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Daily Signal March 09, 2026 · 8 min read

Issue #63: MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.

Protein Design Digest - 2026-03-09 - scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking.

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MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.

Protein-protein interactions (PPIs) are central to cellular signaling and regulation, and their dysregulation underlies many diseases. Predicting the impact of mutations on PPI stability, quantified as ΔΔG, is essential for understanding disease mechanisms and guiding protein engineering. Here, we first present MutPPI, a graph-based deep-learning model that encodes full-residue structural features of protein-protein complexes and employs a shared GIN-GAT feature extractor for wild-type and mutant complexes. MutPPI outperforms 12 existing methods on an antibody-antigen single-point mutation dataset (S645). By integrating evolutionary information from protein language models, we further develop MutPPI-plus, achieving enhanced predictive performance. Second, we proposed a mutation-path-based data augmentation strategy, which enriches input modalities and improves generalization of both MutPPI and MutPPI-plus. After data augmentation, MutPPI-plus demonstrates state-of-the-art performance on S645 and three additional multi-point mutation datasets (SM_ZEMu, SM595, SM1124), substantially surpassing DDMut-PPI. Our analyses highlight the benefits of the multimodal framework and the physically informed data augmentation method. Together, these results provide a versatile computational tool for accurate ΔΔG prediction, advancing rational protein design.

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AlphaFold for Docking Screens.

AlphaFold is an AI system developed by Google DeepMind to generate three-dimensional structures of proteins without experimental data. The models created with AlphaFold are available on the AlphaFold Protein Structure Database (AlphaFoldDB) ( https://alphafold.ebi.ac.uk/ ). The AlphaFold database is searchable by sequence and protein identification. This chapter focuses on an AlphaFold model and its use for docking screens using Molegro Virtual Docker. We rely on Jupyter Notebooks to integrate docking simulations and build regression models based on the atomic coordinates of protein-pose complexes. Our study focuses on constructing a neural network regression model to predict the inhibition of cyclin-dependent kinase 19 (CDK19). This enzyme is a target for anticancer drugs and does not have experimental data for its atomic coordinates. We utilize the Molegro Data Modeller to construct a regression model based on docking results of inhibitors for which binding affinity data is available. All CDK19 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .

Exploring the Mechanism of Action of Chicoric Acid Against Influenza Virus Infection Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

This study theoretically explores the mechanism of action of Chicoric acid against influenza virus based on network pharmacology, molecular docking, and molecular dynamics simulation techniques, aiming to provide insights for the development of new veterinary drugs for influenza. Potential targets for influenza virus action were identified using the PharmMapper (i.e. Version 2017) server and disease databases including GeneCards and OMIM. The STRING online analysis platform and Cytoscape 3.9.1 software were employed to construct a protein-protein interaction (PPI) network of the target proteins, followed by topological analysis to screen for key targets. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets using the DAVID database. A “drug-target-pathway” network diagram was constructed using Cytoscape 3.9.1 software. Molecular docking was carried out with AutoDock 1.5.6 and PyMOL 2.5 software to identify dominant binding targets, followed by molecular dynamics simulation analysis. The results of network analysis showed that there were 31 potential targets of Chicoric acid; the protein interaction network suggested that UBC, UBA52, RPS27A, HCK, and CDKN1B may be the core targets of Chicoric acid; 55 cell biological processes were obtained by GO enrichment analysis, and 15 related signaling pathways were obtained by KEGG pathway enrichment analysis; molecular docking showed that UBC and UBA52 had a good affinity to Chicoric acid and may be the dominant target of Chicoric acid exerting its effect. Chicoric acid may play a role in antiviral activity by acting on the dominant protein of UBC and UBA52, thus achieving an anti-influenza virus effect.

Unraveling the mechanism of curcumin in coronary slow flow phenomenon through network pharmacology and molecular docking.

The coronary slow flow phenomenon (CSFP) is associated with an increased risk of adverse cardiovascular events, yet standardized treatment is lacking. Curcumin, a natural compound, has shown potential in alleviating angina and improving metabolic risk factors in CSFP, but its underlying molecular mechanisms remain unclear. This study employed an integrated computational strategy. Network pharmacology was used to identify potential targets of curcumin and CSFP from public databases, and common targets were identified. Functional enrichment analysis was performed on the common targets, and a protein-protein interaction network was constructed. Core targets were identified using MCODE and CytoHubba plugins in Cytoscape. Molecular docking evaluated the binding modes and affinities of curcumin with the core targets, while molecular dynamics simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations validated the stability and binding free energies of the complexes. A total of 120 predicted targets of curcumin and 435 CSFP-related targets were identified, yielding 19 common targets. Functional enrichment analysis revealed that curcumin may treat CSFP by modulating inflammatory response, vascular function, cell migration, proliferation, apoptosis, and oxidative stress. These targets were associated with key signaling pathways, including NF-κB, TNF, and HIF-1. Network analysis and topological algorithms identified five core targets: EGFR, ICAM1, NFKB1, PTGS2, and STAT3. Molecular docking results demonstrated that curcumin exhibited excellent binding affinity with all core targets. Molecular dynamics simulations confirmed that the curcumin-core target complexes remained structurally stable during the 100 ns simulation, and MM/GBSA calculations indicated significantly negative binding free energies, suggesting strong binding driving forces. Curcumin may exert therapeutic effects on CSFP through a multi-target mechanism, primarily by interacting with key proteins including EGFR, ICAM1, NFKB1, PTGS2, and STAT3, thereby regulating the NF-κB, TNF, and HIF-1 signaling pathways. This study provides a theoretical foundation for the application of curcumin in CSFP treatment, though further experimental validation is required.

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

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