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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 significance. The substituted residues’ location on the protein correlated with channel function and strongly contributed to predictions, while conservedness and physico-chemical properties did not. However, predictions were not sufficiently accurate to form a basis for mechanistic studies. These results show that changes to  I  Na , the mechanism through which  SCN5A  mutations create cardiac risk, are already difficult to predict using purely  in-silico  methods. This partly explains the limited success of systems to predict clinical significance of  SCN5A  variants, and underscores the need for functional studies of  I  Na  in risk assessment.

Predicting changes to INa from missense mutations in human SCN5A