ArticleBioinformatics (Oxford, England)2024
DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.Journal of computational chemistry · 2025Article
- MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes.Briefings in bioinformatics · 2024Article
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3 authors.
Funding
Abstract
motivationProtein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes.
resultsWe developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance. AVAILABILITY AND IMPLEMENTATION: https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.
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