title: Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.
abstract: Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease.
authors.affiliation: Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universität München, Garching/Munich, Germany.|TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Technische Universität München, Garching, Germany.
authors.forename: Jonas
authors.identifier: 0000-0002-8131-660X
authors.initials: J
authors.lastname: Reeb
authors.affiliation: Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universität München, Garching/Munich, Germany.
authors.forename: Maximilian
authors.identifier: 0000-0002-1265-4701
authors.initials: M
authors.lastname: Hecht
authors.affiliation: Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universität München, Garching/Munich, Germany.|Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America.|Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.
authors.forename: Yannick
authors.identifier: 0000-0002-0717-2927
authors.initials: Y
authors.lastname: Mahlich
authors.affiliation: Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America.|Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.
authors.forename: Yana
authors.initials: Y
authors.lastname: Bromberg
authors.affiliation: Department of Informatics, Bioinformatics & Computational Biology-i12, Technische Universität München, Garching/Munich, Germany.|Institute for Advanced Study (TUM-IAS), Garching/Munich, Germany.|Institute for Food and Plant Sciences WZW, Technische Universität München, Weihenstephan, Freising, Germany.
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004195:Disease Models, Animal; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D051379:Mice; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011506:Proteins / Q000235:genetics*
chemical_list: D011506:Proteins