We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We first propose a formal model to assess probabilistically function-impacting variants. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. neutral) without specifically considering possible molecular alterations. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics.
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