Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations.

ABSTRACT: Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method suc

SUBMITTER: McCoy MD 

PROVIDER: S-EPMC7840418 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature