ArticleBioinformatics (Oxford, England)2014
mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 572 papers, 1 of them a synthesis that pooled it.
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Who cites it
572 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,084 citations in OpenAlex.
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- A Compound Heterozygous Missense Variant in TheInternational journal of fertility & sterility · 2026Article
- Prenatal and postnatal manifestations of WBP11-related disorder in Chinese patients: expanding the phenotypic and mutational spectrum.Human genomics · 2026Article
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512 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
motivationMutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein-protein and protein-nucleic acid interactions.
resultsWe show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. AVAILABILITY AND IMPLEMENTATION: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.
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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.