Dataset Information

Predicting changes to INa from missense mutations in human SCN5A.

ABSTRACT: Mutations in SCN5A can alter the cardiac sodium current INa 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 INa, leaving the link from INa to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to INa was compiled. We then applied machine-learning methods to this dataset, and found that changes to INa 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 INa, 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 INa in risk assessment.

SUBMITTER: Clerx M

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

REPOSITORIES: biostudies-literature

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Predicting changes to INa from missense mutations in human SCN5A.

Clerx Michael M Heijman Jordi J Collins Pieter P Volders Paul G A PGA

Scientific reports 20180824 1

Mutations in SCN5A can alter the cardiac sodium current INa 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 INa, leaving the link from INa to clinical phenotype for mechanistic simulation studies. An exhau ...[more]

PMID: 30143662

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Project description:BackgroundRecent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools.ResultsSingle nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool.ConclusionsOur independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Dataset Information

Predicting changes to INa from missense mutations in human SCN5A.

Publications

Predicting changes to I<sub>Na</sub> from missense mutations in human SCN5A.

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