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Daily Signal April 24, 2026 · 10 min read

Issue #94: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors of HER2 L755S, T798I, and T798M based on a Large Database of Curcumin Derivatives.

Protein Design Digest #94: Comprehensive Molecular Docking and Molecular Dynamics Reveal Inhibitors…

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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.

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

Also Worth Reading

Exploring quantum frontiers in protein structure prediction: techniques, challenges, and opportunities.

Protein folding is governed by the principle of free energy minimization, where a protein’s native tertiary structure corresponds to the global minimum on an energy landscape shaped by quantum mechanical interactions such as hydrogen bonding, van der Waals forces, and electron delocalization. Despite significant advances in template-based modeling (TBM), ab-initio simulations, and deep learning approaches, classical methods continue to face challenges due to the exponential complexity of the conformational search space and the approximations involved in modeling molecular interactions. Although AlphaFold, a deep learning-based protein modeling tool, has achieved a remarkable score of 92.4 in the critical assessment of protein structure prediction (CASP), classical protein structure prediction (PSP) remains hindered by the computational limitations of conventional binary architecture in representing the physical constraints of biomolecular systems. By representing the combinatorial explosion of possible conformations as a more tractable optimization problem, quantum computing offers a fundamentally new paradigm for protein three-dimensional (3D) structure prediction. In this review, we explore how quantum computing (QC) techniques including quantum annealing, quantum optimization algorithms, and hybrid quantum-classical approaches can leverage quantum properties such as superposition, entanglement, and tunneling to more efficiently navigate the complex energy landscapes associated with protein folding. While current challenges, including limited qubit fidelity, error correction, and scalability, remain, the integration of quantum algorithms with classical strategies holds significant promise for advancing structural biology, with profound implications for drug discovery and the understanding of complex biomolecular systems.

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.

Identification of paucinervin D as a natural sphingosine-1-phosphate receptor 1 agonist: Insights from pharmacophore modeling, docking, molecular dynamics simulations, and density functional theory.

Sphingosine-1-phosphate receptor 1 (S1PR1), a member of the G protein-coupled receptor (GPCR) family, is a crucial therapeutic target for various diseases. Activation of S1PR1 has been recognized as an effective therapeutic strategy for multiple sclerosis (MS), inflammatory bowel disease (IBD), and psoriasis. Natural products (NPs) serve as a rich source of bioactive compounds for drug discovery. Here, we aimed to discover novel S1PR1 agonists from NPs via multi-level virtual screening (VS). Using a validated HipHop pharmacophore model, we screened a database containing 54,642 NPs, followed by molecular docking. Based on binding mode analysis, four candidate S1PR1 agonists (NPC323626, NPC264112, NPC469907, and NPC22192) were selected. Subsequent molecular dynamics (MD) simulations and binding free energy calculations confirmed the stability of the receptor-ligand complexes and their binding affinities. Among the four candidates, NPC469907 exhibited the strongest binding affinity for S1PR1, with a value of -58.08 ± 0.13 kJ/mol. Furthermore, hydrogen bonds formed between NPC469907 and Glu121 of S1PR1 were found to be essential for receptor activation. Quantum mechanical calculations further revealed that the phenyl-ring-attached hydrogen site in NPC469907 could be modified without compromising its ability to activate S1PR1. The analysis of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) indicated that NPC469907 possessed favorable pharmacokinetic properties and low toxicity. In conclusion, our study identified NPC469907 as a promising natural S1PR1 agonist and established an effective VS strategy for the discovery of novel S1PR1 agonists.

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