mCSM: Predicting the effects of mutations in proteins using graph-based signatures
Open accessAbstract
MOTIVATION: Mutations 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. RESULTS: We 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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The graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability, dynamics and interactions with other molecules.
“Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [20]
mCSM was one of the first scalable computational tools to accurately predict the effects of mutations on binding affinity.
“mCSM (20) was one of the first scalable computational tools to accurately predict the effects of mutations on binding affinity.”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [20]
The final dataset for building mCSM-PPI2 includes 8338 single-point mutations, an increase of up to three-fold compared to previous methods that used data from the first version of SKEMPI.
“Therefore, the final dataset for building mCSM-PPI2 predictive model includes 8338 single-point mutations (S8338), which represents an increase of up to three-fold in datapoints in comparison with previous methods that used data from the first version of SKEMPI with 2007 (S2007) (20,23), 1964 (S1964) (25), 1102 (S1102) (24) and 1327 (S1327) mutations (41).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [20]
Unlike mCSM, which only needs wild-type structures as input, iSEE requires the structures of both wildtype and mutant complexes.
“Unlike mCSM for which only wild‐type structures are needed as input, iSEE does require the structures of both wildtype and mutant complexes.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [11]
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- Authors
- Pires, D. E. V., Ascher, D. B., & Blundell, T. L.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Pires, Douglas E. V.
- Blundell, Tom L.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-11-26
- Volume
- 30
- Issue
- 3
- Pages
- 335-342
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3904523
- OpenAlex
- W2103459989
- Cited by
- 1,066
- References
- 43
- Citations by year
- 2026: 89 · 2025: 121 · 2024: 122 · 2023: 121 · 2022: 98 · 2021: 128 · 2020: 118 · 2019: 88 · 2018: 55 · 2017: 38 · 2016: 46 · 2015: 32 · 2014: 9
- FWCI
- 12.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Wellcome Trust
- Winston Churchill Memorial Trust
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- State Government of Victoria
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- missense mutations, mutation effect prediction, protein stability, protein-protein interaction, protein-nucleic acid interactions, p53, tumor suppressor proteins, somatic mutations, disease progression
- MeSH terms
- Algorithms, Amino Acids, Computer Graphics, DNA, DNA-Binding Proteins, Proteins, Tumor Suppressor Protein p53, Mutation, Missense, Multiprotein Complexes, Protein Stability
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