Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved..

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.

Errataetall:

ErratumIn: Biophys J. 2021 Oct 19;120(20):4636. - PMID 34480851

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:120

Enthalten in:

Biophysical journal - 120(2021), 2 vom: 19. Jan., Seite 189-204

Sprache:

Englisch

Beteiligte Personen:

McCoy, Matthew D [VerfasserIn]
Hamre, John [VerfasserIn]
Klimov, Dmitri K [VerfasserIn]
Jafri, M Saleet [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, N.I.H., Extramural

Anmerkungen:

Date Completed 14.05.2021

Date Revised 20.01.2022

published: Print-Electronic

ErratumIn: Biophys J. 2021 Oct 19;120(20):4636. - PMID 34480851

Citation Status MEDLINE

doi:

10.1016/j.bpj.2020.12.002

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM318980363