Recep Adiyaman
bioinformatics

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

January 24, 2026 Daily Intelligence
Protein Design Daily

Building something in Protein Design?

I love collaborating on new architectural challenges. Let's build together.

LinkedIn Connect Direct Message

🧬 Protein Design Digest

Curated protein signals by Recep Adiyaman

Join 1,000+ researchers. Unsubscribe anytime.

🚀 Today’s Top Signal

DynaBench: Dynamic data for the docking benchmark.

🧬 Abstract

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.

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

⭐ Additional Signals

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.

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.

Exploring the toxicity mechanisms of acetyl tributyl citrate in premature ovarian insufficiency via network toxicology and molecular docking.

Premature ovarian insufficiency (POI) is a complex disorder with diverse etiologies that profoundly impacts female fertility and overall health. Acetyl tributyl citrate (ATBC), a commonly used plasticizer in consumer products, has recently drawn attention for its potential role in disrupting ovarian function. ATBC-associated targets were predicted using STITCH and Swiss Target Prediction tools. Genes implicated in POI were retrieved from the GeneCards and OMIM databases. Overlapping targets were identified and used to construct a protein-protein interaction (PPI) network through the STRING platform, with core targets visualized and analyzed using Cytoscape. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted to determine relevant biological processes and signaling pathways. Molecular docking was performed to evaluate the binding interactions between ATBC and the core target proteins. A total of 84 overlapping targets were identified as potential mediators of ATBC-induced POI. PPI analysis highlighted five central hub proteins: STAT3, EGFR, PIK3CA, MMP9, and PRKCA. Enrichment analyses suggested involvement in oxidative stress, lysosomal activity, and serine/threonine kinase signaling. Key pathways included PI3K-AKT, MAPK, apoptosis, GnRH, and HIF-1 signaling cascades. Molecular docking results demonstrated favorable binding affinities between ATBC and the hub proteins. This integrative study sheds light on the molecular mechanisms by which ATBC may contribute to POI. By identifying critical targets and pathways, our findings provide a foundation for further toxicological research and underscore the utility of combining computational prediction, network analysis to assess the reproductive risks of environmental contaminants.

🧪 AI & Research News

🏢 Industry Insight & Applications

⚡ Quick Reads

Unraveling the mechanisms of nicotine-induced osteoporosis via network toxicology, bioinformatics, and molecular docking.

Introduction Osteoporosis (OP) is linked to smoking. Nicotine may disrupt bone homeostasis through various pathways, but its molecular mechanisms are unclear. This study aims to explore the molecular networks and key regulatory factors underlying nicotine-induced OP. Methods Nicotine toxicity was assessed via ProTox-3.0, with its Simplified Molecular Input Line Entry System (SMILES) structure retrieved from PubChem. Potential targets were predicted using five databases, including SuperPred. OP-related gene data (GSE56815) were extracted from Gene Expression Omnibus (GEO) and combined with GeneCards and Comparative Toxicogenomics Database (CTD) for target screening. Overlapping genes were identified by Venn diagram analysis, followed by protein-protein interaction (PPI) network construction. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using HipLot, while Hallmark Gene Sets provided insights into key biological pathways. Core targets were screened via Cytoscape 3.9.1, and molecular docking was conducted using AutoDockTools 1.5.7. Results In all, 388 nicotine-associated targets and 1777 OP genes were predicted, with 116 overlapping. Enrichment analyses revealed associations with multiple signaling pathways, particularly those involving apoptosis and estrogen. Eight core targets, including SRC, BCL2, and CASP3, were identified. Molecular docking showed strong binding affinity (approximately -5 kcal/mol), with enhanced binding stability through hydrophobic interactions and hydrogen bonding. Conclusions This study suggests nicotine exacerbates OP by regulating key targets, such as CASP3 and ESR1, and pathways like apoptosis and estrogen signaling. These findings provide insights into the molecular mechanisms underlying nicotine’s role in OP and potential therapeutic targets.

In Silico Investigation Reveals <i>IL-6</i> as a Key Target of Asiatic Acid in Osteoporosis: Insights from Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Background/objectives Osteoporosis is a multifactorial skeletal disorder in which chronic inflammation, dysregulated cytokine signaling, and metabolic imbalance contribute to excessive bone resorption and impaired bone formation. Asiatic acid has demonstrated bone-protective effects, but its molecular mechanisms in osteoporosis remain incompletely understood. This study aimed to investigate the anti-osteoporotic mechanisms of asiatic acid using an integrative in silico strategy. Methods Network pharmacology analysis was performed to identify osteoporosis-related molecular targets of asiatic acid. Molecular docking was used to predict the binding modes and affinities between asiatic acid and its target proteins. Molecular dynamics simulation was used to assess the structural stability and interaction persistence of the asiatic acid-protein complex. Results Network pharmacology identified 135 overlapping targets between asiatic acid and osteoporosis, with IL-6 , STAT3 , PPARG , and NFKB1 emerging as key hubs. KEGG analysis indicated the PPAR signaling pathway as a potential mechanism underlying the anti-osteoporotic effect. Molecular docking showed strong binding energies of asiatic acid with all predicted target proteins, with the highest affinity observed for IL-6 , involving key residues ASN61, LEU62, GLU172, LYS66, and ARG168. Consistently, molecular dynamics simulation confirmed stable binding of asiatic acid to IL-6 , with persistent interactions with ASN61, LYS66, LEU62, LEU64, and GLN154 mediated by hydrogen bonds, water bridges, and hydrophobic interactions. Conclusions This integrative in silico study provides mechanistic insight into the potential anti-osteoporotic actions of asiatic acid, implicating IL-6 as a plausible upstream molecular target. These results establish a robust mechanistic framework for future translational studies exploring asiatic acid as a natural therapeutic candidate for osteoporosis.

Exploring the Mechanism of Qigesan in Treating Esophageal Carcinoma Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Qigesan (QGS) is a traditional Chinese herbal medicine used for the treatment of esophageal carcinoma (EC) and possesses anti-cancer properties. However, the mechanism of QGS in the treatment of EC remains unclear. This study aimed to investigate the molecular basis of QGS in the treatment of EC and establish a scientific foundation for its application. This study employed a multifaceted approach-including network pharmacology, molecular docking, and molecular dynamics simulations-to investigate the therapeutic mechanisms of QGS in EC. By leveraging a comprehensive array of databases such as TCMSP, HERB, TTD, OMIM, GeneCards, and DrugBank, we systematically identified potential bioactive components and their corresponding targets related to QGS, as well as targets associated with EC. 271 overlapping targets of QGS and EC were obtained. Network pharmacology analysis identified eight hub targets (TP53, AKT1, IL6, STAT3, TNF, IL1B, EGFR, and CTNNB1) mediating the effects of QGS through dysregulated pathways, including PI3KAkt signaling, apoptosis regulation, AGE-RAGE, and IL-17 signaling. Molecular docking revealed that three QGS-derived compounds-peimisine, salvianolic acid J, and songbeinoneexhibited high binding affinities for multiple hub targets. These compounds concomitantly inhibit the MAPK/NF-ÎşB pathways while activating cell cycle regulation, DNA repair, and apoptosis, suggesting a multi-target therapeutic mechanism against esophageal carcinoma. QGS, a TCM formulation, has been extensively applied in the clinical treatment of EC for a long time and has been demonstrated to relieve esophageal obstruction. Nevertheless, the exact active components within QGS and their underlying molecular mechanisms remain elusive. In this study, network pharmacology, molecular docking, and MD simulation were employed to investigate the potential molecular mechanisms by which QGS exerts its therapeutic effects in the treatment of EC. These findings provide a comprehensive elucidation of the multi-component, multi-target therapeutic strategy employed by QGS in the treatment of EC, laying a solid theoretical foundation for subsequent pharmacological development and clinical validation.

Integrative Approaches to Uncover the Therapeutic Action of Huaiqihuang in Myocarditis: Network Pharmacology, Molecular Docking, and Molecular Dynamics.

Introduction Myocarditis (MC) is an inflammatory cardiomyopathy with high morbidity and mortality. Current treatment options for MC have limitations and side effects, necessitating the exploration of new therapies. Traditional Chinese Medicine (TCM), particularly Huaiqihuang Granules (HQH), has shown promise due to its anti-inflammatory, antioxidative, and anti-apoptotic properties. However, the application in cardiovascular diseases remains underexplored. Methods We employed network pharmacology, molecular docking, and Molecular Dynamics (MD) simulations to evaluate HQH’s effects on MC. This involved identifying bioactive components and therapeutic targets, conducting enrichment analyses, and performing molecular docking and MD simulations to validate the interactions between HQH components and MC-related targets. Results A total of 57 bioactive components in HQH and 143 potential therapeutic targets for MC were identified. Enrichment analyses revealed that HQH’s potential treatment effects on MC involve various processes and pathways, including response to lipopolysaccharide, peptidase activity, the extracellular region, and pathways in cancer. Molecular docking indicates that Physalin A, sibiricoside A_qt, zhonghualiaoine 1, and methylprotodioscin_qt, along with ALB, PTGS2, AKT1, ESR1, and MMP9, may serve as key therapeutic components and targets. MD simulations confirmed strong interactions between HQH’s core components and MC-related targets, supporting their potential therapeutic effects. Discussion This study suggests that HQH exerts therapeutic effects against MC through multi-target mechanisms and stable targets. These findings provide valuable insights into alternative treatment strategies for MC, offering a foundation for further research and clinical exploration. Conclusion This study confirms that HQH can influence MC through various active components and multiple therapeutic targets.

Exploring the mechanism of Kemofang in treating idiopathic membranous nephropathy based on LC-MS/MS combined with network pharmacology, molecular docking, and molecular dynamics simulation.

Idiopathic membranous nephropathy (IMN), an autoimmune glomerular disease, arises from in situ immune complex deposition in the glomerular subepithelial spaces, triggering complement activation and podocyte injury. Although the Kemo Formula shows therapeutic potential for IMN, its mechanisms remain unclear. This study employed LC-MS/MS, network pharmacology, molecular docking, and dynamic simulations to elucidate the mechanism of action. LC-MS/MS and the TCMSP database identified 83 bioactive components from 267 chemicals detected in the Kemo Formula. Using PubChem, Swiss Target Prediction, and GeneCards, 827 drug targets and 2581 IMN-related targets were screened, yielding 336 overlapping targets linked to 81 components. Network analysis prioritized 15 key components ( baicalein and quercetin) and 36 core targets (TP53, IL6, and AKT1). Functional enrichment revealed involvement in hormone response, MAPK cascade regulation, and kinase binding with pathways including lipid metabolism, PI3K/Akt, and MAPK signaling. Molecular docking indicated strong binding affinities between the active components and targets, while dynamic simulations predicted the stability of the galangin-AKT1 complex. The Kemo Formula likely mitigates IMN by multi-target modulation, ameliorating lipid dysregulation, suppressing podocyte apoptosis, and attenuating immune-inflammatory and oxidative stress via PI3K/Akt and MAPK pathways. This integrative approach highlights its multicomponent, multitarget therapeutic strategy against IMN, providing a foundation for further mechanistic and clinical exploration.

Discovery of ActRIIB antagonistic peptides from in vitro-digested chicken breast meat via an integrated Peptidomics and molecular docking approach.

Sarcopenia and obesity are major global health challenges. This study investigated peptides from chicken breast meat via in vitro digestion as potent ActRIIB antagonists to promote myogenesis. The intestinal-phase digest collected at 120 min showed the highest degree of hydrolysis (65.99 % Âą 4.00 %) and enhanced C2C12 proliferation (128.15 % Âą 9.90 %). Peptidomics identified peptides mainly from myofibrillar proteins and metabolic enzymes. Molecular docking revealed key hydrogen-bonding residues, including Glu 95 , Pro 117 , Glu 94 , Thr 93 , Asn 96 (Chain A), Ser 97 (Chain L), and Ser 59 (Chain H). Surface plasmon resonance showed that KEKLHVYKHIEK, EIKKEEKKEER, and DLENDKQQLDEK exhibited strong ActRIIB-binding affinity (K D : 0.514, 0.813, 1.91 ÎźM). These peptides enhanced cell proliferation, inhibited myostatin signaling by reducing Smad2/3 phosphorylation, and upregulated MyoD expression. Molecular dynamics simulations (100 ns) indicated that DLENDKQQLDEK-ActRIIB and EIKKEEKKEER-ActRIIB complexes maintained a stable average number of 6 and 10 hydrogen bonds, respectively. Chicken breast-derived peptides thus represent promising functional food ingredients for combating muscle-wasting disorders.

Enhancing Genomic Selection for Soybean Drought Tolerance via Integration of Epistasis and AlphaFold2 Prediction.

Soybean is a globally important economic and food crop, whose production is often constrained by drought stress, posing a serious threat to yield and quality. Genomic selection (GS) has become a core technology in modern breeding, effectively enhancing breeding efficiency. However, conventional prediction models mainly rely on additive genetic effects and fail to adequately incorporate non-additive factors such as epistasis, limiting further improvements in prediction accuracy. In this study, a genome-wide epistatic analysis of soybean drought tolerance identified 3594 protective interaction pairs. Incorporating significant epistatic SNP pairs into six genomic prediction models resulted in comparable and substantial improvements in prediction accuracy across all models (by 24%). Furthermore, integration of AlphaFold2-based protein structure prediction and transcriptional regulatory analyses validated the biological reliability of protective epistatic pairs, effectively reducing the risk of false positives. Network construction and functional enrichment analyses further revealed that these epistatic pairs participate in coordinated protein structural interactions and are enriched in key biological pathways. Haplotype analysis confirmed the critical regulatory role of non-additive effects in soybean drought tolerance. Collectively, this study establishes a comprehensive evidence chain from molecular mechanisms to breeding applications, demonstrating that integrating epistasis into GS can effectively enhance prediction performance for drought tolerance in soybean. These findings provide novel research strategies for the genetic analysis of complex traits and efficient breeding.

Incorporation of network pharmacology, molecular docking, survival, density functional theory, and experimental studies to explore the potential key targets of formononetin by TERT-mediated anti-cancer effects in MCF-7 breast cancer.

Breast cancer remains a significant global health burden, with a rising incidence and mortality rate, particularly among younger women. Despite substantial therapeutic progress, effective molecular targets for treatment remain limited. This study investigated the oncogenic function of telomerase reverse transcriptase (TERT) and assessed the anti-cancer potential of formononetin using integrated bioinformatics and computational analyses. Pharmacokinetic and toxicity profiles were assessed using SwissADME, pkCSM, and Protox-II. Potential drug and disease targets were retrieved from SwissTarget, TargetNet, GeneCards, and DisGeNET databases, identifying 45 overlapping targets. Protein-protein interaction mapping via STRING and topological analysis in Cytoscape highlighted TERT, PIK3CA, ESR1, and KIT as key nodes. Molecular docking revealed high binding affinities of formononetin toward TERT (- 8.15 kcal/mol) and PIK3CA (- 8.01 kcal/mol). Gene expression profiling using GEPIA2 confirmed significant over expression of TERT and PIK3CA in breast carcinoma tissues. Pathway enrichment analysis, conducted through ShinyGO, in conjunction with density functional theory (DFT) calculations, elucidated the electronic and interaction dynamics underlying ligand-target stability. Collectively, these findings suggest that formononetin may be a promising lead compound for targeting TERT-driven breast cancer, warranting further in vivo and clinical validation to establish its therapeutic potential.

💡 Pipeline Tip

Pin reference genomes by checksum to avoid version drift.

🛠️ Resources

The protein structure is the language of life; design is its poetry. — Recep Adiyaman

BS HF DK