Issue #75: Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.

Signal of the Day

Integrative structural and physicochemical characterization of chalcone synthase enzymes from medicinal plants using AlphaFold, molecular docking, and molecular dynamics.

Chalcone synthase (CHS) is the entry-point enzyme of the flavonoid biosynthetic pathway, catalyzing the first committed step toward the production of diverse bioactive metabolites with antioxidant, anti-inflammatory, and anticancer properties. Here, we conducted a comparative in silico characterization of CHS from 13 medicinal plants, with Arabidopsis thaliana included as reference species. Protein sequences retrieved from UniProtKB were aligned using ClustalW, revealing strong conservation of key motifs, particularly the catalytic triad (Cys-His-Asn), GFGPG motif, and catalytic loop. Physicochemical profiling indicated interspecies variability in predicted protein stability, hydrophobicity, and thermostability, reflecting structural adaptation rather than direct functional divergence. AlphaFold-predicted structures consistently adopted the conserved thiolase-like αβαβα-fold characteristic of type III polyketide synthases, while exhibiting species-specific variations in the substrate-binding channel architecture. These variations are interpreted as structural features that may influence substrate accommodation and selectivity. To assess functional relevance, molecular docking with p-coumaroyl-CoA further confirmed stable substrate placement within the conserved catalytic pocket across species. Furthermore, 100-ns molecular dynamics simulations of representative crystal-derived and AlphaFold-predicted CHS-ligand complexes confirmed conformational stability, which was supported by MM-PBSA calculations revealing favorable binding energetics dominated by van der Waals interactions. Collectively, this study integrates sequence, structural, and dynamic analyses to establish a computational framework for comparative CHS characterization in medicinal plants. While the findings are derived exclusively from in silico approaches, they provide structurally grounded hypotheses that may guide future experimental validation, enzyme engineering, and pathway-oriented exploration of flavonoid biosynthesis.

Why this matters: Provides actionable mutations to enhance catalytic efficiency or thermostability.

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.

Geometric deep learning assists protein engineering. Opportunities and Challenges.

Protein engineering is experiencing a paradigmatic transformation through the integration of geometric deep learning (GDL) into computational design workflows. While traditional approaches such as rational design and directed evolution have achieved significant progress, they remain constrained by the vastness of sequence space and the cost of experimental validation. GDL overcomes these limitations by operating on non-Euclidean domains and by capturing the spatial, topological, and physicochemical features that govern protein function. This perspective provides a comprehensive and critical overview of GDL applications in stability prediction, functional annotation, molecular interaction modeling, and de novo protein design. It consolidates methodological principles, architectural diversity, and performance trends across representative studies, emphasizing how GDL enhances interpretability and generalization in protein science. Aimed at both computational method developers and experimental protein engineers, the review bridges algorithmic concepts with practical design considerations, offering guidance on data representation, model selection, and evaluation strategies. By integrating explainable artificial intelligence and structure-based validation within a unified conceptual framework, this work highlights how GDL can serve as a foundation for transparent, interpretable, and autonomous protein design. As GDL converges with generative modeling, molecular simulation, and high-throughput experimentation, it is poised to become a cornerstone technology for next-generation protein engineering and synthetic biology.

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Pipeline Tip

Pin reference genomes by checksum to avoid version drift.

Resources & Tools

• Dataset: MGnify - Metagenomics resource for microbiome sequence data.
• Dataset: PDBbind - Binding affinity data with 3D structures of protein-ligand complexes.
• Tool: FunFOLD5 - Automated system for protein ligand-binding site prediction and function annotation. View all tools →
• Tool: MultiFOLD/IntFOLD - High-performance protein structure prediction and quality assessment server. View all tools →
• Event: Protein Design Hub (LinkedIn Group) (Ongoing)
• Event: Structural Biology Events (Open)
• Job: Hiring Now: 636 Computational Biology Jobs in Boston, MA – Great Pay & Flexible Hours (March 23, 2026) - Indeed at Indeed Jobs
• Job: Director of Bioinformatics Jobs, Employment - Indeed at Indeed Jobs

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