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

• Published in: Nature communications Vol. 11; no. 1; pp. 5918 - 13
• Main Authors: Pejaver, Vikas, Urresti, Jorge, Lugo-Martinez, Jose, Pagel, Kymberleigh A., Lin, Guan Ning, Nam, Hyun-Jun, Mort, Matthew, Cooper, David N., Sebat, Jonathan, Iakoucheva, Lilia M., Mooney, Sean D., Radivojac, Predrag
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.11.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2410; 631/114/2411; 631/114/663; Amino Acid Substitution - genetics; Amino acids; Computational Biology - methods; Computer science; Datasets; Disease; Disorders; Genetic Predisposition to Disease; Genome, Human; Genomes; Genomics; Genotype & phenotype; Hereditary diseases; Humanities and Social Sciences; Humans; Machine learning; Models, Statistical; Molecular modelling; Molecular structure; multidisciplinary; Mutation; Neurodevelopmental disorders; Ontology; Parameter estimation; Pathogenicity; Pathogens; Phenotype; Phenotypes; Protein structure; Proteins; Proteins - genetics; Science; Science (multidisciplinary); Software; Structure-function relationships
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-19669-x

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. Identifying variants capable of causing genetic disease is challenging. The authors use semisupervised learning to predict pathogenic missense variants and their impacts on protein structure and function, enabling a molecular mechanism-driven approach to studying different types of human disease.