Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Vikas Pejaver, Jorge Urresti, Jose Lugo-Martinez, Kymberleigh A. Pagel, Guan Ning Lin, Hyun-Jun Nam, Matthew Mort, David N. Cooper, Jonathan Sebat, Lilia M. Iakoucheva (), Sean D. Mooney () and Predrag Radivojac ()
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Vikas Pejaver: Indiana University
Jorge Urresti: University of California San Diego
Jose Lugo-Martinez: Indiana University
Kymberleigh A. Pagel: Indiana University
Guan Ning Lin: University of California San Diego
Hyun-Jun Nam: University of California San Diego
Matthew Mort: Cardiff University
David N. Cooper: Cardiff University
Jonathan Sebat: University of California San Diego
Lilia M. Iakoucheva: University of California San Diego
Sean D. Mooney: University of Washington
Predrag Radivojac: Indiana University

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.

Date: 2020
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DOI: 10.1038/s41467-020-19669-x

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