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Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure
, A. Kulandaisamy
Published in Public Library of Science
2019
PMID: 31291347
Volume: 14
   
Issue: 7
Abstract
Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/ MutHTP/boroda.php. © 2019 Popov et al.
About the journal
JournalPLoS ONE
PublisherPublic Library of Science
ISSN19326203
Open AccessNo
Concepts (16)
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    Membrane protein
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    Amino acid sequence
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    Amino acid substitution
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    Article
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    Brain region
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    Comparative study
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    DISEASE ASSOCIATION
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    DISEASE CLASSIFICATION
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    Machine learning
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    Mutational analysis
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    Point mutation
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    Prediction
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    Process development
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    Protein database
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    Protein structure
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    Validation process