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Daily Signal March 27, 2026 · 9 min read

Issue #77: Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

Protein Design Digest - 2026-03-27 - Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

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Computational Identification of Novel Inhibitors Targeting Multiple Proteins of Tomato Brown Rugose Fruit Virus (ToBRFV) Through AlphaFold-Based Protein Modeling, Molecular Docking, MM/GBSA Binding Free Energy Analysis, and Molecular Dynamics Simulation

Abstract Tomato brown rugose fruit virus (ToBRFV), a tobamovirus, poses a significant threat to global tomato production due to its high infectivity, seed-borne transmission, and severe fruit symptoms. In this study, an integrative computational approach was employed to identify plant-derived phytochemicals capable of inhibiting essential viral proteins such as movement protein (MP), coat protein (CP), helicase domain, and RNA-dependent RNA polymerase (RdRP) domain. The three-dimensional structures of these viral targets were predicted using AlphaFold and subsequently validated using Ramachandran plots. A library of 2,847 phytochemicals was subjected to molecular docking, followed by MM-GBSA binding free energy calculations to evaluate binding affinity and interaction strength. Top-ranked compounds were further validated through 100-ns molecular dynamics (MD) simulations to assess complex stability and conformational behavior. Panasenoside, Kaempferol 3-sophorotrioside, Violanin, and Albireodelphin A exhibited the strongest binding affinities toward MP, CP, Helicase, and RdRP, respectively. RMSD and RMSF analyses confirmed the stability of these complexes, highlighting persistent hydrogen-bonding interactions within the active sites. The findings underscore the potential of flavonoids as effective antiviral agents against ToBRFV and provide a foundation for future in vitro and in vivo validation studies to develop flavonoid-based antiviral formulations for sustainable tomato crop protection.

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

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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.

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.

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Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

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

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