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Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.
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.
Afiliação
  • Pejaver V; Department of Computer Science, Indiana University, Bloomington, IN, USA.
  • Urresti J; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
  • Lugo-Martinez J; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
  • Pagel KA; Department of Computer Science, Indiana University, Bloomington, IN, USA.
  • Lin GN; Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
  • Nam HJ; Department of Computer Science, Indiana University, Bloomington, IN, USA.
  • Mort M; Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, 220 Hackerman Hall, 3400 N Charles St, Baltimore, MD, 21218, USA.
  • Cooper DN; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
  • Sebat J; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.
  • Iakoucheva LM; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
  • Mooney SD; Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK.
  • Radivojac P; Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK.
Nat Commun ; 11(1): 5918, 2020 11 20.
Article em En | MEDLINE | ID: mdl-33219223
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Substituição de Aminoácidos / Predisposição Genética para Doença Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Substituição de Aminoácidos / Predisposição Genética para Doença Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido