Predicting changes to I_Na from missense mutations in human SCN5A.

ABSTRACT: Mutations in SCN5A can alter the cardiac sodium current I_Na and increase the risk of potentially lethal conditions such as Brugada and long-QT syndromes. The relation between mutations and their clinical phenotypes is complex, and systems to predict clinical severity of unclassified SCN5A variants perform poorly. We investigated if instead we could predict changes to I_Na, leaving the link from I_Na to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to I_Na was compiled. We then applied machine-learning methods to this dataset, and found that changes to I_Na could be predicted with higher sensitivity and specificity than most existing predictors of clinical

SUBMITTER: Clerx M 

PROVIDER: S-EPMC6109095 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature