Key result
Molecular dynamics and machine learning predict KCNH2 variant trafficking defects with ~75% accuracy.
Why the study?
Most missense variants in LQTS-linked genes are classified as variants of uncertain significance due to a lack of clear disease association, prompting evaluation of computational methods to identify disease-associated variants.
Structure-based molecular dynamics simulations combined with machine learning can improve the classification accuracy of variants of uncertain significance in the Kv11.1 channel PAS domain associated with Long QT syndrome.
1 Abstract Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias despite otherwise normal metrics of cardiac function. Genetic variants in several different cardiac ion channel genes, including KCNH2 , are known to cause LQTS. The population frequency of rare non-synonymous (missense) variants in LQTS-linked genes far outpaces the true incidence of the disease. Therefore, only a small percentage of missense variants identified in LQTS-linked genes are expected to associate with LQTS. Because of a lack of clear association between variants identified in LQTS-linked alleles and diseases, most variants are classified as variants of uncertain physiological significance (VUS). Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) can improve the identification of missense variants in LQTS-linked genes that associate with LQTS. To do this, we focused on investigating KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or loss-of-function (LOF) phenotypes in vitro . We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common LOF phenotype for LQTS-associated variants. Specifically, we used these computational techniques to correlate structural and dynamic changes in the Kv11.1 channel protein PAS domain (PASD) with Kv11.1 channel protein trafficking phenotypes. These simulations unveiled several molecular features, including the numbers of hydrating waters and H-Bonds, as well as FoldX scores, that are predictive of trafficking. We then used statistical and ML (Decision tree (DT), Random forest (RF), and Support vector machine (SVM)) techniques to classify variants using these simulation-derived features. Together with bioinformatics data, such as sequence conservation and folding energies, we were able to predict with reasonable accuracy (≈75%) which KCNH2 variants do not traffic normally. We conclude, structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to a significant improvement (≈10%) in classification accuracy and this approach should therefore be considered to complement the classification of VUS in the Kv11.1 channel PASD.
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Immadisetty et al. (2021) studied Congenital long QT syndrome (LQTS). Structure-based molecular dynamics (MD) simulations and machine learning (ML) was evaluated on Classification accuracy of KCNH2 variants that do not traffic normally. Structure-based molecular dynamics simulations and machine learning predicted KCNH2 variant trafficking defects with ≈75% accuracy, improving classification accuracy by ≈10%.
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