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mCSM-membrane: predicting the effects of mutations on transmembrane proteins.
Nucleic Acids Res. 2020 07 02; 48(W1):W147-W153.NA

Abstract

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

Authors+Show Affiliations

Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia. Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia. School of Computing and Information Systems, University of Melbourne, Parkville, VIC, 3052, Australia.Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia. Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia.Computational Biology and Clinical Informatics, Baker Institute, Melbourne, Victoria 3004, Australia. Structural Biology and Bioinformatics, Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, Parkville, VIC, 3052, Australia. Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32469063

Citation

Pires, Douglas E V., et al. "MCSM-membrane: Predicting the Effects of Mutations On Transmembrane Proteins." Nucleic Acids Research, vol. 48, no. W1, 2020, pp. W147-W153.
Pires DEV, Rodrigues CHM, Ascher DB. MCSM-membrane: predicting the effects of mutations on transmembrane proteins. Nucleic Acids Res. 2020;48(W1):W147-W153.
Pires, D. E. V., Rodrigues, C. H. M., & Ascher, D. B. (2020). MCSM-membrane: predicting the effects of mutations on transmembrane proteins. Nucleic Acids Research, 48(W1), W147-W153. https://doi.org/10.1093/nar/gkaa416
Pires DEV, Rodrigues CHM, Ascher DB. MCSM-membrane: Predicting the Effects of Mutations On Transmembrane Proteins. Nucleic Acids Res. 2020 07 2;48(W1):W147-W153. PubMed PMID: 32469063.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - mCSM-membrane: predicting the effects of mutations on transmembrane proteins. AU - Pires,Douglas E V, AU - Rodrigues,Carlos H M, AU - Ascher,David B, PY - 2020/05/28/accepted PY - 2020/05/04/revised PY - 2020/02/21/received PY - 2020/5/30/pubmed PY - 2020/5/30/medline PY - 2020/5/30/entrez SP - W147 EP - W153 JF - Nucleic acids research JO - Nucleic Acids Res. VL - 48 IS - W1 N2 - Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane. SN - 1362-4962 UR - https://www.unboundmedicine.com/medline/citation/32469063/mCSM_membrane:_predicting_the_effects_of_mutations_on_transmembrane_proteins_ L2 - https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkaa416 DB - PRIME DP - Unbound Medicine ER -
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