Prediction and interpretation of deleterious coding variants in terms of protein structural stability.

ABSTRACT: The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with hig

SUBMITTER: Ancien F 

PROVIDER: S-EPMC5852127 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature