ArticleNucleic acids research2021
mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 86 citations in OpenAlex.
- Structure of the mouse cytoplasmic lattice.Nature · 2026Article
- Article
- TCL1A mediates DNA methylation defects in recurrent hydatidiform mole with NLRP7 pathogenic variants.Nature communications · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- Comprehensive In Silico Analysis of the CXCL12, C55Y Mutation Reveals Structural and Functional Disruption in CXCR4/CXCR7 Signaling.Evolutionary bioinformatics online · 2026Article
- Structure-guided engineering of prototype foamy virus Env identifies key residues for heparan sulfate binding and enhances transduction efficiency.Frontiers in bioengineering and biotechnology · 2026Article
- Structural insights into cationic amino acid transport and viral receptor engagement by CAT1.Nature communications · 2025Article
- In silico evaluation of anticancer peptide as a novel therapeutic agent against AURKA for targeting oral carcinogenesis.Journal of molecular modeling · 2025Article
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.Briefings in bioinformatics · 2025Article
- PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.Protein science : a publication of the Protein Society · 2025Article
- Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.Journal of chemical information and modeling · 2025Article
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Rapid restoration of potent neutralization activity against the latest Omicron variant JN.1 via AI rational design and antibody engineering.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A rare haplotype of the GJD3 gene segregating in familial Meniere's disease interferes with connexin assembly.Genome medicine · 2025Article
- Resilience and evolutionary insights in PPI networks: comparative analysis of node resilience and centrality measures.Frontiers in genetics · 2025Article
- Likely Pathogenic/Pathogenic Variants in the Spliceosome Complex Genes SNRNP200, SF3B1, SF3B2, and SF3B4 Implicated in Nonsyndromic Orofacial Cleft.Human mutation · 2025Article
- Bioinformatic approach to identifying causative missense polymorphisms in animal genomes.BMC genomics · 2024Article
- Cryo-EM structure of the human subcortical maternal complex and the associated discovery of infertility-associated variants.Nature structural & molecular biology · 2024Article
- Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Communications biology · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
Protein-protein interactions play a crucial role in all cellular functions and biological processes and mutations leading to their disruption are enriched in many diseases. While a number of computational methods to assess the effects of variants on protein-protein binding affinity have been proposed, they are in general limited to the analysis of single point mutations and have been shown to perform poorly on independent test sets. Here, we present mmCSM-PPI, a scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations. We expanded our well-established graph-based signatures in order to capture physicochemical and geometrical properties of multiple wild-type residue environments and integrated them with substitution scores and dynamics terms from normal mode analysis. mmCSM-PPI was able to achieve a Pearson's correlation of up to 0.75 (RMSE = 1.64 kcal/mol) under 10-fold cross-validation and 0.70 (RMSE = 2.06 kcal/mol) on a non-redundant blind test, outperforming existing methods. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/mmcsm_ppi.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.