of inhibiting essential viral proteins such as movement protein (MP), coat protein (CP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain. The three-dimensional structures of these viral targets were predicted using AlphaFold and subsequently validated using Ramachandran plots. A library of 2,847 phytochemicals was subjected to molecular docking, followed by MM-GBSA binding free energy calculations to evaluate binding affinity and interaction strength. Top-ranked compounds were further validated through 100-ns molecular dynamics (MD) simulations to assess complex stability and conformational behavior. Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A exhibited the strongest binding affinities toward MP, CP, Helicase, and RdRP, respectively. RMSD and RMSF analyses confirmed the stability of these complexes, highlighting persistent hydrogen-bonding interactions within the active sites. The findings underscore the potential of flavonoids as effective antiviral agents against ToBRFV and provide a foundation for future in vitro and in vivo validation studies to develop flavonoid-based antiviral formulations for sustainable tomato crop protection.

Weekly Digest: Mar 23 - Mar 27, 2026

Fri, 27 Mar 2026 00:00:00 +0000

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Mar 23 - Mar 27, 2026

Issue #76: DynaBench: Dynamic data for the docking benchmark.

Thu, 26 Mar 2026 00:00:00 +0000

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DynaBench: Dynamic data for the docking benchmark.

Protein-protein interactions are central to numerous cellular processes, including transport, signaling, and immune response. Structural modeling of protein assemblies typically relies on AlphaFold or docking methods, which produce structural models evaluated against a single experimental reference. While AlphaFold2 and its extension, AlphaFold-Multimer, have advanced complex prediction, they, and conventional docking tools, offer only static representations. However, flexibility at protein-protein interfaces is increasingly recognized as critical for function. To address this limitation, DynaBench provides a benchmark of interface dynamics in biologically relevant protein assemblies. We performed MD simulations for over 200 protein-protein complexes listed in the Docking Benchmark 5.5 (https://zlab.umassmed.edu/benchmark/), generating three 100 ns long replicas per complex. All trajectories are now publicly available online (http://www-lbt.ibpc.fr/DynaBench) via the MDposit platform (INRIA node), which is part of the EU-funded Molecular Dynamics Data Bank (MDDB). These simulations offer a unique resource for exploring interfacial flexibility, training machine learning models, redefining accuracy metrics for model evaluation, and informing the design of protein interfaces.

Issue #75: Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.

Wed, 25 Mar 2026 00:00:00 +0000

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Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.

Chalcone synthase (CHS) is the entry-point enzyme of the flavonoid biosynthetic pathway, catalyzing the first committed step toward the production of diverse bioactive metabolites with antioxidant, anti-inflammatory, and anticancer properties. Here, we conducted a comparative in silico characterization of CHS from 13 medicinal plants, with Arabidopsis thaliana included as reference species. Protein sequences retrieved from UniProtKB were aligned using ClustalW, revealing strong conservation of key motifs, particularly the catalytic triad (Cys-His-Asn), GFGPG motif, and catalytic loop. Physicochemical profiling indicated interspecies variability in predicted protein stability, hydrophobicity, and thermostability, reflecting structural adaptation rather than direct functional divergence. AlphaFold-predicted structures consistently adopted the conserved thiolase-like αβαβα-fold characteristic of type III polyketide synthases, while exhibiting species-specific variations in the substrate-binding channel architecture. These variations are interpreted as structural features that may influence substrate accommodation and selectivity. To assess functional relevance, molecular docking with p-coumaroyl-CoA further confirmed stable substrate placement within the conserved catalytic pocket across species. Furthermore, 100-ns molecular dynamics simulations of representative crystal-derived and AlphaFold-predicted CHS-ligand complexes confirmed conformational stability, which was supported by MM-PBSA calculations revealing favorable binding energetics dominated by van der Waals interactions. Collectively, this study integrates sequence, structural, and dynamic analyses to establish a computational framework for comparative CHS characterization in medicinal plants. While the findings are derived exclusively from in silico approaches, they provide structurally grounded hypotheses that may guide future experimental validation, enzyme engineering, and pathway-oriented exploration of flavonoid biosynthesis.

Issue #74: Best Practices in Mixed-Solvent Molecular Dynamics and Solvent-Site-Biased Docking.

Tue, 24 Mar 2026 00:00:00 +0000

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Best Practices in Mixed-Solvent Molecular Dynamics and Solvent-Site-Biased Docking.

In this work, we present practical recommendations for the setup, analysis, and integration of mixed-solvent molecular dynamics (MixMD), solvent-biased docking (SSBD) workflows and pharmacophore analysis, drawing on more than a decade of accumulated experience in the field from multiple implementations and applications. Rather than providing a comprehensive review of all applications of MixMD, this Perspective focuses specifically on its use as a methodological foundation for deriving solvent sites that inform docking and pharmacophore-based strategies in structure-based drug design. Currently, mixed-solvent simulations and solvent-biased docking constitute a coherent, experimentally validated strategy for identifying and exploiting binding hot spots in proteins, and for translating solvent occupancy patterns into structurally interpretable pharmacophoric features and docking constraints. By standardizing best practices, and synthesizing previously published computational studies into a unified methodological framework, we aim to facilitate broader adoption of these methods within the structure-based drug design community, enabling more reliable identification of functional sites and accelerating rational ligand discovery.

Issue #73: AlphaFold3: A Transformer in Life Sciences.

Mon, 23 Mar 2026 00:00:00 +0000

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AlphaFold3: A Transformer in Life Sciences.

The development of AlphaFold2 (AF2) marked a revolutionary milestone in the field of life sciences, such as structural and computational biology, offering highly accurate atomic-level predictions of individual protein structures using deep learning techniques. Its unprecedented performance has transformed structural biology by providing insights that were previously dependent on time-consuming experimental methods. However, despite its success, AF2 has notable limitations. It struggles with accurately modeling protein-protein interactions and fails to reliably predict the presence and positioning of non-protein components, such as nucleic acids, metal ions, ligands, and posttranslational modifications, which are critical for understanding full biological functionality. In response to these shortcomings, AlphaFold3 (AF3) has emerged as a more comprehensive solution by integrating sequence, structural, and chemical context to predict a broader range of biomolecular structures and their interactions. However, AF3 is not without limitations. It still struggles with intrinsically disordered regions, low-homology sequences, and RNA structures, particularly long or unvalidated ones. Moreover, antibody- antigen docking and flexible binding site modeling remain challenging. Addressing these gaps may require hybrid approaches that combine AF3 with experimental data, molecular dynamics simulations, or network-based models. This review explores the technical innovations underlying AF3, evaluates its current performance across different biological contexts, and presents its transformative potential in fields, such as antibodies and vaccine development for infectious diseases, cancer, and other diseases, as well as basic biological research. Finally, we highlight the remaining challenges and propose future research directions to further improve the prediction of protein complexes and other biomolecular structures.

Issue #72: Design, Synthesis, Molecular Docking, and Biological Evaluation of Tanshinone IIA Derivatives as Antibreast Cancer Agents.

Fri, 20 Mar 2026 00:00:00 +0000

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Signal of the Day

Design, Synthesis, Molecular Docking, and Biological Evaluation of Tanshinone IIA Derivatives as Antibreast Cancer Agents.

In order to explore the effect of amino introduction of Tanshinone IIA on the antitumor activity, 18 novel N-substituted tanshinone IIA derivatives were synthesized and investigated for their anti-proliferative activity in a panel of cancer cell lines. The biological evaluation of antiproliferative assay led to the discovery of compound TA-16 with a highly potent cytotoxic effect using cervical, colon, liver and breast cancer cells, with IC50 = 1.25 µM against MCF-7 cell. The mechanistic studies indicated the ability of TA-16 in inducing apoptosis of MCF-7 cells through mitochondrial pathway and arresting the cell cycle at the G0/G1 phase. It exhibited significant anti-metastasis properties by inhibiting the expression of MMP-9 and MMP-2. Moreover, the cytotoxic study of compound TA-16 on the MCF-10A, a normal human breast epithelial cell line, further highlighted the potential of compound TA-16 as an anticancer agent for breast cancer with a selectivity index of 4.95. Molecular docking analyses confirmed the binding interaction between compound TA-16 and its target proteins, validating its mechanism of action and potential as a therapeutic agent for breast cancer.

Weekly Digest: Mar 16 - Mar 20, 2026

Fri, 20 Mar 2026 00:00:00 +0000

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Mar 16 - Mar 20, 2026

Issue #71: Design, Synthesis, Molecular Dynamics Simulations, and Biological Evaluation of PB2 Inhibitors as Anti-Influenza A Virus Agent.

Thu, 19 Mar 2026 00:00:00 +0000

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Signal of the Day

Design, Synthesis, Molecular Dynamics Simulations, and Biological Evaluation of PB2 Inhibitors as Anti-Influenza A Virus Agent.

Influenza A virus continues to pose a significant global health threat, causing seasonal epidemics and occasional pandemics. Viral transcription and replication rely on the heterotrimeric polymerase complex where the PB2 subunit initiates RNA synthesis through binding to the host mRNA cap structure. In this study, we began with a structure-activity relationship analysis of the pioneering PB2 inhibitor VX-787. Through computer-aided drug design, combined with considerations of molecular docking scores, ADMET property predictions, and a prodrug esterification strategy, we ultimately designed eight novel compounds. Cytopathic effect assays demonstrated that all compounds exhibited superior inhibitory activity against both H1N1 and H3N2 strains compared to oseltamivir acid. In particular, compounds 11 and 15 displayed nanomolar-level activity against H1N1, while compound 18 showed activity against H3N2 superior to that of VX-787. These findings propose a rational design strategy that may offer new avenues for addressing the resistance and metabolic limitations associated with VX-787 and hold potential for advancing the development of next-generation PB2-targeted anti-influenza therapeutics.

Issue #70: Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.

Wed, 18 Mar 2026 00:00:00 +0000

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Signal of the Day

Molecular Dynamics-Guided Design and Chemoproteomic Profiling of Covalent Kinase Activity Probes.

Covalent small molecule probes can be powerful tools to interrogate protein activity state in native cellular environments. The design of familywide activity probes requires an understanding of the molecular sources of conserved and target-specific small molecule targeting across protein family members. Here, we developed and applied a multifaceted docking and molecular dynamics (MD) simulation pipeline to design cell-permeable covalent kinase activity probes from a set of hinge-binding pharmacophores. This computationally-guided approach yielded a series of cell-active indazole sulfonylfluorides that target a conserved catalytic lysine in active protein kinases. Chemoproteomic profiling of a lead probe, K60P, confirmed engagement of more than 100 unique native kinases across several cancer cell lines. Competitive profiling identified kinases as the predominant class of specific targets for K60P but also highlighted significant nonkinase targets for K60P and the established covalent kinase probe, XO44, underscoring the utility of native kinase profiling in situ to identify relevant targets of small molecule kinase inhibitors in cells. Dose-, time- and site-specific proteomic mapping with a known target kinase, ABL1, coupled with a Bayesian Metropolis Monte Carlo (BMMC) kinetic modeling method showed that key descriptors of covalent probe efficiency could be predicted with straightforward dose- and time-dependent covalent engagement studies and highlighted kinact/KI as a key variable to optimize for specific and broad kinase engagement. Finally, focused molecular dynamics simulations revealed that K60P, as well as the comparator probe XO44, preferentially engage with target kinases in their active, DFG-in conformations, which is driven by increasing population of reaction-ready small molecule conformation. These results together establish a computational and kinetic modeling framework for designing covalent activity probes and highlight the balance of target selectivity and kinetic efficiency as a key factor in determining their proteome-wide reactivity.

Issue #69: Molecular embedding-based algorithm selection in protein-ligand docking.

Tue, 17 Mar 2026 00:00:00 +0000

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Signal of the Day

Molecular embedding-based algorithm selection in protein-ligand docking.

Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes. MolAS is a lightweight algorithm-selection model that predicts per-algorithm performance from pretrained protein and ligand embeddings using attentional pooling and a shallow residual decoder. With hundreds to a few thousand labelled complexes, MolAS achieves up to a 15 percentage-point absolute improvement over the single best solver (SBS) and closes 17-66% of the virtual best solver (VBS)-SBS gap across five docking benchmarks. Analyses of selection frequencies, margin-conditioned reliability, and benchmark-level oracle structure indicate that MolAS is most effective when the workflow-defined oracle landscape has low winner entropy and a reasonably separable top-solver region, but degrades under protocol mismatch that shifts solver rankings and changes the induced labels. These results suggest that, in the evaluated regime, robustness is limited less by representational capacity than by workflow- and protocol-induced instability in solver hierarchies, positioning MolAS as an in-domain selector for fixed pipelines and as a diagnostic tool for assessing when docking algorithm selection is well-posed. Scientific Contribution: MolAS introduces a controlled, embedding-based selector that reduces dependence on heavy graph encoders, enabling a cleaner separation between representational choices and workflow-defined label structure. A cross-benchmark and cross-protocol analysis links selection success and failure to oracle entropy, near-ties among top solvers, and protocol-induced ranking shifts, providing an evidence-backed diagnostic account of when docking algorithm selection is likely to yield gains. The findings differentiate this work from prior docking AS studies that report in-domain improvements under a single fixed workflow by explicitly characterising protocol dependence and motivating protocol-aware modelling as a route to stronger generalisation.

Issue #68: Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.

Mon, 16 Mar 2026 00:00:00 +0000

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Signal of the Day

Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.

Over the past three years, AlphaFold-a deep learning-based protein structure prediction system-has transformed structural biology by providing near-experimental accuracy models directly from amino acid sequences. This narrative review synthesizes applications reported in the 2022-2025 literature across human, microbial, and viral systems, drawing on peer-reviewed studies as our data source. Representative examples include modeling of SARS-CoV-2 spike and nucleocapsid proteins in virology, assisting cryo-EM interpretation of bacterial ribosomal and membrane-protein complexes in microbiology, and refining conformational hypotheses for human GPCRs in biomedicine. Across these cases, AlphaFold predictions have complemented experimental workflows by accelerating hypothesis generation, improving model fitting within ambiguous density regions (poorly resolved areas of cryo-EM maps), and guiding mutagenesis strategies to probe dynamic conformational states. We also summarize recent method extensions: AlphaFold-Multimer improves multi-chain complex assembly prediction, while molecular dynamics (MD) simulations augment AlphaFold’s static models by sampling conformational flexibility and testing stability. Despite these advances, important limitations remain-particularly for intrinsically disordered regions, protein-ligand and protein-cofactor interactions, and very large or transient assemblies-and current community benchmarks indicate that approximately one-third of residues may lack atomistic precision, underscoring uncertainty in flexible or modified segments. Framed within a clear chronological window and evidence base, our analysis highlights both the practical impact and the remaining challenges of integrating AlphaFold with experiment, outlining priorities where further methodological innovation and orthogonal validation are needed.

Issue #67: How to make the most of your masked language model for protein engineering

Fri, 13 Mar 2026 00:00:00 +0000

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Signal of the Day

How to make the most of your masked language model for protein engineering

A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that choice of sampling method is at least as impactful as the model used, motivating future research into this under-explored area.

Weekly Digest: Mar 09 - Mar 13, 2026

Fri, 13 Mar 2026 00:00:00 +0000

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Mar 09 - Mar 13, 2026

Issue #66: A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Thu, 12 Mar 2026 00:00:00 +0000

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Signal of the Day

A multimodal approach integrating spectroscopy, deep learning guided molecular docking, and molecular dynamics simulation for predictive assessment of pioglitazone to albumin binding for formulation development.

Binding affinity is a critical parameter that can influence the state of the drug in vivo and help to define the formulation strategy. The current study implements a multimodal approach to analyse the binding affinity between human serum albumin (HSA) and pioglitazone. Ultraviolet (UV) absorbance and fluorescence spectrometry analyses were performed on different combinations of HSA and pioglitazone complexes, and the absorbance and fluorescence intensities were mapped to calculate the binding constant. DynamicBind, a distinct deep-learning artificial intelligence tool, was implemented to perform in silico docking studies using a non-conventional approach. Furthermore, molecular dynamics simulation was also performed to generate root mean square deviation, radius of gyration, and root mean square fluctuation values, followed by principal component analysis, probability distribution function, and free energy landscape analysis. The simulation output was analysed to interpret the binding affinity and associated conformation of the protein-active pharmaceutical ingredient (API) complex. The binding constant calculated through UV analysis was 1.1 × 10 4 M -1 . Fluorescence spectroscopic analysis derived a value of 1.7 × 10 5 M -1 . At the same time, DynamicBind predicted the cLDDT score for the top predicted model to be 0.634, and a binding affinity value of greater than 5, indicating a relatively moderate binding between pioglitazone and HSA. The results from molecular dynamics simulations further complemented our earlier observations, indicating non-covalent binding interactions and a stable protein-API complex, which is desirable for developing a formulation using HSA as a carrier polymer. This orthogonal approach also provided critical information on the fate of the API and possible considerations that needed to be made during the design of the formulation process, highlighting the need for similar approaches that could provide multifaceted advantages and help in optimising R&D costs and timelines.

Issue #65: Multispectral, Molecular Docking, and Dynamics Simulation Studies of Secalonic Acid F Binding to Human Serum Albumin.

Wed, 11 Mar 2026 00:00:00 +0000

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Signal of the Day

Multispectral, Molecular Docking, and Dynamics Simulation Studies of Secalonic Acid F Binding to Human Serum Albumin.

Secalonic acid F (SAF) is a fungal secondary metabolite with broad pharmacological activities. This study investigated the interaction mechanism between SAF and HSA through multispectral techniques, molecular docking, and molecular dynamics simulations. The results show that SAF effectively reduces the intrinsic fluorescence of HSA through static quenching and forms a stable 1:1 molar ratio SAF-HSA complex. SAF binds to the second domain site of HSA. The binding reaction is a spontaneous, exothermic process driven by enthalpy, mainly stabilized through hydrogen bonds and van der Waals forces. Spectral analysis confirmed an increase in the α-helical structure of HSA upon binding. Molecular docking and molecular dynamics simulations, including analyses of RMSD, RMSF, and Rg, further supported and elucidated the experimental results.

Issue #64: scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking.

Tue, 10 Mar 2026 00:00:00 +0000

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Signal of the Day

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking.

Summary Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell-cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand-receptor interactions from scRNA-seq data and performs structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell-cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and implementation scDock is freely available at https://doi.org/10.6084/m9.figshare.31370368 and https://github.com/Andrewneteye4343/scDock. It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian. Supplementary information Supplementary data are available at Bioinformatics online.

Issue #63: MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.

Mon, 09 Mar 2026 00:00:00 +0000

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Signal of the Day

MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.

Protein-protein interactions (PPIs) are central to cellular signaling and regulation, and their dysregulation underlies many diseases. Predicting the impact of mutations on PPI stability, quantified as ΔΔG, is essential for understanding disease mechanisms and guiding protein engineering. Here, we first present MutPPI, a graph-based deep-learning model that encodes full-residue structural features of protein-protein complexes and employs a shared GIN-GAT feature extractor for wild-type and mutant complexes. MutPPI outperforms 12 existing methods on an antibody-antigen single-point mutation dataset (S645). By integrating evolutionary information from protein language models, we further develop MutPPI-plus, achieving enhanced predictive performance. Second, we proposed a mutation-path-based data augmentation strategy, which enriches input modalities and improves generalization of both MutPPI and MutPPI-plus. After data augmentation, MutPPI-plus demonstrates state-of-the-art performance on S645 and three additional multi-point mutation datasets (SM_ZEMu, SM595, SM1124), substantially surpassing DDMut-PPI. Our analyses highlight the benefits of the multimodal framework and the physically informed data augmentation method. Together, these results provide a versatile computational tool for accurate ΔΔG prediction, advancing rational protein design.

Issue #62: Identification of Bioactive Ingredients and Mechanistic Pathways of Xuefu Zhuyu Decoction in Ventricular Remodeling: A Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations.

Fri, 06 Mar 2026 00:00:00 +0000

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Signal of the Day

Identification of Bioactive Ingredients and Mechanistic Pathways of Xuefu Zhuyu Decoction in Ventricular Remodeling: A Network Pharmacology, Molecular Docking and Molecular Dynamics Simulations.

Background Xuefu Zhuyu Decoction (XFZYD) is clinically used in China to promote blood circulation, resolve blood stasis, and alleviate ventricular remodeling (VR). However, its molecular mechanisms remain unclear. Objective This study investigates the active components and underlying molecular mechanisms of XFZYD in treating VR. Methods Targets of XFZYD’s active components and VR-related targets were identified. A protein-protein interaction (PPI) network and a drug-ingredient-target network were constructed. GO functional annotation and KEGG pathway enrichment analysis were performed to explore biological functions. Hub targets and their corresponding active ingredients were validated through molecular docking and molecular dynamics (MD) simulations. Results A total of 1,089 active ingredients with high gastrointestinal absorption (GI) and drug-likeness (DL ≥ 2) were identified. Five hundred and thirty-eight common targets were shared between XFZYD and VR, with 10 core targets, including AKT1, STAT3, TP53, EGFR, SRC, TNF, MAPK3, CTNNB1, IL6, and VEGFA. GO analysis revealed XFZYD’s influence on wound healing, oxygen response, epithelial cell proliferation, and receptor signaling. KEGG analysis highlighted key pathways such as PI3K-Akt signaling, lipid and atherosclerosis, and fluid shear stress. Molecular docking revealed that active ingredients display favorable interactions with the hub genes, with binding energies from -9.5 to -6.0 kcal/mol. These interactions were further validated through MD simulations, demonstrating stable binding throughout the 100 ns simulation period. Conclusion XFZYD exhibits therapeutic effects on VR through multiple active components and pathways, providing a scientific basis for its clinical application and further research.

Weekly Digest: Mar 02 - Mar 06, 2026

Fri, 06 Mar 2026 00:00:00 +0000

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Mar 02 - Mar 06, 2026

Issue #61: Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Thu, 05 Mar 2026 00:00:00 +0000

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Signal of the Day

Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

Issue #60: Anomalous Effect of Denaturant on Protein Unfolding Dynamics Revealed by Single Molecule Manipulation Experiments.

Wed, 04 Mar 2026 00:00:00 +0000

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Signal of the Day

Anomalous Effect of Denaturant on Protein Unfolding Dynamics Revealed by Single Molecule Manipulation Experiments.

High temperatures and chemical denaturants in bulk experiments, as well as mechanical forces in single-molecule studies, typically promote protein unfolding. In this study, we report an unexpected decrease in the unfolding rate of cold shock protein (Csp) at low concentrations of guanidine hydrochloride (GuHCl) in single-molecule magnetic tweezers experiments. This behavior contrasts with that of control protein GB1, which unfolds faster under the same denaturing conditions. Steered molecular dynamics (SMD) simulations indicate that stretching force applied to the N- and C-termini of Csp triggers an allosteric conformational change, converting loop regions into β-strands and reducing the solvent-accessible surface area (SASA). The combination of experimental and simulation data suggests that the unfolding transition state of Csp has a smaller SASA than that of the native state, providing a structural explanation for the observed kinetic anomaly. These results demonstrate that allosteric conformational or dynamical changes, triggered by mechanical or chemical perturbations, can render proteins resistant to denaturation by lowering their unfolding rates, thereby conferring resistance to environmental stress.

Issue #59: Development of DHODH inhibitors incorporating virtual screening, pharmacophore modeling, fragment-based optimization methods, ADMET, molecular docking, molecular dynamics, PCA analysis, and free energy landscape.

Tue, 03 Mar 2026 00:00:00 +0000

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Signal of the Day

Development of DHODH inhibitors incorporating virtual screening, pharmacophore modeling, fragment-based optimization methods, ADMET, molecular docking, molecular dynamics, PCA analysis, and free energy landscape.

The overexpression of dihydroorotate dehydrogenase (DHODH) in various malignant tumor cells is significantly associated with ferroptosis, making DHODH inhibition a promising strategy for cancer therapy. In this study, we employed an integrated approach to screen and optimize DHODH inhibitor candidates. First, virtual screening of the FDA-approved drug library identified 20 potential compounds (with the positive control AG-636 as a benchmark, docking score: 133.166). Subsequent pharmacophore modeling (ROC curve value >0.8) further narrowed the candidates to six compounds, which underwent fragment displacement optimization. All optimized compounds were evaluated for absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Molecular docking identified compounds 65:[(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 3-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) (docking score: 197.362) and 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) (docking score: 202.623) as high-affinity candidates. Molecular dynamics (MD) simulations, principal component analysis (PCA), and free energy landscape (FEL) analyses confirmed stable binding conformations for both compounds. Notably, compound 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) exhibited minimal conformational changes, suggesting superior binding stability. This study advances compound 66: [(4S)-2,2-dimethyl-1,3-dioxolan-4-yl]methyl 4-(4-{[(2S)-2-hydroxypropyl]oxy}phenyl) as a promising DHODH inhibitor candidate through a multimodal workflow integrating structure-based pharmacophore design, fragment optimization, ADMET profiling, and advanced molecular simulations, providing a novel avenue for DHODH-targeted antitumor therapies.

Issue #58: Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

Mon, 02 Mar 2026 00:00:00 +0000

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Signal of the Day

Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. In this study, we present an integrative computational framework that combines ensemble machine learning, molecular docking, and molecular dynamics simulations to predict and characterize the antimalarial activity of compounds from the Malaria Box database. Initially, topographical and quantum mechanical descriptors were used to construct regression models for predicting pEC 50 values, but due to the limited predictive performance in the global regression, a classification strategy was adopted, categorizing compounds into “active” and “very active” classes. The best ensemble classifier achieved robust performance (Acc 10 -fold = 0.738, Acc ext = 0.675), with good sensitivity and specificity over individual models. Subsequent regression modeling within each class yielded high predictive accuracy, with ensemble models reaching Q 2 10-fold values of 0.810 and 0.793 for the very active and active classes, respectively. To explore potential mechanisms of action, molecular docking was performed against P. falciparum Cytochrome B, revealing strong binding affinities for most compounds, particularly those forming π-π stacking and hydrogen bonds with Glu272. Molecular dynamics simulations over 200 ns confirmed the stability of several ligand-protein complexes, including unexpected behavior from compound M31, which demonstrated stable binding despite poor docking scores, suggesting a possible competitive inhibition mechanism. Binding free energy calculations further validated these findings, highlighting several promising candidates for future experimental evaluation. This integrative approach offers a powerful platform for accelerating antimalarial drug discovery by combining predictive modeling with mechanistic insights.

Issue #57: Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

Fri, 27 Feb 2026 00:00:00 +0000

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Signal of the Day

Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced drug discovery. The current study employed a robust computational pipeline integrating Machine Learning (ML) algorithms, QSAR modeling, molecular docking, molecular dynamics (MD), density functional theory (DFT), and network pharmacology to identify novel Anaplastic Lymphoma Kinase (ALK) tyrosine kinase inhibitors. An initial library of 3546 compounds from the CHEMBL4247 database was systematically filtered to 578. This screening utilized Lipinski’s rule of five, aided by QSAR and detailed PaDEL descriptor analysis. An ensemble ML model, specifically a Voting Classifier (VC) combining XGBoost, LightGBM, and ExtraTrees algorithms, attained high predictive accuracy (ROC-AUC = 0.99), facilitating a strong classification and prioritization of active leads. Molecular docking experiment identified five top hit ligands (60, 63, 124, 130, 204) having docking score ranging from -9.0 to -10.4 kcal/mol and also confirmed their strong binding affinities, which surpassed the native co-crystallized ligand used as a standard. Later on, ADMET studies were executed to explore their physicochemical properties. MD simulation trajectories and MM/PBSA analyses validated the notably conformational stability and favorable binding free energies of these hit complexes. Network pharmacology was incorporated to understand tentative mechanisms of action and potential off-targets, generating a protein-protein interaction (PPI) network. DFT-based frontier molecular orbital (FMO) analysis showed Ligand124 possessed the highest electrophilicity and optimal polarizability, consistent with its marked interaction stability in MD simulations. In addition, the molecular mechanisms of hit compounds against TC were elucidated using a network pharmacology approach, which revealed a compound-target network with crucial hub targets like AKT1 and TP53. Significant correlations with cancer-related pathways, such as PI3K-Akt and MAPK signaling, as well as key involvement in kinase activity, phosphorylation, and membrane signaling complexes, were observed by the enrichment analysis of the main targets. These comprehensive results imply that investigated hit compounds probably modulate the oncogenic signaling networks, especially those controlling cell survival, proliferation, and drug resistance, in order to achieve its anti-TC therapeutic actions. These findings highlight the fundamental ability of integrating ML and computational chemistry to accelerate therapeutic development for TC.

Weekly Digest: Feb 23 - Feb 27, 2026

Fri, 27 Feb 2026 00:00:00 +0000

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Feb 23 - Feb 27, 2026

Issue #56: A Neural Time-Series Learning Method for Accelerating Free-Energy Perturbation and Rare-Event Molecular Dynamics Simulations.

Thu, 26 Feb 2026 00:00:00 +0000

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Signal of the Day

A Neural Time-Series Learning Method for Accelerating Free-Energy Perturbation and Rare-Event Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are central to materials and drug discovery yet remain computationally demanding, particularly for free-energy perturbation (FEP) protocols and rare-event sampling. Existing sequence-based accelerators, especially Long Short-Term Memory (LSTM) models, often fail to capture long-range temporal structure and provide sufficient expressive capacity in noisy trajectories. Here, we introduce BiLSTMK-MD, a neural time-series learning method that constructs a causality-preserving surrogate for MD and FEP trajectories to reduce sampling requirements. The approach couples a sliding-window bidirectional LSTM encoder, which preserves long-range correlations, with an attention mechanism to enhance temporally informative frames, while a Kolmogorov-Arnold network output layer applies expressive nonlinear readout to decode the attention-refined representation into the final output. A two-stage, fANOVA-guided Bayesian optimization process searches for the optimal hyperparameter configuration for each system. Across four data sets, BiLSTMK-MD achieves mean absolute errors below 1.5 kcal mol-1 for window-resolved free-energy increments, reconstructs dihedral free-energy basins from 1-10% of trajectories, and maintains performance across systems. In long-trajectory regimes, it affords up to 400-fold acceleration for FEP and >700-fold speedup for rare-conformation sampling over conventional MD/FEP simulation. This neural time-series surrogate therefore provides a route to reducing sampling demands for free-energy estimation and rare-event characterization.

Issue #55: ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation

Wed, 25 Feb 2026 00:00:00 +0000

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Signal of the Day

ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation

Sequential recommendation increasingly employs latent multi-step reasoning to enhance test-time computation. Despite empirical gains, existing approaches largely drive intermediate reasoning states via target-dominant objectives without imposing explicit feasibility constraints. This results in latent drift, where reasoning trajectories deviate into implausible regions. We argue that effective recommendation reasoning should instead be viewed as navigation on a collaborative manifold rather than free-form latent refinement. To this end, we propose ManCAR (Manifold-Constrained Adaptive Reasoning), a principled framework that grounds reasoning within the topology of a global interaction graph. ManCAR constructs a local intent prior from the collaborative neighborhood of a user’s recent actions, represented as a distribution over the item simplex. During training, the model progressively aligns its latent predictive distribution with this prior, forcing the reasoning trajectory to remain within the valid manifold. At test time, reasoning proceeds adaptively until the predictive distribution stabilizes, avoiding over-refinement. We provide a variational interpretation of ManCAR to theoretically validate its drift-prevention and adaptive test-time stopping mechanisms. Experiments on seven benchmarks demonstrate that ManCAR consistently outperforms state-of-the-art baselines, achieving up to a 46.88% relative improvement w.r.t. NDCG@10. Our code is available at https://github.com/FuCongResearchSquad/ManCAR.

Issue #54: Self-Aware Object Detection via Degradation Manifolds

Tue, 24 Feb 2026 00:00:00 +0000

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Signal of the Day

Self-Aware Object Detection via Degradation Manifolds

Object detectors achieve strong performance under nominal imaging conditions but can fail silently when exposed to blur, noise, compression, adverse weather, or resolution changes. In safety-critical settings, it is therefore insufficient to produce predictions without assessing whether the input remains within the detector’s nominal operating regime. We refer to this capability as self-aware object detection. We introduce a degradation-aware self-awareness framework based on degradation manifolds, which explicitly structure a detector’s feature space according to image degradation rather than semantic content. Our method augments a standard detection backbone with a lightweight embedding head trained via multi-layer contrastive learning. Images sharing the same degradation composition are pulled together, while differing degradation configurations are pushed apart, yielding a geometrically organized representation that captures degradation type and severity without requiring degradation labels or explicit density modeling. To anchor the learned geometry, we estimate a pristine prototype from clean training embeddings, defining a nominal operating point in representation space. Self-awareness emerges as geometric deviation from this reference, providing an intrinsic, image-level signal of degradation-induced shift that is independent of detection confidence. Extensive experiments on synthetic corruption benchmarks, cross-dataset zero-shot transfer, and natural weather-induced distribution shifts demonstrate strong pristine-degraded separability, consistent behavior across multiple detector architectures, and robust generalization under semantic shift. These results suggest that degradation-aware representation geometry provides a practical and detector-agnostic foundation.

Issue #53: Predicting the active sites of quinolone antibiotics interacting with organisms by deep learning and molecular docking.

Mon, 23 Feb 2026 00:00:00 +0000

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Signal of the Day

Predicting the active sites of quinolone antibiotics interacting with organisms by deep learning and molecular docking.

Quinolones (QNs) antibiotics have become one of the most commonly used antibacterial drugs for human and animals in the world. In this study, we focused on 19 common quinolone (QN) antibiotics and collected their bioassay activity data from the PubChem website. Subsequently, using deep learning techniques, we constructed 45 biological activity prediction models based on the PubChem BioAssay dataset. The prediction accuracy of all models exceeded 95%, with the exception of the model for CCRIS mutagenicity studies, which achieved an accuracy of 85.22 ± 0.17%. Collectively, these deep learning models can serve as reliable tools for the prediction and evaluation of quinolone antibiotics. The bioassay activity of 19 QNs antibiotics was predicted by developed models to fill in the missing activity data. It was found that QNs antibiotics were generally active against bacterial DNA repair enzymes and neurobehavioral related protein, including hypothetical protein HP1089, recBCD - exodeoxyribonuclease V subunit RecBCD, recombination protein RecB and SLC5A7. Molecular dynamics simulation results showed that all fluoroquinolone complexes with HP1089, recBCD, RecB, and SLC5A7 reached stable conformations after an initial 0-10 ns relaxation, Our research provides a theoretical basis and technical support for elucidating the regulatory mechanisms of organisms in response to environmental exogenous chemicals, the formulation of environmental protection and food safety policies, the risk assessment of novel compounds, and the development of eco-friendly pharmaceuticals.

Issue #52: A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide-Ligand Interaction.

Fri, 20 Feb 2026 00:00:00 +0000

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Signal of the Day

A New Insight into the Study of Neural Cell Adhesion Molecule (NCAM) Polysialylation Inhibition Incorporated the Molecular Docking Models into the NMR Spectroscopy of a Crucial Peptide-Ligand Interaction.

The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM) is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and aggressive clinical status. During the NCAM polysialylation process, polysialyltransferases (polySTs), such as polysialyltransferase IV (ST8SIA4) or polysialyltransferase II (ST8SIA2), can catalyze the addition of CMP-sialic acid (CMP-Sia) to the NCAM to form polysialic acid (polySia). In this study, the docking models of polysialyltransferase IV (ST8Sia4) protein and different ligands were predicted using Alphafold 3 and DiffDock servers, and the prediction accuracy was further verified using the NMR experimental spectra of the interactions between polysialyltransferase domain (PSTD), a crucial peptide domain in ST8Sia4, and a different ligand. This combination strategy provides new insights into a quick and effective screening for inhibitors of tumor cell migration.

Weekly Digest: Feb 16 - Feb 20, 2026

Fri, 20 Feb 2026 00:00:00 +0000

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Feb 16 - Feb 20, 2026

Issue #51: Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.

Thu, 19 Feb 2026 00:00:00 +0000

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Signal of the Day

Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.

Zika virus (ZIKV) remains a global health threat, for which no licensed antiviral treatment has been available. In this study, we employed in silico approaches to optimize monoclonal antibodies targeting the Zika virus envelope protein (ZIKV E) in the Domain III (DIII) region, which is crucial for receptor binding and virus entry. A high-resolution crystal structure of ZIKV E in complex with the neutralizing antibody ZV-64 was used as a template for designing a library of antibody variants through targeted double-point mutations. The variants were systematically evaluated for stability, binding affinity, solubility, and protein-protein interaction potential using FoldX, DeepPurpose, SoluProt, and molecular docking. Among all the mutants, Variants-213 and -206 were identified as the top candidates, exhibiting the most favorable predicted binding affinity and solubility compared to the control antibody. The molecular dynamics simulations further revealed the structural stability of the two mutant variants, in which Variant-206 showed a predicted binding energy (-76.90 kcal/mol) along with higher conformational flexibilities. The findings demonstrate the use of computational antibody engineering to identify potentially high-affinity therapeutics against ZIKV, providing a foundation for future experimental validation and therapeutic development against ZIKV.

Issue #50: Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.

Wed, 18 Feb 2026 00:00:00 +0000

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Signal of the Day

Molecular docking: a computational approach for the discovery of novel targets against visceral leishmaniasis.

The protozoan parasite Leishmania donovani is a major causative agent of visceral leishmaniasis (VL), a lethal disease posing significant public health challenges globally. Existing anti-VL drugs have become increasingly ineffective due to rising drug resistance, underscoring the urgent need for novel and effective therapeutic candidates. Computational approaches offer rapid and systematic methods for identifying potential drug targets and supporting rational drug design. This review discusses in silico molecular docking studies targeting various Leishmania proteins and their inhibitors, alongside the in vitro and in vivo validation of selected compounds, emphasizing their crucial roles in advancing antileishmanial drug discovery. In the review, we have focused on a molecular docking study and explored potential compounds with high binding energy toward protein targets of Leishmania. Following the in silico screening, our review highlights compounds that exhibit both in vitro and in vivo antileishmanial properties, allowing for an assessment of their therapeutic efficacy. Different Software is available for molecular docking, has been mentioned in the review. Overall conclusion of this review supports the computational approach in drug discovery before the in vitro and in vivo study, which can save cost and time efficiency as well.

Issue #49: AlphaFold2-Guided Cyclic Peptide Stabilizer Design to Target Protein-Protein Interactions.

Tue, 17 Feb 2026 00:00:00 +0000

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Signal of the Day

AlphaFold2-Guided Cyclic Peptide Stabilizer Design to Target Protein-Protein Interactions.

The control and modulation of protein-protein interactions (PPIs) is of central importance for the majority of biological processes and most biomedical applications. Stabilization of PPIs, besides inhibition, is of growing pharmaceutical interest. Due to their small size, drug-like organic molecules may not provide sufficient interaction surfaces to allow for high-affinity dual binding to both partners of a protein-protein complex. Cyclic peptides offer larger interaction surfaces, making them a promising class of stabilizers of PPIs. We have developed a computational protocol to rapidly and systematically design cyclic peptides that optimize not only the interaction with one target protein but simultaneously optimize the dual binding to two protein partners. The cyclic peptide generation is based on a modified AlphaFold2-based peptide design approach and combines confidence scoring with force field-based scoring using Molecular Dynamics simulations. The performance of the method is tested on protein-protein complexes with known cyclic peptide binders and stabilizers. In addition, the approach is used to design cyclic peptides that can act as bifunctional molecules, recruiting the cellular protein degradation system to a target protein. The designed cyclic peptides achieve similar or better calculated interaction scores than known binders and exhibit well-balanced interactions with both protein partners. The design protocol is generally applicable to cyclic peptide design for modulating or inducing protein-protein association and could be useful for many biomedical design efforts.

Issue #48: DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.

Mon, 16 Feb 2026 00:00:00 +0000

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Signal of the Day

DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.

Motivation Protein structure prediction has been revolutionized and generalized with the advent of cutting-edge AI methods such as AlphaFold, but reliance on computationally intensive multiple sequence alignments (MSA) remains a major limitation. Results We introduce DeepFold-PLM, a novel framework that integrates advanced protein language models with vector embedding databases to enhance ultra-fast MSA construction, remote homology detection, and protein structure prediction. DeepFold-PLM utilizes high-dimensional embeddings and contrastive learning, significantly accelerate MSA generation, achieving 47 times faster than standard methods, while maintaining prediction accuracy comparable to AlphaFold. In addition, it enhances structure prediction by extending modeling capabilities to multimeric protein complexes, provides a scalable PyTorch-based implementation for efficient large-scale prediction. Our method also effectively increases sequence diversity (Neff = 8.65 versus 4.83 with JackHMMER) enriching coevolutionary information critical for accurate structure prediction. DeepFold-PLM thus represents a versatile and practical resource that enables high-throughput applications in computational structural biology. Availability and implementation Source codes and user-friendly Python API of all modules of DeepFold-PLM publicly available at https://github.com/DeepFoldProtein/DeepFold-PLM.

Issue #47: Deconvolving mutation effects on protein stability and function with disentangled protein language models.

Fri, 13 Feb 2026 00:00:00 +0000

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Signal of the Day

Deconvolving mutation effects on protein stability and function with disentangled protein language models.

Understanding how evolutionary constraints shape protein sequences is fundamental to deciphering the molecular mechanisms underlying protein stability and function, which has broad implications in protein engineering and therapeutics development. Recent advances in protein language models (pLMs) have enabled accurate prediction of mutation effects through evolutionary information, effectively capturing the selective pressure that governs protein sequence variation. A critical challenge, however, remains in disentangling the intertwined mutation effects on protein stability and function, as evolutionary signals conflate both stability-driven and function-driven pressures, obscuring the mechanistic basis of mutation effects and limiting their utility for rational protein engineering. In this work, we introduce DETANGO, a novel deep learning framework that explicitly deconvolves the mutation effects on protein functions by removing components attributable to stability perturbations from the pLM-predicted mutation effects. Guided by computational or experimental stability measurements, DETANGO estimates a functional plausibility score for each single-point mutation that is the component of the mutation effect not accounted for by changes in stability. Single-point mutations with low functional plausibilities are predicted to be stable-but-inactive (SBI) variants, whose compromised activities are caused by direct perturbations on functional mechanisms rather than structural stability. Residues enriched for such variants are inferred to be functionally critical, as indicated by the strong evolutionary pressures to maintain protein function. Through extensive benchmarking experiments, we show that DETANGO accurately identifies SBI variants and pinpoints functionally important residues across contexts, including ligand binding, catalysis, and allostery. Moreover, extending DETANGO from individual proteins to homologous protein families reveals shared and distinctive functional patterns across protein families. Collectively, these results establish DETANGO as a biologically grounded framework for disentangling evolutionary constraints on protein stability and function, advancing mechanistic understanding of protein function, and informing rational protein engineering.

Weekly Digest: Feb 09 - Feb 13, 2026

Fri, 13 Feb 2026 00:00:00 +0000

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Feb 09 - Feb 13, 2026

Issue #46: Unfreezing structural biology for drug discovery.

Thu, 12 Feb 2026 00:00:00 +0000

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Signal of the Day

Unfreezing structural biology for drug discovery.

Structure-based drug discovery relies on three-dimensional protein structures to provide the atomic blueprints for small-molecule design, indicating where to place each atom to maximize favorable interactions. The advent of cryo-cooling crystals in crystallography greatly accelerated the ease and accessibility of structural data, making it a mainstay of most drug discovery efforts. However, despite its successes, including producing numerous clinically successful molecules, cryo-cooled samples only tell part of the structural story: they may leave out dynamic details or introduce artifacts that may lead drug discovery campaigns astray. In this Perspective, we highlight recent studies characterizing temperature-sensitive structural phenomena observed by crystallography. We showcase how leveraging information on rare, hidden conformational states informs ligand discovery via molecular docking. This demonstrates the value of performing structural studies at elevated temperatures, closer to where biology occurs, to ‘unfreeze’ structural ensembles for drug discovery and design.

Issue #45: De novo protein design: a transformative frontier in clinical protein applications.

Wed, 11 Feb 2026 00:00:00 +0000

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Signal of the Day

De novo protein design: a transformative frontier in clinical protein applications.

Issue #44: Immunoinformatics and molecular docking reveal potential multi-epitope vaccine against Pseudomonas aeruginosa.

Tue, 10 Feb 2026 00:00:00 +0000

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Signal of the Day

Immunoinformatics and molecular docking reveal potential multi-epitope vaccine against Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a common opportunistic pathogen and a leading cause of hospital-acquired pneumonia, yet there is currently no approved vaccine to prevent its infections. This study utilizes immunoinformatics to identify cytotoxic T-lymphocyte (CTL) epitopes derived from conserved regions of 6 key virulence factors: Pili, FliD, AlgF, PelG, Exoenzyme T, and XcpQ. Conserved peptide fragments were identified using the Protein Variability Server. The CTL epitopes were evaluated for immunogenicity, antigenicity, post-translational modifications, allergenicity, cross-reactivity, toxicity, and population coverage analysis. Molecular docking between human leukocyte antigens (HLAs) and the corresponding CTL epitopes, along with binding affinity analysis, was also conducted. A multi-epitope vaccine (PaMEV) construct was designed using selected epitopes, and its secondary and tertiary structures were predicted, refined, and validated. All selected epitopes were highly conserved (Shannon index ≤0.1) and showed strong HLA binding (half maximal inhibitory concentration ≤500 nM). They were predicted to be non-allergenic, non-toxic, and non-cross-reactive. Molecular docking revealed stable HLA-epitope complexes with 8-14 hydrogen bonds and high binding affinity (values of the binding free energy <0 and dissociation constant <100 nM). A PaMEV was designed using the 6 CTL epitopes, and structure analysis confirmed its stability and effective epitope presentation. The selected epitopes showed strong potential for inclusion in a peptide-based PaMEV, with favorable immunogenicity and docking results supporting its design. The final construct exhibited structural stability and strong HLA interactions, suggesting it as a promising vaccine candidate against P. aeruginosa. Experimental validation through in vitro and in vivo studies is recommended.

Issue #43: From Atoms to Cells: AI-Based Structure Prediction Fueling Molecular Dynamics Simulations in Computational Structural Biology.

Mon, 09 Feb 2026 00:00:00 +0000

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Signal of the Day

From Atoms to Cells: AI-Based Structure Prediction Fueling Molecular Dynamics Simulations in Computational Structural Biology.

The simulation of biological systems has undergone a revolutionary transformation, progressing from modeling single proteins to entire cellular environments. This leap forward is driven by the convergence of molecular dynamics (MD) simulations and artificial intelligence (AI)-powered structure prediction. Traditionally, MD simulations provided atomic-level insights into protein function and interactions, yet their accuracy relied on experimentally determined structures. AI-based models, such as AlphaFold, now enable the rapid and accurate prediction of protein structures, expanding the scope of simulations beyond isolated biomolecules to complex assemblies. However, a structure alone is not sufficient to capture biological function. Molecular motion underlies almost all cellular processes, from enzyme catalysis to signal transduction. MD simulations breathe life into static models, revealing dynamic conformational changes and mechanistic pathways. With computational power and AI capabilities, we are now approaching the long-sought goal of simulating entire cellular processes with unprecedented resolution. This chapter explores how AI and MD are bridging the gap between static snapshots and dynamic cellular models, paving the way for whole-cell simulations. The ability to computationally reconstruct cellular behavior at the molecular scale is poised to transform biological research, drug discovery, and synthetic biology, marking an era in which digital cells become a fundamental tool in scientific exploration.

Issue #42: De novo protein design enables targeting of intractable oncogenic interfaces

Fri, 06 Feb 2026 00:00:00 +0000

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Signal of the Day

De novo protein design enables targeting of intractable oncogenic interfaces

ABSTRACT Protein-protein interactions (PPIs) involving oncogenic drivers remain among the most intractable targets in cancer biology due to their dynamic conformations and limited accessibility to conventional small molecules. Although antibodies and indirect inhibitors have achieved clinical success against targets such as PD-1/PD-L1 and MYC, challenges persist related to tissue penetration, intracellular delivery, resistance, and incomplete blockade of key interface hotspots. Here, we present DesignForge, an integrated de novo protein design framework that combines deep-learning-based structure generation, sequence optimization, and energetic hotspot mapping to create compact miniprotein binders for PPIs. Using this approach, we engineered PD-1 mimetics predicted to disrupt the PD-1/PD-L1 immune checkpoint, designed scaffolds targeting the MYC/MAX dimerization interface, and generated KRAS binders in a manner predicted to occlude RAF interaction. The top designs showed high structural confidence by AlphaFold2, favorable stability metrics, and consistent hotspot engagement identified through MOE-based analyses. Collectively, these results establish DesignForge as a generalizable in silico platform for rational design of therapeutic protein binders that extend beyond antibody and small-molecule modalities to systematically target intractable oncogenic PPIs.

Weekly Digest: Feb 02 - Feb 06, 2026

Fri, 06 Feb 2026 00:00:00 +0000

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Feb 02 - Feb 06, 2026

Issue #41: High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Thu, 05 Feb 2026 00:00:00 +0000

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Signal of the Day

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 makes a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals.

Issue #40: Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

Wed, 04 Feb 2026 00:00:00 +0000

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Signal of the Day

Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.

Background Ochratoxin A (OTA), a common food-borne mycotoxin, is a potential human carcinogen, yet the specific molecular mechanisms linking it to hepatocellular carcinoma (HCC) remain unclear. Methods We integrated network toxicology to predict OTA targets and intersected them with HCC transcriptomic data to identify key candidate genes. Functional enrichment analysis was then conducted. Multiple machine learning algorithms were applied to screen and validate core genes. Furthermore, molecular docking and molecular dynamics (MD) simulations were employed to evaluate the binding stability between OTA and key target proteins. Results A total of 50 key genes were identified as potential targets for potential OTA-associated hepatocarcinogenesis. Enrichment analysis revealed their significant involvement in critical processes such as xenobiotic metabolism and oxidative stress response. Machine learning analysis prioritized eight core genes (AURKA, GABARAPL1, CA2, PARP1, LMNA, SLC27A5, EPHX2, and GSTP1), and a combined diagnostic model demonstrated outstanding performance (AUC = 0.986). Structural analyses via molecular docking and MD simulations confirmed stable binding interactions between OTA and these core targets. Conclusions This integrated computational study identifies a set of candidate genes through which OTA may potentially interact with HCC-associated molecular networks. The robust binding predicted between OTA and the core targets provides a structural basis for these interactions. These findings offer a prioritized list of targets and a theoretical framework for subsequent experimental validation and investigation into OTA’s toxicological role in HCC.

Issue #39: Comparison of In Vitro Multiple Physiological Activities of Cys-Tyr-Gly-Ser-Arg (CYGSR) Linear and Cyclic Peptides and Analysis Based on Molecular Docking.

Tue, 03 Feb 2026 00:00:00 +0000

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Signal of the Day

Comparison of In Vitro Multiple Physiological Activities of Cys-Tyr-Gly-Ser-Arg (CYGSR) Linear and Cyclic Peptides and Analysis Based on Molecular Docking.

Peptide cyclization is a strategy to improve biological stability and functional activity, but direct comparison between linear and cyclic peptides with the same sequence is still limited. In this study, linear (L-CR5) and cyclic (C-CR5) forms were synthesized, and biological functions such as antioxidant, whitening, and anti-wrinkle activity were compared and evaluated. C-CR5 showed about 22.3 times of DPPH radical scavenging activity, which was significantly stronger than L-CR5, and tyrosinase inhibition increased rapidly in C-CR5 to reach inhibition of 95% or more, whereas L-CR5 showed only moderate activity in the same range (about 6.5 times). MMP-1 expression in the evaluation of anti-wrinkle activity did not show a decreasing trend in L-CR5 at all, while C-CR5 showed an anti-wrinkle effect, which was reduced by about 92.8% at 400 μg/mL. As a result of molecular docking analysis, C-CR5 exhibited lower MolDock scores than L-CR5 toward both tyrosinase and MMP-1, indicating a potentially higher binding affinity and improved binding stability. This is expected to be due to reduced structural flexibility and optimized residue directions (especially Tyr and Arg). These results indicate that peptide cyclization is an example of enhanced functional bioactivity of CYGSR and provides a positive case for the structure-activity relationship.

Issue #38: Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

Mon, 02 Feb 2026 00:00:00 +0000

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Signal of the Day

Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

Issue #37: Efficacy of Ipflufenoquin against Strawberry Gray Mold: Insights from AlphaFold- Based Structural Modeling and Genome-Wide Transcriptomic Analysis.

Sat, 31 Jan 2026 00:00:00 +0000

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Signal of the Day

Efficacy of Ipflufenoquin against Strawberry Gray Mold: Insights from AlphaFold- Based Structural Modeling and Genome-Wide Transcriptomic Analysis.

Gray mold caused by Botrytis cinerea significantly threatens strawberry production. This study evaluated the efficacy of ipflufenoquin, a novel dihydroorotate dehydrogenase (DHODH) inhibitor, against fungal pathogens isolated from Korean strawberry fields in 2023. Ipflufenoquin demonstrated a high in vitro sensitivity to B. cinerea and broad activity against other pathogens. Fruit and greenhouse trials confirmed its robust control of gray mold, including strains resistant to multiple fungicide classes. However, treatment shifted the fungal community, promoting less sensitive genera, such as Cladosporium and Rhizopus. Structural modeling with AlphaFold2 and molecular docking confirmed that ipflufenoquin binds to the quinone binding tunnel of DHODH, correlating binding affinity with susceptibility. Additionally, RNA-seq analysis revealed that ipflufenoquin suppresses primary metabolic pathways while triggering a robust stress response, up-regulating detoxification and efflux transporter genes. This integrated study confirms the efficacy of ipflufenoquin against gray mold and elucidates its molecular impacts, offering essential data for sustainable management strategies.

Issue #36: Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.

Fri, 30 Jan 2026 00:00:00 +0000

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Signal of the Day

Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.

Protein language models (pLMs) have become essential tools in computational biology, powering diverse applications from variant effect prediction to protein engineering. Central to their success is the use of pretrained embeddings-contextualized representations of amino acid sequences-which enable effective transfer learning, especially in data-scarce settings. However, recent studies have revealed that standard masked language modeling objectives used to train these models often produce representations that are misaligned with the needs of downstream tasks. While scaling up model size improves performance in some cases, it does not universally yield better representations. In this study, we investigate two complementary strategies for improving pLM representations: (i) integrating text annotations through contrastive learning, and (ii) combining multiple embeddings via embedding fusion. We benchmark six text-integrated pLMs (tpLMs) and three large-scale pLMs across six biologically diverse tasks, showing that no single model dominates across settings. Fusion of multiple tpLMs embeddings improves performance on most tasks but presents a computational bottleneck due to the combinatorial number of possible combinations. To overcome this, we propose greedier forward selection, a linear-time algorithm that efficiently identifies near-optimal embedding subsets. We validate its utility through two case studies, homologous sequence recovery and protein-protein interaction prediction, demonstrating new state-of-the-art results in both. Our work highlights embedding fusion as a practical and scalable strategy for improving protein representations.

Weekly Digest: Jan 26 - Jan 30, 2026

Fri, 30 Jan 2026 00:00:00 +0000

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Jan 26 - Jan 30, 2026

Issue #35: PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.

Thu, 29 Jan 2026 00:00:00 +0000

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Signal of the Day

PepScorer::RMSD: An Improved Machine Learning Scoring Function for Protein-Peptide Docking.

Over the past two decades, pharmaceutical peptides have emerged as a powerful alternative to traditional small molecules, offering high potency, specificity, and low toxicity. However, most computational drug discovery tools remain optimized for small molecules and need to be entirely adapted to peptide-based compounds. Molecular docking algorithms, commonly employed to rank drug candidates in early-stage drug discovery, often fail to accurately predict peptide binding poses due to their high conformational flexibility and scoring functions not being tailored to peptides. To address these limitations, we present PepScorer::RMSD, a novel machine learning-based scoring function specifically designed for pose selection and enhancement of docking power (DP) in virtual screening campaigns targeting peptide libraries. The model predicts the root-mean-squared deviation (RMSD) of a peptide pose relative to its native conformation using a curated dataset of protein-peptide complexes (3-10 amino acids). PepScorer::RMSD outperformed conventional, ML-based, and peptide-specific scoring functions, achieving a Pearson correlation of 0.70, a mean absolute error of 1.77 Å, and top-1 DP values of 92% on the evaluation set and 81% on an external test set. Our PLANTS-based workflow was benchmarked against AlphaFold-Multimer predictions, confirming its robustness for virtual screening. PepScorer::RMSD and the curated dataset are freely available in Zenodo.

Issue #34: Tailored pyrrole-based imidazothiazole scaffolds: Synthetic elaboration, enzyme kinetic profiling and DFT-guided molecular docking toward Antidiabetic therapeutics.

Wed, 28 Jan 2026 00:00:00 +0000

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Signal of the Day

Tailored pyrrole-based imidazothiazole scaffolds: Synthetic elaboration, enzyme kinetic profiling and DFT-guided molecular docking toward Antidiabetic therapeutics.

The current research study highlights the successful biological evaluation of novel imidazo-thiadiazole based pyrrole derivatives, with the aim of targeting diabetes mellitus through alpha-amylase and alpha-glucosidase inhibition. These compounds exhibited promising anti-diabetic activity, notably compound 8 emerged as a leading candidate (3.50 ± 0.20, and 4.10 ± 0.10 µM) which outperformed the potential of acarbose (6.20 ± 0.10 and 6.70 ± 0.20 µM), a reference drug. The enhanced biological potential of compound 8 is likely due to incorporation of hydroxyl substituents, which may strengthen its binding affinity and selectivity towards the targeted enzymes. Molecular docking revealed stable interactions with key amino acids residues of targeted enzymes, providing mechanistic basis for its potent inhibitory activity. To further established their therapeutic relevance, enzyme kinetic study was conducted which confirmed their mode of inhibition while ADMET analysis indicated favorable pharmacokinetics and safety profiles. Moreover, pharmacophore modeling and molecular dynamics simulations reinforced the stability and binding efficiency of lead compounds under dynamic biological conditions. All the experimental results and in silico validations demonstrate that potent compounds possess significant anti-diabetic activity profile. Their ability to outperform an existing diabetes mellitus inhibitor and maintaining a favorable safety profile suggest that these compounds have potential to be further used in drug development and optimization against Diabetes Mellitus.

Issue #33: ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

Tue, 27 Jan 2026 00:00:00 +0000

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Signal of the Day

ModelCIF update: Supporting Emerging Classes of Computational Macromolecular Models.

The recent development of highly accurate protein structure prediction tools has led to a rapid expansion in the scope of computational structural biology, enabling a much wider range of modelling studies than ever before. These new in silico opportunities help life science researchers understand how proteins interact with their environment and support design of new molecules with desired properties. Ultimately, they have broad applications, e.g. in medicine, drug discovery or engineering. To ensure reproducibility and to facilitate data exchange and reuse, predicted structures or computed structure models can be stored using ModelCIF, a rich data representation designed to include the atomic coordinates/metadata. The previously published version of ModelCIF (1.4.4; 2022-12-21) mainly covered protein structure predictions generated by homology and ab initio modelling. In this work, we present an extension of the ModelCIF (https://github.com/ihmwg/ModelCIF) data standard and its associated tools. This extension supports important new use cases, including modelling protein-ligand and protein-protein interactions, sampling multiple conformational states and designing proteins de novo. We define guidelines for storage and validation of modelling results for those use cases by applying new and existing ModelCIF categories to capture protocols, inputs and outputs. Additionally, we outline updates to the software tools and resources that implement these new standards and provide functionality for model generation, validation, archiving, and visualisation. By enabling consistent metadata capture across different modelling workflows, this framework aims to support the FAIR dissemination of computational models, thereby promoting reproducibility and reusability in downstream applications.

Issue #32: Energy-Driven Innovations in Computational De Novo Protein Engineering.

Mon, 26 Jan 2026 00:00:00 +0000

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Signal of the Day

Energy-Driven Innovations in Computational De Novo Protein Engineering.

Energy models play a crucial role in the advancement of computational de novo protein engineering, enabling the design of novel proteins with tailored functionalities. Proteins serve as the foundation of biochemical processes, making their precise engineering essential for applications in biotechnology, medicine, and synthetic biology. Unlike traditional approaches that focus on modifying existing proteins, de novo engineering introduces entirely new constructs, a paradigm shift driven by energy-based strategies that guide protein folding, stability, and functionality through comprehensive simulations of energy landscapes. Computational techniques such as molecular dynamics (MD), thermodynamic integration, and Monte Carlo sampling are fundamental in evaluating designed proteins’ stability and dynamic behavior. Widely used tools such as CHARMM, Amber, and Rosetta leverage advanced energy functions to optimize protein structures, facilitating accurate predictions of folding pathways and binding affinities. Additionally, the integration of machine learning (ML) and deep learning (DL) has significantly improved the speed and precision of energy-based modeling, enhancing the design and optimization process. This review systematically analyzes recent studies, provides quantitative benchmarking of major computational platforms, and presents a decision framework for method selection based on accuracy-cost-throughput trade-offs. By integrating classical force fields, quantum mechanical approaches, and AI-driven predictions with experimental validation, this work outlines a roadmap for advancing therapeutic and industrial protein design through synergistic physics-based and data-driven strategies.

Issue #31: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Sun, 25 Jan 2026 00:00:00 +0000

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Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Objective This study presents a methodology employing virtual screening to identify curcumin derivatives with selective affinity for the HER2 mutations L755S, T798I, and T798M. Methods Curcumin derivatives were retrieved from the ChEMBL database and filtered using KNIME. HER2 mutations were modeled in silico using MOE software with PDB ID 3RCD. Molecular docking and dynamics simulations were conducted to screen high-affinity compounds and evaluate binding interactions. Result From 505 curcumin derivatives, the RDKit module implemented in KNIME successfully filtered 317 compounds. Subsequent molecular docking against wild-type HER2 identified 100 curcumin derivatives with low docking scores, among which the top 20 compounds exhibited better binding affinities than Lapatinib. Further molecular docking screening against the three HER2 mutations identified five lead compounds with the lowest docking scores. Molecular docking and molecular dynamics simulation revealed critical binding interactions with residues essential for kinase domain stability. Chemical structural analysis revealed key modifications, such as geranyl and tripeptide modifications. CHEMBL3758656 and CHEMBL3827366, two curcumin derivatives, demonstrated consistent binding across HER2 mutations and a favorable ADMET profile. Conclusion This study successfully identified CHEMBL3758656 and CHEMBL3827366 as promising HER2 inhibitors through comprehensive virtual screening. Their high binding affinity against L755S, T798I, and T798M mutations and favorable ADME and toxicity properties underscore their potential as alternative therapeutics for HER2-positive breast cancer.

Issue #30: DynaBench: Dynamic data for the docking benchmark.

Sat, 24 Jan 2026 00:00:00 +0000

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Signal of the Day

DynaBench: Dynamic data for the docking benchmark.

Protein-protein interactions are central to numerous cellular processes, including transport, signaling, and immune response. Structural modeling of protein assemblies typically relies on AlphaFold or docking methods, which produce structural models evaluated against a single experimental reference. While AlphaFold2 and its extension, AlphaFold-Multimer, have advanced complex prediction, they, and conventional docking tools, offer only static representations. However, flexibility at protein-protein interfaces is increasingly recognized as critical for function. To address this limitation, DynaBench provides a benchmark of interface dynamics in biologically relevant protein assemblies. We performed MD simulations for over 200 protein-protein complexes listed in the Docking Benchmark 5.5 ( https://zlab.umassmed.edu/benchmark/), generating three 100 ns long replicas per complex. All trajectories are now publicly available online ( http://www-lbt.ibpc.fr/DynaBench) via the MDposit platform (INRIA node), which is part of the EU-funded Molecular Dynamics Data Bank (MDDB). These simulations offer a unique resource for exploring interfacial flexibility, training machine learning models, redefining accuracy metrics for model evaluation, and informing the design of protein interfaces.

Issue #29: Mechanistic Investigation of Astragalus Root in the Management of T2DM-NAFLD Comorbidity: An Integrated Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Study

Fri, 23 Jan 2026 00:00:00 +0000

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Signal of the Day

Mechanistic Investigation of Astragalus Root in the Management of T2DM-NAFLD Comorbidity: An Integrated Network Pharmacology, Molecular Docking, Molecular Dynamics Simulation, and In Vitro Study

Background: /Objectives: Astragalus root is a classical qi-tonifying traditional Chinese medicine that has demonstrated potential therapeutic efficacy in T2DM and NAFLD. However, the precise mechanisms underlying its effects on the comorbidity of these two disorders remain unclear. This study investigated the molecular mechanisms by which astragalus root ameliorated T2DM-NAFLD comorbidity. Methods: Network pharmacology, molecular docking, molecular dynamics simulation, and in vitro experiments were employed to elucidate the potential roles and mechanisms of astragalus root in the management of T2DM-NAFLD comorbidity. Results: A total of 25 bioactive constituents and 152 corresponding targets associated with astragalus root were identified. PPI network analysis revealed the top ten core candidate targets, among which six possessed suitable crystal structures for molecular docking, including IL-6, AKT1, JUN, TNF, CASP3, and ESR1. KEGG analysis further identified the PI3K-AKT as the most significantly en-riched pathway. Molecular docking of the principal bioactive constituent formononetin from astragalus root with the six core targets was conducted using AutoDock4 software. Molecular dynamics simulations verified the stability of the interactions between for-mononetin and each of the six core target proteins. In vitro experiments demonstrated that formononetin obviously decreased lipid droplet accumulation, downregulated TC and TG levels, suppressed the expression of TNF-α, IL-6, and IL-1β, decreased ROS and MDA levels, and enhanced GSH content and SOD activity. These therapeutical effects were achieved through inhibition of protein expression within the PI3K/AKT/mTOR signaling pathway. Conclusions: This study determined the potential therapeutic targets and underlying mechanisms of formononetin derived from astragalus root in the T2DM-NAFLD management, thereby providing a scientific basis for its clinical application.

Weekly Digest: Jan 19 - Jan 23, 2026

Fri, 23 Jan 2026 00:00:00 +0000

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Jan 19 - Jan 23, 2026

Issue #28: Predicting the Mechanism of Action of Bawei Chufan Soup in Treating Teen Depression through Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.

Thu, 22 Jan 2026 00:00:00 +0000

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Predicting the Mechanism of Action of Bawei Chufan Soup in Treating Teen Depression through Network Pharmacology, Molecular Docking and Molecular Dynamics Simulation.

Introduction The Bawei Chufan Soup (BWCFS) in Traditional Chinese Medicine (TCM) offers unique advantages in treating Teen Depression (TD). This study utilizes network pharmacology, molecular docking, and molecular dynamics simulations to predict the material basis and mechanism of action of the decoction. Methods The TCMSP, SwissADME, and SwissTargetPrediction databases were utilized to obtain the active ingredients and targets of the BWCFS. The GeneCards, OMIM, and Disgenet databases were used to identify disease targets, and the intersection of these sets was determined using the VENNY tool. The intersecting targets were imported into the String database for protein- protein interaction analysis and the screening of core targets. GO and KEGG enrichment analyses of the intersecting targets were conducted using the David database, and drugcomponent- target-pathway network diagrams were constructed using Cytoscape 3.10.0 software. The molecular docking models of the core components and key targets were generated using AutoDock Vina, and kinetic simulations were conducted using GROMACS 2020.3, paired with the best docking models. Results After screening, the study identified the core components of BWCFS as Baicalein, Kaempferol, Quercetin, Cerevisterol, and Cavidine, with the key targets for TD being AKT1, IL6, TNF, ESR1, and IL1B. GO enrichment analysis revealed that BWCFS may affect signal transduction in the treatment of TD, and is associated with cellular components such as the plasma membrane and dendrites, as well as the regulation of protein binding. KEGG analysis suggested that the intersecting genes are primarily enriched in the cyclic adenosine monophosphate (cAMP) signaling pathway. Molecular docking results indicated that AKT1 shows good binding affinity with Baicalein, Cavidine, Kaempferol, and Quercetin, while Cerevisterol exhibits strong binding with TNF. The molecular dynamics simulations were stable and reliable. During the protein-ligand complex simulation, the binding between the protein and ligand was stable, with van der Waals interactions as the primary force, while hydrogen bonds were present between both the protein and ligand. Discussion Though this study has several common limitations associated with network pharmacology, and no animal experiments have been conducted for verification, the study has successfully explored and validated the mechanism of action of BWCFS in treating TD using scientific computational methods. This study provides new perspectives and methods for the development and management of pharmacological treatments for TD, offering innovative insights into TCM approaches for its treatment. Conclusion Through network pharmacology, this study preliminarily predicted the material basis and mechanism of action of BWCFS in treating TD. Furthermore, the therapeutic effects of BWCFS on TD may be associated with neuroinflammation and structural and functional changes in neuronal dendrites. The cAMP-PKA-NF-κB and cAMP-PI3K-AKT-NF-κB pathways are proposed as potential therapeutic targets.

Issue #27: AlphaFold can help African researchers to do cutting-edge structural biology.

Wed, 21 Jan 2026 00:00:00 +0000

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AlphaFold can help African researchers to do cutting-edge structural biology.

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Issue #26: MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Tue, 20 Jan 2026 00:00:00 +0000

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Signal of the Day

MetalloDock: Decoding Metalloprotein-Ligand Interactions via Physics-Aware Deep Learning for Metalloprotein Drug Discovery.

Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDock, the first DL-based docking framework specially designed for metalloprotein targets. By innovatively integrating an autoregressive spatial decoding engine with a physics-constrained geometric generation paradigm, MetalloDock can precisely reconstruct metal coordination geometries and accurately capture metal-ligand interactions, which enhance both the accuracy of metalloprotein-ligand docking and binding affinity prediction. Extensive evaluations on our custom-built benchmark data set demonstrate that MetalloDock outperforms existing methods, including AlphaFold3, in docking success rate and virtual screening performance for metalloprotein targets. In real-world applications, MetalloDock successfully identified multiple novel hit compounds in a virtual screening campaign targeting the prostate-specific membrane antigen. Additionally, it enabled rational drug design for acidic polymerase endonuclease, leading to the discovery of potent inhibitors. These results highlight the broad applicability of MetalloDock in accelerating metalloprotein-targeted drug discovery and provide a standardized framework for future evaluation of metalloprotein-specific docking algorithms.

Issue #25: Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Mon, 19 Jan 2026 00:00:00 +0000

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Signal of the Day

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery as CYP450-mediated metabolism profoundly influences drug efficacy, safety, and adverse reaction risks. However, experimental determination of CYP450-ligand interactions remains resource- and time-intensive, underscoring the need for robust computational alternatives. While ligand-based methods are commonly employed, they often fail to fully account for structural intricacies governing protein-ligand interactions. To address this gap, we developed a hybrid machine learning framework integrating ligand descriptors, protein descriptors, and protein-ligand interaction descriptors that include molecular docking-derived parameters, rescoring function components from multiple algorithms, and structural interaction fingerprints (SIFt). Evaluated on CYP1A2 and CYP17A1 isoforms, our model demonstrated superior predictive accuracy in cross-validation compared with stand-alone molecular docking and ligand-based approaches. Furthermore, benchmarking against state-of-the-art tools (SwissADME and ADMETlab 3.0) revealed enhanced performance in binding prediction. This work establishes a versatile framework for advancing computational tools to prioritize CYP450 binding assessments during drug discovery.

Issue #24: In Silico Discovery of RIOK3 Inhibitors Against Pancreatic Ductal Adenocarcinoma Using Homology Modelling, Molecular Docking, Molecular Dynamics Simulations, ADMET Prediction, and MTT assay

Fri, 16 Jan 2026 00:00:00 +0000

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In Silico Discovery of RIOK3 Inhibitors Against Pancreatic Ductal Adenocarcinoma Using Homology Modelling, Molecular Docking, Molecular Dynamics Simulations, ADMET Prediction, and MTT assay

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer strongly linked to RIO Kinase 3 (RIOK3), which promotes progression by stabilizing and phosphorylating Focal Adhesion Kinase (FAK). Advances in protein structure prediction, particularly AlphaFold2, have significantly enhanced our understanding of protein dynamics, aiding in the identification of potential inhibitors for targeted therapies. This study used structure-based virtual screening, molecular dynamics simulations, ADMET/toxicity prediction, and in vitro validation to identify potential inhibitors of RIOK3 for PDAC treatment. The 3D structure of RIOK3 was predicted using AlphaFold2 and docked with FDA-approved drugs via AutoDock Vina. Pharmacokinetic and pharmacodynamic properties were assessed with SwissADME, and in vitro validation was performed using MTT assays to assess cell viability and growth inhibition. Four top-scoring compounds were identified, with binding energies between − 11.3 and − 10.4 kcal/mol. Venetoclax showed the most stable complex with RIOK3, followed by Conivaptan and Irinotecan. Drospirenone showed weaker binding. Molecular dynamics simulations and MM/GBSA analysis supported the stability of these complexes. SwissADME and ProTox-II confirmed that the compounds met drug-likeness criteria but exhibited distinct pharmacokinetic and toxicity profiles. In vitro MTT assays showed concentration-dependent growth inhibition in PANC-1 cells, with Conivaptan having the lowest IC₅₀ value. This study identifies RIOK3 as a promising therapeutic target for PDAC, with Venetoclax, Conivaptan, Drospirenone, and Irinotecan as repurposable candidates for further research. Further studies should include biochemical assays, expanded cytotoxicity profiling in multiple PDAC cell lines, and in vivo evaluations to validate RIOK3-targeted therapies for PDAC treatment.

Weekly Digest: Jan 12 - Jan 16, 2026

Fri, 16 Jan 2026 00:00:00 +0000

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Jan 12 - Jan 16, 2026

Issue #23: Benchmarking co-folding methods to predict the structures of covalent protein-ligand complexes.

Thu, 15 Jan 2026 00:00:00 +0000

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Benchmarking co-folding methods to predict the structures of covalent protein-ligand complexes.

Targeted covalent inhibitors (TCIs) are emerging as a new modality in drug discovery because of their strong binding affinity and prolonged target engagement. However, the rational design of TCIs remains a significant challenge and is hindered by the lack of methods that accurately predict the structures of covalent protein-ligand complexes. Recent advances in co-folding approaches have made substantial strides in modeling complex biomolecular structures. Despite significant progress, their performance profiles for predicting the structures of covalent protein-ligand complexes remain largely unexplored because of the absence of rigorous benchmarks. Here, we introduce CoFD-Bench, a comprehensive benchmark dataset comprising 218 recently resolved covalent complexes designed to systematically evaluate both classical docking methods (AutoDock-GPU, CovDock, and GNINA) and deep learning co-folding models (AlphaFold3 (AF3), Chai-1, and Boltz-1x). Our results demonstrate that co-folding methods achieve superior ligand RMSD accuracy and protein-ligand interaction recovery. However, their performance markedly declines for novel pocket-ligand pairs. In contrast, classical docking methods exhibit stable but modest performance, which is primarily limited by target conformations. Furthermore, computational efficiency evaluations show that co-folding methods are slower than classical approaches, posing challenges for large-scale predictions. We also reveal that AF3 has the potential to identify native covalent residues through noncovalent co-folding, with a ligand RMSD comparable to that of covalent co-folding. These findings offer a possible route to explore covalent binding without prior specification of reactive residues, which are often unknown in real-world scenarios. Our study provides crucial insights and new opportunities for future co-folding-based TCI design, informing future model applications and improvements. CoFD-Bench offers rigorous evaluation criteria, diverse docking scenarios, and various methodological baselines, positioning it as an important benchmark for future model development and assessment.

Issue #22: Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.

Wed, 14 Jan 2026 00:00:00 +0000

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Advantages and Limitations of AlphaFold in Structural Biology: Insights from Recent Studies.

Issue #21: Geometric deep learning assists protein engineering. Opportunities and Challenges.

Tue, 13 Jan 2026 00:00:00 +0000

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Geometric deep learning assists protein engineering. Opportunities and Challenges.

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function. This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies. By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

Issue #20: Synthesis, Anticancer Evaluation, and Molecular Docking of Triazolylmethyl-Dihydroquinazolinyl Benzoate Derivatives as Potential PARP-1 Inhibitors.

Mon, 12 Jan 2026 00:00:00 +0000

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Synthesis, Anticancer Evaluation, and Molecular Docking of Triazolylmethyl-Dihydroquinazolinyl Benzoate Derivatives as Potential PARP-1 Inhibitors.

Quinazolinone derivatives have emerged as promising scaffolds in medicinal chemistry due to their broad spectrum of biological activities, including anticancer potential. Incorporation of triazole rings through click chemistry has further boosted the pharmacological relevance of such compounds, due to the triazole’s stability, bioisosterism, and ability to engage in key interactions with biological targets. Motivated by these properties, a library of 24 triazolylmethyl-dihydroquinazolinyl benzoate (TDB) derivatives (7a-x) was synthesized using a click chemistry strategy, starting from anthranilamide and phthalic anhydride. The structures of the synthesized compounds were established through IR, 1 H NMR, 13 C NMR, and HRMS spectral analysis. The anticancer potential of all derivatives was evaluated by using SRB assay, with compounds 7j and 7q displaying notable activity, with GI 50 values of 22 and 48 µg/mL, respectively. In addition, compounds 7a, 7e, 7f, 7l, 7u, 7v, and 7x displayed moderate activity, with GI 50 values ranging from 58 to 77 µg/mL. In addition, molecular docking studies were performed using poly(ADP-ribose) polymerase-1 as the target enzyme, and the results confirmed that the TDB derivatives exhibited strong binding affinity. Furthermore, molecular dynamics simulations were conducted to evaluate the stability of the docked complexes, specifically for compounds 7j and 7q, which confirmed that the TDB derivatives formed stable interactions with poly(ADP-ribose) polymerase-1.

Issue #19: Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

Sun, 11 Jan 2026 00:00:00 +0000

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Integrative Network Pharmacology and Molecular Docking-Based Validation of Berberine as a Therapeutic Agent in Arsenic-Induced Cardiotoxicity.

Exposure to arsenic (As) is a serious environmental and public health risk because it can cause systemic toxicity, which could lead to serious cardiovascular disease like heart failure, arrhythmias, and coronary heart disease (CHD). Exploring safer and multi-target therapeutic agents is gaining popularity as a result of the shortcomings of traditional therapies. The isoquinoline alkaloid berberine which is derived from plants, exhibits strong anti-inflammatory, antioxidant, and cardioprotective properties. This study employs an integrated network pharmacology and molecular docking approach to investigate the molecular mechanisms and therapeutic potential of berberine in arsenic-induced cardiotoxicity. Key genes target arsenic-induced cardiotoxicity and berberine, have been identified using the Swiss Target Prediction, Gene Cards, OMIM, and CTD databases. A protein-protein interaction (PPI) network was generated by analysing frequently intersecting genes with the STRING and Cytoscape tools. Shiny GO was used to conduct pathway enrichment analysis for the KEGG and Gene Ontology databases. Auto Dock was used to assess berberine’s binding affinity. Berberine and arsenic-related cardiotoxicity shared 17 common targets. The primary targets were identified using Cytoscape ABL-1 (2G2F), CDK2 (1HCK), CYP19A1 (3EQM), ICAM-1 (4G6J), KIT (1T45), MAPK14 (3PY3), PGR (1A28), PTGS2 (5F19), RAC1 (3TH5), and SRC (2SRC). Enrichment analysis revealed TNF, VEGF, and AGE-RAGE signaling involvement, all of which are linked to oxidative stress, inflammation, and endothelial dysfunction. Binding affinity between berberine and the target was found to be ABL-1 (-9.2 kcal/mol), PTGS2 (-8.8 kcal/mol), SRC (-8.7 kcal/mol), CYP19A1 (-8.6 kcal/mol), KIT (-8.3 kcal/mol), RAC1 (-7.9 kcal/mol), CDK2 (-7.5 kcal/mol), ICAM-1 (-7.2 kcal/mol), MAPK (-6.8 kcal/mol), PGR (-5.6 kcal/mol). Berberine has multi-targeted therapeutic potential for arsenic-induced cardiotoxicity by modulating inflammatory and oxidative pathways. These results could support the possible usage of berberine in the treatment of cardiovascular diseases caused by arsenic and provide a mechanistic link for further experimental validation.

Issue #18: Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

Sat, 10 Jan 2026 00:00:00 +0000

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Integrated QSAR, molecular docking, and dynamics-based discovery of a potent selective HDAC1 inhibitor with therapeutic potential in aggressive cancers.

This research introduces a comprehensive computational and experimental approach aimed at the systematic design of selective Histone Deacetylase 1 (HDAC1) inhibitors, which hold therapeutic promise for treating aggressive cancers. A comprehensive Quantitative Structure-Activity Relationship (QSAR) model was constructed utilizing 1168 experimentally validated HDAC1 inhibitors, incorporating molecular descriptors associated with hydrogen bonding, steric, and electronic properties. The validated model, with a R 2 of 0.80 and a Q 2 of 0.80, was utilized for the virtual screening of the ChemDiv HDAC library, successfully identifying high-potential hits. The leading compounds underwent receptor-based molecular docking with the HDAC1 crystal structure (PDB ID: 4BKX), which highlighted essential interactions such as zinc ion coordination and π-π stacking. Notably, compound 0356-0096 demonstrated a higher binding affinity than the reference inhibitor vorinostat. Molecular dynamics (MD) simulations conducted over a duration of 500 ns demonstrated the stability of the complex and a decrease in flexibility, as evidenced by analyses of Root Mean Square Deviation (RMSD) and Fluctuation (RMSF). The analysis of simulation trajectories through Principal Component Analysis (PCA) and the mapping of the Free Energy Landscape (FEL) revealed stable low-energy conformations that align with thermodynamically favorable binding conditions. The results of ADMET profiling demonstrated that the lead compounds exhibit good oral bioavailability, low toxicity, and favorable metabolic stability. Validation through in vitro methods using the MTT assay on MDA-MB-231 (triple-negative breast cancer) and A431 (epidermoid carcinoma) cell lines revealed significant, dose-dependent cytotoxic effects, with IC 50 values of 2.7 μM and 91.6 nM, respectively. The computed Selectivity Index (SI) demonstrated a preferential cytotoxic effect on cancer cells in comparison to normal NRK kidney cells. This integrative QSAR-docking-MD-FEL-MTT approach effectively identified compound 0356-0096 as a potent and selective HDAC1 inhibitor. By combining predictive computational models with empirical validation, it provides a structured pathway for the preclinical development of targeted epigenetic cancer therapeutics.

Issue #17: Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Fri, 09 Jan 2026 00:00:00 +0000

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Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Peroxisome proliferator-activated receptor γ (PPARγ) is a key therapeutic target for type 2 diabetes and cardiovascular diseases due to its central role in regulating glucose and lipid metabolism. While full PPARγ agonists exhibit efficacy, they are linked to adverse effects; in contrast, PPARγ partial agonists retain metabolic regulatory functions with improved safety, representing promising candidates for type 2 diabetes treatment. However, their action mechanisms and structure-activity relationships remain unclear. Herein, we developed an integrated virtual screening strategy combining fragment molecular orbital (FMO) calculations, machine learning, molecular docking, interaction fingerprint (IFP) filtering, and molecular dynamics (MD) simulations to identify potential PPARγ partial agonists and elucidate their interaction mechanisms. FMO analysis first confirmed interaction differences between PPARγ agonist classes at the binding pocket, pinpointing critical residues (CYS285, ARG288, ILE341, and SER342) for partial agonist activity. Using three machine learning algorithms (random forest, extra trees, and XGBoost) with extended connectivity fingerprints (ECFP), we constructed QSAR classification models and screened 9630 compounds. SHAP analysis highlighted key fingerprint fragments (positions 45, 1034, and 1243) governing bioactivity. Molecular docking and IFP refinement yielded six high-potency candidates, whose binding stability and partial agonist properties were validated via MD simulations, MM/PBSA binding free energy calculations, hydrogen bond analysis, and FMO calculations. Notably, these candidates did not directly interact with the AF2 domain, consistent with the canonical partial agonist mode of action. This multidisciplinary approach provides a framework for rational design of novel PPARγ partial agonists, and the identified molecules serve as promising leads for type 2 diabetes therapeutics.

Weekly Digest: Jan 05 - Jan 09, 2026

Fri, 09 Jan 2026 00:00:00 +0000

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Jan 05 - Jan 09, 2026

Issue #16: Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Thu, 08 Jan 2026 00:00:00 +0000

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Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Issue #1: Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

Wed, 07 Jan 2026 00:00:00 +0000

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Discovery of PPARγ Partial Agonists for Treatment of Type 2 Diabetes Based on an Integrated Virtual Screening Strategy that Combines Fragment Molecular Orbital Calculations, Machine Learning, Molecular Docking, Interaction Fingerprint Filtering, and Molecular Dynamics Simulations.

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Issue #15: SMARTDock: A Toolkit for the Automated Development of Target-Specific Scoring Functions Using Bioactivity Data.

Wed, 07 Jan 2026 00:00:00 +0000

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SMARTDock: A Toolkit for the Automated Development of Target-Specific Scoring Functions Using Bioactivity Data.

Molecular docking has become an essential tool in the early stages of structure-based drug discovery, enabling rapid virtual screening of large compound libraries against biological targets. However, the accuracy of binder selection is often limited by the available scoring functions. Here, we present a novel workflow SMARTDock (Scoring with Machine learning and Activity for Ranking Targeted Docking) that enhances the virtual screening capabilities of GOLD docking by integrating publicly available bioactivity data, a protein-ligand interaction fingerprint (PADIF), and machine learning classification models within a user-friendly Docker environment. This platform-independent approach enables seamless use on different operating systems and is accessible to both computational and medicinal chemists. With only a ChEMBL target ID, a protein structure file, and a SMILES list of testing compounds, users can build and apply target-specific scoring models to improve the enrichment of active compounds in the top ranks. SMARTDock implements the PADIF-based ML methodology to assist in virtual screening. Previous validation of this underlying methodology demonstrated its capacity to enhance screening performance across multiple targets. Finally, we show the advantages and disadvantages in the bioactive classification in virtual screening tasks.

Issue #1: AlphaFold for Docking Screens.

Tue, 06 Jan 2026 00:00:00 +0000

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AlphaFold for Docking Screens.

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Issue #1: AlphaFold for Docking Screens.

Mon, 05 Jan 2026 00:00:00 +0000

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AlphaFold for Docking Screens.

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Issue #13: AlphaFold for Docking Screens.

Mon, 05 Jan 2026 00:00:00 +0000

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AlphaFold for Docking Screens.

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Issue #14: Assessing the validity of leucine zipper constructs predicted by AlphaFold.

Mon, 05 Jan 2026 00:00:00 +0000

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Signal of the Day

Assessing the validity of leucine zipper constructs predicted by AlphaFold.

AP-1 transcription factors are a network of cellular regulators that combine in different dimer pairs to control a range of pathways involved in differentiation, growth, and cell death. They dimerize via leucine zipper coiled-coil domains that are preceded by a basic DNA binding domain. Depending on which AP-1 transcription factors dimerize, different DNA sequences will be recognized resulting in differential gene expression. The affinity of AP-1 transcription factors for each other dictates which dimers form. The relative concentration of AP-1 transcription factors varies with tissue type and environment, adding another layer of control to this integral network of cellular regulation. The development of artificial intelligence (AI)-based protein structure prediction methods gives us a new technique to investigate or predict how dimerization affects combinatorial control. All versions of AlphaFold2 and AlphaFold3 are AI/deep learning programs that predict 3D structures of proteins from an amino acid sequence and multiple sequence alignments of homologous proteins. To fully realize the potential of AI for structural biology, it is essential to understand its current capabilities and limitations. In this study, we used the classical example of an AP-1 dimer: Fos and Jun, and an array of over 2000 experimentally tested human leucine zippers to interrogate how AlphaFold models leucine zipper domains and if AlphaFold can be used to differentiate between probable and improbable dimer interfaces. We found that AlphaFold predicts highly confident leucine zipper dimers, even for dimer pairs such as the FosB homodimer, for which electrostatics are known to prevent their formation in vivo. This is an important case study concerning high-confidence but low-accuracy protein structure prediction.

Issue #12: AlphaFold for Docking Screens.

Sun, 04 Jan 2026 00:00:00 +0000

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AlphaFold for Docking Screens.

🧬 Abstract

Issue #13: AlphaFold for Docking Screens.

Sun, 04 Jan 2026 00:00:00 +0000

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Signal of the Day

AlphaFold for Docking Screens.

Weekly Digest: Dec 28 - Jan 04, 2026

Sun, 04 Jan 2026 00:00:00 +0000

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Dec 28 - Jan 04, 2026

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sat, 03 Jan 2026 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

The development of small-molecule tyrosine kinase inhibitors remains a high-priority strategy in modern oncology, particularly those targeting the Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) to disrupt pathological angiogenesis. This study utilized a dual-methodology approach to evaluate a novel series of five coumarin nucleoside conjugates ( 5a - 5e ) as potential anti-cancer agents. Initially, the compounds’ drug-likeness was confirmed via ADMET prediction, which established favorable pharmacokinetic profiles. This was followed by an integrated MTT cytotoxicity screening against Oct1 (head and neck) and C33a (cervical) cancer cell lines, which identified compound 5d as the most potent cellular agent. The core of the investigation involved a comprehensive in silico analysis targeting the VEGFR-2 tyrosine kinase domain (TKD). Molecular docking revealed that all five compounds possess significantly superior predicted binding affinities compared to the native ligand, ATP (- 25.44 kJ/mol). Critically, the primary cellular lead 5d (- 29.46 kJ/mol) and the strongest binder 5e (- 31.30 kJ/mol) both surpassed the affinity of the clinical benchmark, Sorafenib (- 28.80 kJ/mol), confirming their high potential as competitive inhibitors. Further validation using Molecular Dynamics (MD) simulation and MMPBSA analysis demonstrated exceptional dynamic stability and thermodynamic preference for the TKD-ligand complexes, firmly supporting the predicted binding hypothesis. In conclusion, compounds 5d and 5e are validated lead candidates possessing favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, direct cellular cytotoxicity, and a robust computationally modeled dual-action profile. Future research is urgently mandated to perform VEGFR-2-specific functional assays to definitively validate the predicted anti-angiogenic mechanism and conduct in-vivo studies to assess therapeutic efficacy.

Issue #11: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sat, 03 Jan 2026 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #12: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sat, 03 Jan 2026 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Fri, 02 Jan 2026 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Thu, 01 Jan 2026 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #10: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Thu, 01 Jan 2026 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Wed, 31 Dec 2025 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #9: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Wed, 31 Dec 2025 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Tue, 30 Dec 2025 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #8: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Tue, 30 Dec 2025 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Mon, 29 Dec 2025 00:00:00 +0000

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Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #7: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Mon, 29 Dec 2025 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #7: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sun, 28 Dec 2025 00:00:00 +0000

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Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Weekly Digest: Dec 21 - Dec 28, 2025

Sun, 28 Dec 2025 00:00:00 +0000

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Dec 21 - Dec 28, 2025

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sat, 27 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #5: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Sat, 27 Dec 2025 00:00:00 +0000

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Daily curated signals from arXiv, PubMed, and BioRxiv.

Signal of the Day

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Fri, 26 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #1: Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

Thu, 25 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Integrated cytotoxicity screening and in silico analysis of coumarin nucleoside conjugates as computationally modeled VEGFR-2 inhibitors: oncocyte cytotoxicity, molecular docking, and dynamics simulation studies.

🧬 Abstract

Issue #1: Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Wed, 24 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

🧬 Abstract

A novel series of dihydropyridine-sulfonyl derivatives (AG-CHO and analogues A1-A7) were synthesized and structurally characterized. Molecular docking demonstrated favorable binding of these compounds to autophagy-associated and cancer-related targets, while molecular dynamics simulations confirmed A5 as the most stable ligand protein interactions. Functional assays in SKOV-3, MCF-7, A549, and EA.hy.926 cells using acridine orange staining and flow cytometry revealed significant autophagy induction. Among all tested compounds AG-CHO emerged as the most potent inducer of autophagy. Notably, derivatives such as A6 and A7 showed selective potency in endothelial cells, whereas A1, A5, and A7 were effective in A549 cells, indicating cell-specific activity. Collectively, this integrated computational and experimental study identifies A5 as the lead compound and highlights dihydropyridine-sulfonyl scaffolds as promising autophagy modulators and potential anticancer candidates for further preclinical development.

Issue #4: Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Wed, 24 Dec 2025 00:00:00 +0000

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Signal of the Day

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Issue #1: Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Tue, 23 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

🧬 Abstract

Issue #3: A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.

Tue, 23 Dec 2025 00:00:00 +0000

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Signal of the Day

A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.

The accurate prediction of protein-ligand binding poses and affinities is central to structure-based drug design. In this study, we first benchmarked three distinct pose generation strategies for data sets from the ASAP Antiviral Challenge 2025: molecular docking (Glide and AutoDock Vina), ligand-based superposition (FlexS), and deep learning-based modeling (AlphaFold3, Boltz-2, DiffDock and Gnina). We evaluated their performance on binding pose prediction for ligands targeting SARS-CoV-2 and MERS-CoV main protease (Mpro). For binding affinity estimation, we implemented a machine learning-based scoring approach called ligand-residue interaction profile scoring function (LRIP-SF), which integrates molecular mechanics generalized Born surface area (MM-GBSA) energy decomposition with machine learning algorithms. Our results showed that deep learning-based modeling with AlphaFold3 achieved the highest pose prediction accuracy with a success rate of 88.1% and an average ligand root-mean-square deviation (LRMSD) of 1.12 Å. Moreover, binding poses predicted by AlphaFold3 enabled the most accurate potency predictions by LRIP-SF, with the lowest mean absolute error (MAE) and root-mean-square error (RMSE) in pIC50 units across both targets: the MAE and RMSE are 0.606 and 0.813, respectively, for MERS-CoV Mpro and 0.724 and 0.894 respectively for SARS-CoV-2 Mpro. Although ligand-based superposition method (FlexS) was less accurate in pose prediction, it offered competitive potency prediction performance with significantly lower computational cost. To interpret model predictions by LRIP-SF and identify critical binding determinants, we performed global sensitivity analysis (GSA), revealing key residues that contributed most significantly to ligand binding. These findings highlight the importance of pose quality and interaction profiling in affinity prediction and demonstrate the great potential of deep learning-based methods for drug discovery, especially in the absence of cocrystal structures.

Issue #2: Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Mon, 22 Dec 2025 00:00:00 +0000

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🚀 Today’s Top Signal

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

🧬 Abstract

Issue #3: Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Mon, 22 Dec 2025 00:00:00 +0000

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Signal of the Day

Cytotoxicity, apoptosis, molecular docking, and molecular dynamics study of novel compounds of Sulfamide derivatives coupled with DHP scaffolds as potent inhibitors of the MCF-7, A549, SKOV-3, and EA. yh926 carcinoma cells.

Issue #2: Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond.

Sun, 21 Dec 2025 00:00:00 +0000

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Signal of the Day

Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond.

Molecule parametrization is an essential requirement to guarantee the accuracy of docking calculations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and protonation states. This includes large biological macromolecules, such as proteins and nucleic acids, and small molecules, such as ligands and cofactors. The structures of proteins and nucleic acids are challenging due to omission of several atoms from the structural model, and from the lack of connectivity and bond order information in the PDB and mmCIF file formats. For small molecules, the very large chemical diversity poses challenges for both validating correctness and providing accurate parameters. These challenges affect various modeling approaches like molecular docking and molecular dynamics. Moreover, several specialized methods (particularly in molecular docking) leverage specific chemical properties to add custom potentials, pseudoatoms, or manipulate atomic connectivity. To address these challenges, we developed Meeko, a molecular parametrization Python package that leverages the widely used RDKit cheminformatics library for a chemically accurate description of the molecular representation. Small molecules are modeled as single RDKit molecules, and biological macromolecules as multiple RDKit molecules, one for each residue. Meeko is highly customizable and designed to be easily scriptable for high-throughput processing, replacing MGLTools for receptor and ligand preparation.

Weekly Digest: Dec 14 - Dec 21, 2025

Sun, 21 Dec 2025 00:00:00 +0000

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Dec 14 - Dec 21, 2025