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. Genetic variants in several different cardiac ion channel genes, including KCNH2, are known to cause LQTS. Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) could improve the identification of missense variants in LQTS-linked genes. To do this, we investigated KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or class II (trafficking-deficient) phenotypes in vitro. We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common phenotype for LQTS-associated variants. Specifically, we used 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 hydrogen bonding pairs, as well as folding free energy scores, that are predictive of trafficking. We then used statistical and machine learning (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 that structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to an improvement in classification accuracy. Therefore, this approach should be considered to complement the classification of variant of unknown significance (VUS) in the Kv11.1 channel PASD.
|Number of pages||15|
|Journal||Journal of Molecular and Cellular Cardiology|
|State||Published - Jul 2023|
Bibliographical noteFunding Information:
Research reported in this publication release was supported by the American Heart Association under grant number 20IPA35320141 . This work was also supported by NIH /NIGMS grant GM111913 and NIH/NHLBI grant HL157635 to T.M. We are grateful for Dr. Jeff Wereszczynski for providing renderings of the PASD for our illustrations. Fig. 1 was created with BioRender.com .
Research reported in this publication release was supported by the American Heart Association under grant number 20IPA35320141. This work was also supported by NIH/NIGMS grant GM111913 and NIH/NHLBI grant HL157635 to T.M. We are grateful for Dr. Jeff Wereszczynski for providing renderings of the PASD for our illustrations. Fig. 1 was created with BioRender.com.
© 2023 Elsevier Ltd
- Machine learning
- Molecular dynamics simulations
ASJC Scopus subject areas
- Molecular Biology
- Cardiology and Cardiovascular Medicine