Predicted molecular effect tells stories about system level of disease

J Reeb, M Hecht, Y Mahlich, Y Bromberg & B Rost (2015). Accepted for publication in PLoS CB

Poster ISMB 2016


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 (nsSNVs), also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mammalian 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.


117 OMIA mutations with sequences: omia_set.tsv

5661 OMIM mutations with sequences: omim_set_5661.tsv