Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

Abstract

AbstractPredicting the functional impact of KCNQ1 variants of uncertain significance (VUS) can assist physicians in taking appropriate treatment decision for patients with genetic heart rhythm disorder. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1, that performs better or comparable with algorithms only on evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of evolutionary features in predicting variant activation voltage and deactivation time. As AI is increasing applied to problems in biology, biophysical understanding will be critical with respect to ‘explainable AI’, i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.Author summaryHeartbeat is maintained by electrical impulses generated by ion-conducting channel proteins in the heart such as the KCNQ1 channel. Heritable mutations in KCNQ1 can lead to channel loss-of-function and predisposition to fatal irregularities of the heartbeat. Machine learning methods that can predict the outcome of a mutation on KCNQ1 structure and function would be of great value in helping to assess the risk that a mutation could lead to a heart rhythm disorder. Recently, machine learning has made great progress in predicting the structures of proteins from their sequences. However, there are limited studies that link the effect of a mutation and change in protein structure with its function. This work presents the development of neural network models designed to predict mutation-induced changes in KCNQ1 functional parameters such as peak current density and voltage of activation. We compare the predictive value of features extracted from sequence, structure, and physicochemical properties of KCNQ1. Moreover, input sensitivity analysis connects biophysical features with specific functional parameters that provides insight into underlying molecular mechanisms for KCNQ1 ion channels. The best performing neural network model is publicly available as a webserver, called Q1VarPredBio, that delivers predictions about the functional phenotype of KCNQ1 variants to researchers and physicians.