mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions
Open accessAbstract
Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light into potential molecular mechanisms giving rise to different phenotypes. Effectively and efficiently predicting consequences of mutations on protein-nucleic acid interactions, however, remained until recently a great and unmet challenge. Here we report an updated webserver for mCSM-NA, the only scalable method we are aware of capable of quantitatively predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable data set, based on a new release of the ProNIT database, which has significantly improved the reliability and applicability of the methodology. Our new predictive model was capable of achieving a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming its previous version. The server is freely available via a user-friendly web interface at: http://structure.bioc.cam.ac.uk/mcsm_na.
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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 [29]
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- Authors
- Pires, D. E. V., & Ascher, D. B.
- Affiliations
- Fiocruz Minas (BR)
- The University of Melbourne (AU)
- Corresponding author
- Pires, Douglas E.V.
- Ascher, David B.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-04-04
- Volume
- 45
- Issue
- W1
- Pages
- W241-W246
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC5570212
- OpenAlex
- W2605361745
- Cited by
- 127
- References
- 29
- Citations by year
- 2026: 12 · 2025: 10 · 2024: 12 · 2023: 14 · 2022: 14 · 2021: 21 · 2020: 20 · 2019: 11 · 2018: 9 · 2017: 4
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Arca - Repositório Institucional da Fiocruz (submittedVersion, other-oa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Research Councils UK
- University of Melbourne (UOM0017)
- State Government of Victoria
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (MR/M026302/1)
- Fundação Oswaldo Cruz
- Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa
- Medical Research Council (MR/M026302/1)
- National Health and Medical Research Council (1072476)
- 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, protein-nucleic acid interactions, pharmacophore modeling, reverse mutation, web server, mutation effect prediction
- MeSH terms
- Machine Learning, Protein Conformation, alpha-Helical, Protein Conformation, beta-Strand, Binding Sites, DNA, DNA-Binding Proteins, Humans, Models, Molecular, Protein Binding, Software, Thermodynamics, Models, Statistical, RNA-Binding Proteins, Mutation, Missense, Internet, Protein Interaction Domains and Motifs, Protein Stability, Reverse Genetics
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