@article{cd95838080cb41718f0b7257b7c33e03,
title = "Computational predictors fail to identify amino acid substitution effects at rheostat positions",
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.",
author = "M. Miller and Y. Bromberg and L. Swint-Kruse",
note = "Funding Information: We thank Vikas R. Pejaver, Predrag Radivojac (Indiana University, Bloomington) and Sean Mooney (University of Washington, Seattle) for sharing their not-yet-published MutPred2 code and for helpful advice on how to implement it; Yannick Mahlich, Chengsheng Zhu, Yanran Wang (all Rutgers University) Konrad Schreiber (Ludwig-Maximilians-Universit?t of Munich, LMU) and Sonakshi Bhattacharjee (Technical University of Munich, TUM) for discussions and technical support, and Inga Weise (TUM) for other support. Particular thanks are due to Burkhard Rost (TUM) for his hospitality and valuable discussions. Last but not least, we thank all those who deposit their experimental data in public databases, those who maintain these databases, and all researchers who maintain public accessibility to their variant-effect predictors. L.S.K. was supported by an internal Lied basic science grant from the KUMC Research Institute, with support from a NIH Clinical and Translational Science Award grant (UL1TR000001, formerly UL1RR033179) and by private funds. Y.B. and M.M. were supported by the NIH/NIGMS grant U01 GM115486. Y.B. was additionally supported by an Informatics Research Starter grant from the PhRMA foundation, NIH 01 GM 115486, and USDA-NIFA 1015:0228906 grants. Publisher Copyright: {\textcopyright} 2017 The Author(s).",
year = "2017",
month = jan,
day = "30",
doi = "10.1038/srep41329",
language = "English (US)",
volume = "7",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
}