Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

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-

SUBMITTER: Popov P 

PROVIDER: S-EPMC6620012 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature