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Daily Signal January 24, 2026 · 9 min read

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

Protein Design Digest - 2026-01-24 - DynaBench: Dynamic data for the docking benchmark.

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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.

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

Also Worth Reading

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.

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Pipeline Tip

Pin reference genomes by checksum to avoid version drift.

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

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