Recep Adiyaman
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Weekly Digest: Dec 21 - Dec 28, 2025

December 28, 2025 Daily Intelligence
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🧬 Protein Design Digest

Curated protein signals by Recep Adiyaman

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🧬 Weekly Recap

Dec 21 - Dec 28, 2025

Missed a day? Here are the top research signals and tools from the past week, summarized for your Sunday reading.

🏆 Top Signals of the Week

🗓️ Sunday, Dec 21

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

🧬 Abstract

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.

Why it matters: Expands the searchable sequence space for novel folds and high-affinity binders.

🗓️ Monday, Dec 22

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.

Why it matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

🗓️ Tuesday, Dec 23

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

🧬 Abstract

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.

Why it matters: Critical for improving fold accuracy and reducing structural uncertainty in de novo design.

🗓️ Wednesday, Dec 24

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.

Why it matters: Enhances small-molecule or peptide docking accuracy for targeted drug discovery.

🗓️ Saturday, Dec 27

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.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

🗓️ Sunday, Dec 28

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.

Why it matters: Essential ground-truth data for validating next-gen foundation models like Boltz or Chai.

⚡ Selected Quick Reads

  • From sweetener to risk factor: Network toxicology, molecular docking and molecular dynamics reveal the mechanism of aspartame in promoting coronary heart disease.: Aspartame, a widely used non-nutritive sweetener, has been epidemiologically linked to coronary heart disease (CHD), although the underlying mechanisms remain unclear. This study employed an integrative computational strategy combining network toxicology, molecular docking, and molecular dynamics to decode aspartame’s CHD-promoting mechanisms. Initially, the toxicity profile of aspartame was predicted using ProTox 3.0 and ADMETlab 3.0, which highlighted significant cardiotoxicity. Through multi-source target screening of aspartame (PharmMapper, SEA, etc.) and CHD (GeneCards, OMIM), 216 shared targets were identified. Protein-protein interaction network analysis revealed 10 hub targets (INS, PPARGC1A, TNF, AKT1, IL6, MMP9, IGF1, PTGS2, SIRT1, PPARG). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed significant enrichment in lipid metabolism, inflammatory responses, insulin resistance, and atherosclerosis-related pathways. Molecular docking and molecular dynamics simulations (MDS) demonstrated high-affinity binding of aspartame to three core targets (PTGS2, TNF, and PPARGC1A), with a binding energy ≤ -7.0 kcal/mol, and confirmed high binding stability. This study reveals that aspartame may promote the pathogenesis of CHD by disrupting cardiovascular homeostasis through multi-target interactions, including inflammatory response, metabolic dysregulation, and vascular remodeling. These findings provide molecular evidence for re-evaluating the safety profile of aspartame and establish a computational framework to guide experimental validation and preventive strategies.
  • Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence-function relationships across protein engineering campaigns.: The discovery and engineering of new enzymes is important across the bioeconomy, with diverse applications from foods to pharmaceuticals, sensors to agriculture. However, enzyme engineering, in particular machine learning-guided engineering, is hampered by a lack of data. Currently there exists no database designed to capture and interpret datasets created in this domain, nor are there easy analysis and visualisation tools. We developed the Enzyme Engineering Database to provide a centralized resource and an online analysis tool to consolidate sequence-function data from enzyme engineering campaigns, thereby making three contributions: (i) a database into which researchers can deposit public data, (ii) visualisation and analysis tools for protein engineers to analyse their own data or compare enzyme variants to other engineering campaigns, and (iii) a gold-standard dataset for benchmarking automated extraction along with the first large language model extraction pipeline specific for enzyme engineering campaigns. The Enzyme Engineering Database is accessible at http://enzengdb.org/.
  • A fully automated benchmarking suite to compare macromolecular complexes.: Protein structure prediction has a long history of benchmarking efforts such as critical assessment of structure prediction, continuous automated model evaluation and critical assessment of prediction of interactions. With the rise of artificial intelligence-based methods for prediction of macromolecular complexes, benchmarking with large datasets and robust, unsupervised scores to compare predictions against a reference has become essential. Also, the increasing size and complexity of experimentally determined reference structures by crystallography or cryogenic electron microscopy poses challenges for structure comparison methods. Here we review the current state of the art in scoring methodologies, identify existing limitations and present more suitable approaches for scoring of tertiary and quaternary structures, protein-protein interfaces and protein-ligand complexes. Our methods are designed to scale efficiently, enabling the assessment of large, complex systems. All developments are available in the structure benchmarking framework of OpenStructure. OpenStructure is open source software and available for free at https://openstructure.org/ .
  • Multi-target exploration of newly synthesized pyrazoline-quinoline derivatives via in vitro screening, QSAR, molecular docking, MD simulations, and DFT analysis.: The development of multifunctional therapeutic agents remains a promising strategy in modern drug discovery, particularly for diseases associated with oxidative stress, bacterial infections, and cancer progression. In this study, a new series of [5-(substituted phenyl)-3-(substituted phenyl)-4,5-dihydro-pyrazol-1-yl]-(2-methyl-quinolin-4-yl)-methanones (9a-o) has been synthesized and evaluated for anticancer, antibacterial, and antioxidant activities through established in vitro and in silico screening models. The in vitro cytotoxic evaluation conducted against the human lung cancer cell line (A549) using the MTT assay revealed that all synthesized compounds have significant inhibitory potential. Among them, compounds 9i, 9b, 9h, 9d, and 9o demonstrated superior potency, showing IC₅₀ values of 3.68 ± 0.45 μM, 4.06 ± 0.35 μM, 4.33 ± 0.68 μM, 6.32 ± 0.89 μM, and 7.82 ± 0.52 μM compared to the reference drug doxorubicin, which showed an IC₅₀ of 9.48 ± 0.35 μM under identical experimental conditions. The same compounds also possess the best antibacterial (MIC value 12.5-25 μg/mL) and antioxidant potential. The in silico studies encompassed ADMET analysis, QSAR, molecular docking, and molecular dynamics simulations, which were carried out using Molsoft LLC, pkCSM, ChemDes, AutoDock 4.2, and GROMACS software. Their electronic and reactivity features were also analyzed through DFT calculations based on HOMO, LUMO, electron affinity, ionization potential, chemical potential, and global softness. The results of computational studies reinforced the findings in all dimensions. In summary, this study introduces a promising class of pyrazoline-quinoline conjugates with significant multifunctional efficacy.
  • Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.: Background Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, insomnia, and increased suicide risk, posing a significant burden on global health. Zhenwu decoction (ZWD), a traditional Chinese medicine, has shown therapeutic potential in alleviating MDD symptoms. However, its complex composition has limited the understanding of its underlying pharmacological mechanisms. This study aimed to explore the antidepressant mechanisms of ZWD in the treatment of MDD. Methods Active compounds and potential targets of ZWD were identified through database screening and network pharmacology analysis. These targets were intersected with MDD-related genes to construct a protein-protein interaction network. Core targets were further refined using random forest algorithms. Molecular docking and molecular dynamics simulations were employed to evaluate the binding affinity and stability between key compounds and core targets. Experimental validation was conducted in a lipopolysaccharide (LPS)-induced mouse model of depression using behavioral testing, measurement of inflammatory cytokines, and gene expression analysis. Results Network pharmacology and machine learning identified TNF-α signaling as key pathways in the antidepressant effects of ZWD. Enrichment analysis highlighted the involvement of Lipid and atherosclerosis, the IL-17 signaling pathway. Core targets, including PPARG, F10, AR, TNF, PIK3CG, ADH1C, and GABRA6, were predicted to mediate its effects. Molecular docking and dynamics simulations confirmed strong binding of ZWD components, especially kaempferol, to TNF-α, inhibiting its expression. In vivo, ZWD improved anxiety/depressive-like behaviors in LPS-treated mice, evidenced by better performance in the behavioral tests. ZWD also reduced neuroinflammation, with decreased Tnf-α levels, and reduced IBA-1 and GFAP staining, indicating reduced microglial and astrocyte activation. These results suggest that ZWD alleviates depression through modulation of TNF-α-mediated inflammation. Conclusions These findings suggest that ZWD exerts antidepressant effects primarily by modulating TNF-α-mediated inflammatory pathways, providing a comprehensive molecular and experimental framework supporting its clinical potential in MDD treatment.
  • UPLC-Q-TOF/MS-based spectrum-effect correlation combined with chemometrics and molecular docking for quality assessment and screening of bioactive components with hemostatic, antinociceptive, and anti-inflammatory activities in Liparis nervosa.: Ethnopharmacological relevance Liparis nervosa (LN) occurs in Southwest China and is traditionally used as a hemostatic and detoxifying agent; however, the pharmacodynamic basis for its medicinal properties is unclear; this impedes the quality standardization and clinical application of this herb. Aim of the study This study aimed to establish an integrated quality assessment system for LN by combining comprehensive chemical profiling with pharmacological evaluation to identify bioactive components and quality markers. Materials and methods Chemical profiling of ten regional LN specimens via UPLC-Q-TOF/MS revealed 53 shared components and characteristic fingerprints. Concurrently, systematic evaluation of hemostatic, antinociceptive, and anti-inflammatory activities was used to identify bioactive fractions. Using spectrum-effect modeling, which integrates techniques such as gray relational analysis, partial least squares regression, and bivariate correlation linked chromatographic features to bioactivities, these pharmacological effects were correlated with specific chemical components. Molecular docking was performed to validate target interactions. Orthogonal design coupled with spectrum-effect relationship analysis was used to pinpoint potential quality markers. Results As the first comprehensive study to systematically identify bioactive fractions and quality markers of LN, this work developed a tripartite evaluation framework integrating chemical profiling, pharmacological verification, and molecular docking-based target validation. Conclusions This methodology advances the standardization of LN, supports the interpretation of its pharmacological mechanisms of action, and facilitates the development of multi-target phytotherapeutic agents using LN bioactives.

🛠️ Tools & Datasets

  • 🛠 Tool: ReFOLD4 - Sophisticated protein structure refinement tool for improving model quality.
  • 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
  • 🛠 Tool: FunFOLD5 - Automated system for protein ligand-binding site prediction and function annotation.
  • 🛠 Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server.
  • 💾 Dataset: PDB-REDO - Optimized protein structure database with refined models.
  • 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
  • 🛠 Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server.
  • 🛠 Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging.
  • 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
  • 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
  • 🛠 Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging.
  • 🛠 Tool: Chai-1 - Multi-modal foundation model for molecular structure prediction.
  • 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
  • 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.
  • 🛠 Tool: ProteinSolver - Graph-based neural network for protein sequence design.
  • 🛠 Tool: RFdiffusion - State-of-the-art generative model for de novo protein design.
  • 💾 Dataset: Protein Data Bank (PDB) - The single global archive for macromolecular structure data.
  • 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
  • 🛠 Tool: RFdiffusion - State-of-the-art generative model for de novo protein design.
  • 🛠 Tool: ProteinMPNN - High-speed sequence design optimized for fixed-backbone folding.
  • 💾 Dataset: AlphaFold Structure Database - 200M+ predicted structures from DeepMind/EMBL-EBI.
  • 💾 Dataset: Uniprot Knowledgebase - The world’s most comprehensive resource for protein sequence and annotation.

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