Rhapsody: Pathogenicity prediction of human missense variants based on protein sequence, structure and dynamics

Abstract

The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. Here we present a significant extension that integrates coevolutionary data from Pfam database and we also introduce a new interface (Rhapsody) that enables fully automated assessment of pathogenicity. Benchmarked against a dataset of about 20,000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of our approach by in silico saturation mutagenesis study of human H-Ras. The tool is made available both as a webtool (rhapsody.csb.pitt.edu) and an open source Python package (pip install prody-rhapsody).