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Daily Signal March 30, 2026 · 9 min read

Issue #78: FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories.

Protein Design Digest - 2026-03-30 - Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

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Signal of the Day

FastMDAnalysis: Software for Automated Analysis of Molecular Dynamics Trajectories.

The analysis of molecular dynamics (MD) trajectories remains fragmented, requiring researchers to integrate multiple computational methods in bespoke scripts. This creates a significant barrier to reproducibility and limits analytical scope. We present FastMDAnalysis, a unified framework that establishes a reproducible, automated workflow for end-to-end trajectory analysis. The system orchestrates a comprehensive and extensible suite of core analysis modules, including root-mean-square deviation and fluctuation, radius of gyration, hydrogen bonding, solvent-accessible surface area, secondary structure assignment, dimensionality reduction, clustering, fraction of native contacts for protein folding studies, and dihedral angle analysis, within a single, consistent environment built on MDTraj, scikit-learn, and SciPy. The software natively supports all major trajectory formats, including GROMACS, AMBER, and CHARMM. We demonstrate a > 90 % $$ >90% $$ reduction in code volume for standard workflows and validate its numerical equivalence to reference implementations. FastMDAnalysis provides a methodological advance that makes rigorous, multi-analysis MD studies accessible and reproducible for the computational chemistry, biology, and biophysics communities. The software is freely available under the MIT license at https://github.com/aai-research-lab/fastmdanalysis.

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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.

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.

Integrated DFT, molecular docking, and molecular dynamics investigation of some novel 2-thiohydantoin analogues as potent CDK2 inhibitors for anticancer therapy.

Cancer progression is driven by dysregulation of cyclin-dependent kinase 2 (CDK2), a critical cell cycle regulator. This study employed an integrated computational approach combining Density Functional Theory (DFT), molecular docking, molecular dynamics (MD) simulations, and MM-PBSA calculations to evaluate 2-thiohydantoin derivatives as CDK2 inhibitors. DFT calculations revealed compounds 2b-e narrowest lowest unoccupied molecular orbital (LUMO)- highest occupied molecular orbital (HOMO) gaps (3.02-3.26 eV in DMSO) and highest electrophilicity indices (> 3.20 eV), indicating enhanced reactivity toward biological targets. QTAIM and Fukui function analyses identified key electrophilic centers (C2, O12, C14) and hydrogen bonding sites essential for protein interactions. Molecular docking against CDK2 (PDB: 1HCK) showed compounds 2c, 2d, and 2b exhibited superior binding affinities (-9.312, -9.303, and - 9.269 kcal/mol) compared to ATP (-8.460 kcal/mol), forming critical hydrogen bonds with Lys33 and Thr14. The 10 ns MD simulations confirmed stable binding, with compound 2f maintaining highest conformational stability (RMSD ~ 0.05 nm) and robust hydrogen bonding (mean: 2.70 bonds). MM-PBSA analysis revealed compound 2d achieved optimal binding affinity (ΔG_bind = -34.50 ± 0.42 kcal/mol) through balanced van der Waals interactions (-50.74 kcal/mol) and minimal desolvation penalty (52.40 kcal/mol). Compounds 2b, 2c, 2d, and 2f emerged as lead candidates for experimental validation as next-generation CDK2-targeted anticancer agents.

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

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