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Protein Sci. 2019 Nov 6. doi: 10.1002/pro.3774. [Epub ahead of print]

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.

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Department of Biochemistry, University of Cambridge, Tennis Court Road, UK.
MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK.


Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms, but also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires understanding of impacts of missense mutations in gene expression and regulation, and on disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate, but are also time consuming and costly. Computational tools used to predict impacts of mutations can provide useful information more quickly. Here we focus on two widely-used structure-based approaches, originally developed in the Blundell lab: site-directed mutator SDM, a statistical approach to analysing amino acid substitutions, and mCSM, which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here we describe DUET which uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. This article is protected by copyright. All rights reserved.


amino acid substitution probabilities; drug resistance; genetic disorders; machine learning; mutations; protein stability and interactions; protein structure


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