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Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

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. W...

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Published in:Biophysical journal 2021-01, Vol.120 (2), p.189-204
Main Authors: McCoy, Matthew D., Hamre, John, Klimov, Dmitri K., Jafri, M. Saleet
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Language:English
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description 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 successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.
doi_str_mv 10.1016/j.bpj.2020.12.002
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subjects Computational Biology
Computational Tool
Genetic Predisposition to Disease
Genetic Variation
Humans
Machine Learning
Mutation, Missense
Phenotype
title Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations
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