Computational predictors fail to identify amino acid substitution effects at rheostat positions.

M Miller, Y Bromberg, L Swint-Kruse

Journal: Scientific reports 2018;7():41329

PMID: 28134345

Abstract

Many computational approaches exist for predicting the effects of amino acid substitutions. Here, we considered whether the protein sequence position class - rheostat or toggle - affects these predictions. The classes are defined as follows: experimentally evaluated effects of amino acid substitutions at toggle positions are binary, while rheostat positions show progressive changes. For substitutions in the LacI protein, all evaluated methods failed two key expectations: toggle neutrals were incorrectly predicted as more non-neutral than rheostat non-neutrals, while toggle and rheostat neutrals were incorrectly predicted to be different. However, toggle non-neutrals were distinct from rheostat neutrals. Since many toggle positions are conserved, and most rheostats are not, predictors appear to annotate position conservation better than mutational effect. This finding can explain the well-known observation that predictors assign disproportionate weight to conservation, as well as the field's inability to improve predictor performance. Thus, building reliable predictors requires distinguishing between rheostat and toggle positions.

Address: Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Dr, New Brunswick, NJ 08901, USA.; Department for Bioinformatics and Computational Biology, Technische Universität München, Boltzmannstr. 3, 85748 Garching/Munich, Germany.; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Technische Universität München, 85748 Garching/Munich, Germany.; Department of Genetics, Rutgers University, Human Genetics Institute, Life Sciences Building, 145 Bevier Road, Piscataway, NJ 08854, USA.; Institute for Advanced Study at Technische Universität München (TUM-IAS), Lichtenbergstraße 2a, 85748, Garching/Munich, Germany.; The Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA.
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