Recep Adiyaman
bioinformatics

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

January 25, 2026 Daily Intelligence
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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.

🧬 Abstract

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.

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

⭐ Additional Signals

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.

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 ”M) which outperformed the potential of acarbose (6.20 ± 0.10 and 6.70 ± 0.20 ”M), 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.

Identification of novel umami peptides in fermented milk and elucidation of their umami mechanism via molecular docking and molecular dynamics simulations.

A streamlined workflow integrating multi-model machine learning, bioinformatics filtering, sensory evaluation, molecular docking and dynamics simulations was applied to mine umami peptides in fermented milk. Based on dual selection criteria-(i) unanimous umami prediction by UMPred-FRL, Umami_YYDS, Umami-MRNN, Mlp4Umami, Umami_TD, (ii) favorable in silico properties (non-toxicity, non-allergenicity, good solubility, stability, potential bioactivity)-ten out of the 1505 peptides identified by peptidomics were shortlisted as umami peptide candidates. Sensory evaluation confirmed that eight imparted an umami taste. Molecular docking revealed that umami peptides interact with TAS1R1/TAS1R3 primarily through hydrogen bonds formed between their hydrophilic residues (predominantly Lys, Tyr) and receptor hydrophilic residues (notably Lys/Arg in TAS1R1, Asn in TAS1R3). Residues Arg307/Met375/Lys379 of TAS1R1, and Arg327/443/Ala329/Val437/Met452 of TAS1R3 were key interaction sites. Molecular dynamics simulations showed that the three peptides with the highest umami taste-EVFTKK, SKKTVDME, VMGVSKVKE-formed stable and compact complexes with TAS1R1/TAS1R3. This work enhances understanding of the umami characteristics of fermented milk.

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⚡ Quick Reads

Screening of active constituents in camellia oil against atopic dermatitis via molecular docking and experimental validation: elucidation of the underlying molecular mechanism.

Objective Atopic dermatitis (AD) is a chronic inflammatory skin disease. The JAK/STAT and PDE4/cAMP pathways are pivotal in driving its inflammation. This study aimed to discover natural JAK1 and PDE4 inhibitors from camellia oil to alleviate AD. Methods Utilizing the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we employed a target-based molecular docking approach against key inflammatory targets (JAK1, PDE4B, PDE4D) of AD to screen the compounds in camellia oil. This virtual screening was followed by in vitro enzymatic assays to validate their inhibitory effects. Based on these findings, we employed a DNCB-induced AD mouse model to compare the therapeutic efficacy of (1% and 4%) ( +)-catechin hydrate and (4% and 6%) epicatechin against 1.5% ruxolitinib cream. Results Although molecular docking screening predicted seven compounds with potential high binding affinity for PDE4B and PDE4D, respectively, subsequent in vitro enzymatic inhibition assays demonstrated that all of these compounds exhibited low inhibitory rates against the enzymes. In comparison, (+)-catechin hydrate and epicatechin not only exhibited excellent binding affinity with JAK1 but also achieved high inhibition rates. Their IC 50 values for JAK1 inhibition were 1125.65 ± 0.56 nM and 3531.24 ± 0.17 nM, respectively. Animal studies have demonstrated that both (+)-catechin hydrate and epicatechin can significantly ameliorate symptoms of AD, including reducing the severity of skin lesions and itching behavior, while also suppressing the expression of inflammatory mediators such as TSLP, IL-4, and IL-13. Conclusion In camellia oil, (+)-catechin hydrate and epicatechin are the primary active constituents for the treatment of AD, suggesting that their anti-AD effects were possibly mediated through the suppression of the JAK1-driven inflammatory signaling pathway. This study not only provides a novel utilization strategy for camellia oil, but also offers new insights for the treatment of AD.

ViralBindPredict: Empowering Viral Protein-Ligand Binding Sites through Deep Learning and Protein Sequence-Derived Insights.

The development of a single therapeutic compound can exceed 1.8 billion USD and take more than a decade, underscoring the urgent need to accelerate drug discovery. Computational methods have become indispensable; however, traditional approaches, such as docking simulations, face limitations because they depend on protein and ligand structures that may be unavailable, incomplete, or of low accuracy. Even recent breakthroughs, such as AlphaFold, do not consistently provide models precise enough to identify ligand-binding sites or drug-target interactions. We present ViralBindPredict, a deep learning framework that predicts viral protein-ligand binding sites directly from sequence. We also introduce the first curated large-scale benchmark of viral protein-ligand interactions, comprising >10,000 viral chains and ≈13,000 interactions processed using a 4.5 Å heavy-atom contact threshold. ViralBindPredict combines Mordred ligand descriptors with contextual protein embeddings from ESM2 or ProtTrans, enabling structure-free learning of binding preferences. Leakage-controlled data splits were applied to prevent overlap across protein sequence clusters and ligand scaffolds (Cluster90%, NoRed90%→Cluster90%, Cluster40%, NoRed90%→Cluster40%). Across most regimes, multilayer perceptrons, especially with ESM-2 embeddings, outperformed LightGBM baselines, maintaining strong precision-recall for unseen ligands but showing larger drops for unseen proteins, indicating that the protein context dominates generalization. ViralBindPredict introduces the first leakage-controlled benchmark for viral protein-ligand interactions and demonstrates accurate ligand-binding residue prediction directly from protein sequence. Together, these advances establish ViralBindPredict as a robust and extensible workflow for sequence-based antiviral discovery, supporting rapid target prioritization, compound repurposing, and de novo drug design, even in the absence of structural data.

Based on 3D-QSAR modeling and molecular dynamics of novel peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 inhibitors design and screening.

Background This study investigates the role of peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) in tumorigenesis and evaluates the potential of novel PIN1 inhibitors for cancer therapeutics. The design integrates computational approaches, including three-dimensional quantitative structure-activity relationship modeling, molecular docking, and molecular dynamics simulations, to develop and assess new inhibitors targeting PIN1. A dataset of 26 derivatives was utilized to construct predictive models and design potent inhibitors. Methods First, a Comparative Molecular Similarity Indices Analysis model was constructed, incorporating steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields to develop a predictive model for PIN1 inhibitors. Molecular docking was then performed to predict binding affinity between the inhibitors and PIN1, followed by molecular dynamics simulations to assess the stability of the inhibitors. Energy decomposition analysis identified key residues involved in binding, providing insights for molecular optimization. Results The Comparative Molecular Similarity Indices Analysis model showed good predictive ability, with a cross-validated q2 of 0.630 and a non-cross validated r2 of 0.925. The top optimized compound showed a predicted pIC50 of 9.962, indicating strong inhibitory activity. Molecular docking confirmed strong binding affinity between the inhibitor and PIN1. Molecular dynamics simulations demonstrated the compound’s stability at the binding site, and energy decomposition analysis revealed key residues contributing to binding. Conclusion The integration of computational techniques highlights a rational approach to the design of potent PIN1 inhibitors, offering a promising foundation for further development in cancer therapeutics.

DDI-AttendNet: cross attention with structured graph learning for inter-drug connectivity analysis.

Introduction In the context of interdisciplinary computational science and its increasingly vital role in advancing applied computer-aided drug discovery, the accurate characterization of inter-drug connectivity is essential for identifying synergistic therapeutic effects, mitigating adverse reactions, and optimizing polypharmacy strategies. Traditional computational approaches-such as similarity-based screening, molecular docking simulations, or conventional graph convolutional networks-often struggle with a range of limitations, including incomplete relational structures, lack of scalability to complex molecular systems, restricted model interpretability, and an inability to capture the multi-level hierarchical nature of chemical interactions and pharmacological effects. These constraints hinder the full potential of data-driven strategies in complex biomedical environments. Methods To address these pressing challenges, we introduce DDI-AttendNet, a novel cross-attention architecture integrated with structured graph learning mechanisms. Our model explicitly encodes both molecular topologies and inter-drug relational dependencies by leveraging dual graph encoders, one dedicated to learning intra-drug atomic interactions and the other to capturing the broader inter-drug relational graph. The model’s centerpiece is a cross-attention module, which dynamically aligns and contextualizes functionally relevant substructures across interacting drug pairs, allowing for more nuanced predictions. Built upon the foundation described in our methodology section, DDI-AttendNet is evaluated on multiple large-scale DDI benchmark datasets. Results The results demonstrate that our model consistently and significantly outperforms state-of-the-art baselines, with observed improvements exceeding 5%-10% in AUC and precision-recall metrics. Attention weight visualization contributes to improved interpretability, allowing researchers to trace predictive outcomes back to chemically meaningful features. Discussion These advancements affirm DDI-AttendNet’s capability to model complex drug interaction structures and highlight its potential to accelerate safer and more efficient data-driven drug discovery pipelines.

Study of the multitarget mechanism of Zao Ren Gan Cao Da Mai decoction in the treatment of insomnia comorbid with depression based on network pharmacology and molecular docking technology.

Based on network pharmacology and molecular docking to explore the mechanism of “Zao Ren Gan Cao Da Mai Decoction” in the treatment of insomnia comorbid with depression. This study employs network pharmacology and molecular docking techniques to uncover the mechanisms by which ZRGCDMD treats depression associated with insomnia. Using network pharmacology, 244 active ingredients were identified from ZRGCDMD, with key components including baicalein, ÎČ-carotene, kaempferol, quercetin, naringenin, and diosgenin. Additionally, 88 targets associated with depression comorbid with insomnia were identified. Gene ontology and Kyoto encyclopedia of genes and genomes analyses revealed that ZRGCDMD operates through pathways including neuroactive ligand-receptor interactions, lipid metabolism, and the AGE-RAGE signaling pathway. Using molecular docking technology, the binding energy range between the active ingredient and the primary target was determined to be between -9.2 and -6.1 kcal/mol. Moreover, protein-protein interaction network and molecular docking studies indicate that important targets, such as IL1B, HIF1A, TP53, IL-6, AKT1, and TNF, may be crucial for ZRGCDMD’s effectiveness in treating depression comorbid with insomnia. This study explored the potential active ingredients, potential targets, and signaling pathways of ZRGCDMD in the treatment of depression comorbid with insomnia. This helps to elucidate the therapeutic efficacy and mechanism of action of ZRGCDMD, and provides new insights into its clinical application.

Rational discovery of testosterone-enhancing peptide (AGNYGLPT) from sea cucumber: targeting T-type calcium channels through docking, molecular dynamics simulations, and cellular validation.

Calcium ions (Ca 2+ ) are a crucial signaling factor in testosterone synthesis. This study employed computer-guided peptide screening and mechanism exploration to elucidate how sea cucumber peptide (SCP) promotes testosterone synthesis via Cacna1g (T-type calcium channel). Ca 2+ treatment alone significantly upregulated the expression of Cacna1g, protein kinase C (PKC), protein kinase A (PKA), and testosterone synthase genes (StAR, Hsd17b3, Cyp17a1); these effects were further enhanced by SCP co-treatment. Molecular docking combined with correlation analysis pinpointed the peptide’s isoelectric point as a critical determinant governing its binding affinity to Cacna1g. SCP was subsequently fractionated into three components via ion-exchange chromatography, among which the fraction 1 (F1) elevated intracellular Ca 2+ levels and enhanced testosterone synthesis. Moreover, molecular docking results for the F1 sequences also showed a positive linear relationship between binding affinity and isoelectric point. Peptide AGNYGLPT in F1 stabilizes Cacna1g (with the strongest binding ability) via hydrogen bonding, and participates in ion channel formation (Charge = -2, Radius = 2.8 Å). In vitro, AGNYGLPT increased intracellular Ca 2+ levels and enhanced testosterone synthesis, but this effect was abolished by inhibition of T-type calcium channels. This study provided mechanistic insights into peptide-channel interactions and offered new ideas for computer-assisted screening of active peptides.

An evaluation of Roluperidone as a promising repurposing candidate for Alzheimer’s Disease: A Computational Investigation.

Alzheimer’s disease (AD) is the most dominant and prevalent form of dementia. The therapeutic agents for AD are not sufficient. Drug repurposing (i.e., also called drug repositioning or therapeutic switching of drugs) could contribute to adding novel therapeutic agents in AD discovery pipeline. Blood-brain barrier (BBB) is a crucial factor, for brain’s diseases related drug discovery. Since, CNS active compounds have BBB crossing property, in this study this category of compounds was re-evaluated as repurposing potential candidate for AD by integrated machine learning algorithm, cheminformatics analysis, molecular Docking and simulation-based approach. We builded three machine learning model such as Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) for the prediction of AD potential repurposing candidates. The SVM classification model performed better than others. The SVM classification model achieved an Area Under the Curve of the Receiver Operating Characteristics (ROC-AUC) of 0.81, along with higher precision, recall, and F1 scores. The support vector machine (SVM) was implemented to classify 500 CNS active compounds as AD drug potential and non-AD drug potential. Using the SVM model, 60 compounds were predicted as AD repurposing potential from 500 CNS active compounds. Structural similarity analysis of 60 compounds with Donepezil as a reference drug was performed using 5 different types of fingerprints such as ‘substructure’, ’extended’, ‘circular’, ‘EState’, ‘MACCS’. 9 compounds from them obtained as structurally most similar to the reference drug. After the molecular docking performance of 9 compounds into the active site & peripheral anionic site of human acetylcholinesterase (hAChE), it was revealed that Roluperidone’ had binding affinity of -12 kcal/mol, and ‘Napitane’ had binding affinity of -11.9 kcal/mol whereas the reference drug Donepezil had a binding affinity of -11.8 Kcal/mol. Molecular dynamics simulation revealed that Roluperionde had better binding integrity to hAChE. This study laid out computational reinvestigation of 500 CNS active drugs for therapeutic switching to AD, and ‘Roluperidone’ is found as an AD repurposing potential candidate. However, in-vitro and in-vivo studies are further needed to fully elucidate the compound’s potential as AD repurposing drugs.

Targeting D-Ribose-Binding Proteins in Brucella melitensis: A Novel Frontier Against Antibiotic Resistance

Abstract Antibiotic resistance among pathogens common to human beings and animals, which include Brucella melitensis , has end up a significant worldwide health task. Traditional antibiotic treatments for brucellosis, along with lengthy-time period regimens of doxycycline and rifampicin, are going through increasing boundaries because of rising resistance, affected person adherence issues, and considerable side results. This observe investigates the capacity of targeting the periplasmic D-ribose-binding protein (DBP), a key component of the bacterial ATP-binding cassette (ABC) delivery system, as a unique healing technique. Protein structural modeling was carried out the use of superior computational tools together with AlphaFold, Swiss-Model, and Phyre2, followed by validation via Ramachandran plots and energy minimization techniques. Molecular docking analyses recognized D-Talopyranose as a promising ligand with a high binding affinity of -5.8 kcal/mol. Subsequent ADMET profiling found out favorable pharmacokinetic and toxicological results, assisting its potential as a drug candidate. Molecular dynamics simulations similarly evaluated the stability and dynamics of the protein-ligand interplay complex, confirming its suitability for therapeutic programs. Our outcomes reveal that targeting DBP could offer a unique mechanism to combat antibiotic-resistant lines of Brucella melitensis by using disrupting essential metabolic pathways. This study affords a promising street for revolutionary brucellosis treatments by way of addressing the challenges posed by means of antibiotic resistance and paves the manner for experimental validation and optimization of the identified ligands. Such focused strategies may also notably improve ailment control and reduce the worldwide burden of brucellosis, mainly in areas where traditional antibiotics are losing their efficacy.

💡 Pipeline Tip

Use local MSA generation (colabfold_search) to bypass speed bottlenecks.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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