Biophysical Insights into and Probabilistic Interpretation of Cardiac Ion Channel Variants of Uncertain Significance

Project: Research


  • Bian Li (PI)


Perturbed function of cardiac ion channels caused by genetic variation is the cause of a group of cardiac arrhythmias including long QT syndrome (LQTS), Brugada syndrome (BrS), and familial atrial fibrillation (FAF) that collectively affect at least 1 in 1,000 individuals. Genetic testing has become an important component of clinical care for the diagnosis and tailored treatment of affected individuals. While the clinical reports can sometimes be definitive, the overwhelming majority of identified variants are variants of uncertain significance (VUS) whose pathogenicity cannot be ascertained with available evidence. In silico functional impact prediction can enable variant interpretation. However, predictions made by widely used methods such as SIFT and PolyPhen-2 about the impact of variants on channel function are often unreliable (for example, poor specificity) and provide no biophysical insights. I argue that the limited reliability and lack of mechanistic insights of available tools are largely due to their failure to adequately account the physicochemical environment perturbed by amino acid substitutions in the context of ion channel structure and function. To address the challenge, I will leverage the latest advances in the experimental determination of ion channel structures, computational structure modeling, and high-throughput evaluation of ion channel variants (Nanion SyncroPatch 384PE Platform) to provide a precise biophysics-driven framework for mechanistic interpretation of cardiac ion channel variants. This framework will have substantial advantages of being able to provide a residue constraint profile on a much finer scale than gene and feature-based approaches while capturing spatial constraint imposed by 3D interactions. It will also provide mechanistic predictions about the effects of VUS on channel function. To enable the broad use of my approach, I will integrate these results to create a probabilistic framework and a centralized resMy preliminary computational modeling showed that modeled structures and energetic calculations offer accurate yet mechanistically interpretable and experimentally testable predictions about the functional impact of cardiac ion channel variants. My preliminary implementation of the MTR3D metric demonstrated considerable power in distinguishing pathogenic from benign variants.
Award amount$128,836.00
Award date01/01/2020
Program typePostdoctoral Fellowship
Award ID20POST35220002
Effective start/end date01/01/202012/31/2021