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Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations
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DOI：10.1016/j.bpj.2020.12.002.png)
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摘要
En 中文
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-beta 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.
Keyword:
LONG-QT SYNDROME
CEREBRAL AMYLOID ANGIOPATHY
REVERSIBLE RANDOM COIL
BETA-SHEET TRANSITION
DYNAMICS SIMULATIONS
COUPLING-CONSTANTS
FUNCTIONAL IMPACT
CALMODULIN
MUTATIONS
CALCIUM
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期刊
IF:
3.1
论文数:
5.0W
被引数:
4.4W
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引用论文
Histological and immunohistochemical characteristics of cerebral amyloid angiopathy in elderly dogs.png)
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