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

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

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.

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.

Mechanisms of Yiqi Huoxue Granule in Atherosclerosis Treatment: Insights from UPLC-Q-Exactive Orbitrap-MS Analysis, Network Pharmacology, Molecular Docking, and Experimental Verification.

Yiqi Huoxue Granule (YQHX), a traditional Chinese medicine (TCM) formulation, is extensively utilized for the treatment of atherosclerotic diseases. However, its active constituents and molecular mechanisms remain unclear. We utilized a systematic methodology to identify bioavailable compounds in vivo and predict and validate the principal targets and pathways responsible for their anti-atherosclerotic actions. Serum pharmacochemistry utilizing UPLC-Q-Exactive Orbitrap-MS was employed to identify the bioavailable compounds of YQHX. An integrated methodology combining network pharmacology and molecular docking was implemented to predict its potential targets and mechanisms against atherosclerosis, which were subsequently verified experimentally in apolipoprotein E-deficient (ApoE-/-) mice. We identified 36 absorbable compounds in the serum of rats following YQHX administration, and 252 potential therapeutic targets were predicted. Protein-protein interaction analysis identified 10 hub targets, which are IL-6, TNF, EGFR, TP53, AKT, STAT3, SRC, CTNNB1, TLR4, and MMP-9. Enrichment analyses indicated that these targets are primarily involved in lipid metabolism and inflammatory responses, with significant enrichment in the PI3K-Akt and SRC signaling pathways. Molecular docking revealed strong binding affinities between the proteins EGFR, SRC, and AKT and their respective compounds. In ApoE-/- mice, YQHX significantly attenuated atherosclerotic plaque progression, enhanced lipid profiles, and inhibited systemic and plaque inflammation (decreased IL-6, IL-1β, sVCAM-1, and macrophage infiltration). Western blotting analysis revealed that these benefits were associated with the inhibition of SRC and AKT phosphorylation within the plaques. This study systematically identified the bioactive compounds of YQHX and demonstrated its multi-target anti-atherosclerotic effect, which involved the enhancement of lipid metabolism and suppression of inflammation, mediated, at least in part, by the inhibition of the SRC/AKT signaling pathway.

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Pipeline Tip

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

Resources & Tools

• Dataset: PDBbind - Binding affinity data with 3D structures of protein-ligand complexes.
• Dataset: BioLiP - Verified biologically relevant ligand-protein interactions.
• Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server. View all tools →
• Tool: PyMOL - Gold standard for molecular visualization and publication-quality imaging. View all tools →
• Event: Structural Biology Events (Open)
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Job: Postdoc in Computational Biology & Machine Learning - Academic Positions at Academic Positions
• Job: Postdoctoral Research Fellow (Bioinformatics) - Academic Positions at Academic Positions

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