{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T04:16:51Z","timestamp":1775276211643,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2018,4,27]],"date-time":"2018-04-27T00:00:00Z","timestamp":1524787200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Fund for Scientific Research","award":["FNRS"],"award-info":[{"award-number":["FNRS"]}]},{"name":"M.R. Research Director"},{"DOI":"10.13039\/501100002661","name":"FNRS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (\u0394\u0394G0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured \u0394\u0394G0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used \u0394\u0394G0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Note<\/jats:title>\n                    <jats:p>The article 10.1093\/bioinformatics\/bty340\/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty348","type":"journal-article","created":{"date-parts":[[2018,4,25]],"date-time":"2018-04-25T15:59:04Z","timestamp":1524671944000},"page":"3659-3665","source":"Crossref","is-referenced-by-count":155,"title":["Quantification of biases in predictions of protein stability changes upon mutations"],"prefix":"10.1093","volume":"34","author":[{"given":"Fabrizio","family":"Pucci","sequence":"first","affiliation":[{"name":"Department of BioModeling BioInformatics & BioProcesses, Universit\u00e9 Libre de Bruxelles, Brussels, Belgium"}]},{"given":"Katrien V","family":"Bernaerts","sequence":"additional","affiliation":[{"name":"Department of BioModeling BioInformatics & BioProcesses, Universit\u00e9 Libre de Bruxelles, Brussels, Belgium"},{"name":"Department of Biobased Materials, Maastricht University, Maastricht, The Netherlands"}]},{"given":"Jean Marc","family":"Kwasigroch","sequence":"additional","affiliation":[{"name":"Department of BioModeling BioInformatics & BioProcesses, Universit\u00e9 Libre de Bruxelles, Brussels, Belgium"}]},{"given":"Marianne","family":"Rooman","sequence":"additional","affiliation":[{"name":"Department of BioModeling BioInformatics & BioProcesses, Universit\u00e9 Libre de Bruxelles, Brussels, Belgium"}]}],"member":"286","published-online":{"date-parts":[[2018,4,26]]},"reference":[{"key":"2023012712364208700_bty348-B1","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1021\/acs.jctc.7b00125","article-title":"The Rosetta all-atom energy function for macromolecular modeling and design","volume":"13","author":"Alford","year":"2017","journal-title":"J. Chem. Theory Comput"},{"key":"2023012712364208700_bty348-B2","doi-asserted-by":"crossref","first-page":"D120","DOI":"10.1093\/nar\/gkh082","article-title":"ProTherm, version 4.0: thermodynamic database for proteins and mutants","volume":"32","author":"Bava","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023012712364208700_bty348-B3","doi-asserted-by":"crossref","first-page":"W306","DOI":"10.1093\/nar\/gki375","article-title":"I-mutant2.0: predicting stability changes upon mutation from the protein sequence or structure","volume":"33","author":"Capriotti","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2023012712364208700_bty348-B4","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res"},{"key":"2023012712364208700_bty348-B5","doi-asserted-by":"crossref","first-page":"S5.","DOI":"10.1186\/1471-2105-14-S2-S5","article-title":"iStable: off-the-shelf predictor integration for predicting protein stability changes","volume":"14","author":"Chen","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023012712364208700_bty348-B6","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1002\/prot.20810","article-title":"Prediction of protein stability changes for single site mutations using support vector machines","volume":"62","author":"Cheng","year":"2006","journal-title":"Proteins Struct. Funct. Bioinformatics"},{"key":"2023012712364208700_bty348-B7","doi-asserted-by":"crossref","first-page":"143001.","DOI":"10.1088\/1361-648X\/aa5c76","article-title":"Computational protein design: a review","volume":"29","author":"Coluzza","year":"2017","journal-title":"J. Phys. Condens. Matter"},{"key":"2023012712364208700_bty348-B8","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1093\/bioinformatics\/btp445","article-title":"Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: poPMuSiC-2.0","volume":"25","author":"Dehouck","year":"2009","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B9","doi-asserted-by":"crossref","first-page":"151.","DOI":"10.1186\/1471-2105-12-151","article-title":"PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality","volume":"12","author":"Dehouck","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023012712364208700_bty348-B10","doi-asserted-by":"crossref","first-page":"2816","DOI":"10.1093\/bioinformatics\/btv291","article-title":"INPS: predicting the impact of non-synonymous variations on protein stability from sequence","volume":"31","author":"Fariselli","year":"2015","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B11","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1038\/nmeth.3027","article-title":"Deep mutational scanning: a new style of protein science","volume":"11","author":"Fowler","year":"2014","journal-title":"Nat. Methods"},{"key":"2023012712364208700_bty348-B12","doi-asserted-by":"crossref","first-page":"S7.","DOI":"10.1186\/1471-2164-15-S4-S7","article-title":"NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation","volume":"15","author":"Giollo","year":"2014","journal-title":"BMC Genomics"},{"key":"2023012712364208700_bty348-B13","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/S0022-2836(02)00442-4","article-title":"Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations","volume":"320","author":"Guerois","year":"2002","journal-title":"J. Mol. Biol"},{"key":"2023012712364208700_bty348-B14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023012712364208700_bty348-B15","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1038\/nature19946","article-title":"The coming of age of de novo protein design","volume":"537","author":"Huang","year":"2016","journal-title":"Nature"},{"key":"2023012712364208700_bty348-B16","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1002\/prot.22921","article-title":"Role of conformational sampling in computing mutation-induced changes in protein structure and stability","volume":"79","author":"Kellogg","year":"2011","journal-title":"Proteins"},{"key":"2023012712364208700_bty348-B17","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1002\/humu.21242","article-title":"Performance of protein stability predictors","volume":"31","author":"Khan","year":"2010","journal-title":"Hum. Mutat"},{"key":"2023012712364208700_bty348-B18","first-page":"116","article-title":"MAESTROweb: a web server for structure based protein stability prediction","volume":"16","author":"Laimer","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B19","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1093\/bioinformatics\/btn353","article-title":"Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis","volume":"24","author":"Masso","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B20","doi-asserted-by":"crossref","DOI":"10.1155\/2014\/278385","article-title":"AUTO-MUTE 2.0: a portable framework with enhanced capabilities for predicting protein functional consequences upon mutation","volume":"2014","author":"Masso","year":"2014","journal-title":"Adv. Bioinformatics"},{"key":"2023012712364208700_bty348-B21","doi-asserted-by":"crossref","first-page":"W229","DOI":"10.1093\/nar\/gkx439","article-title":"SDM: a server for predicting effects of mutations on protein stability","volume":"45","author":"Pandurangan","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023012712364208700_bty348-B22","doi-asserted-by":"crossref","first-page":"W239","DOI":"10.1093\/nar\/gkl190","article-title":"CUPSAT: prediction of protein stability upon point mutations","volume":"34","author":"Parthiban","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023012712364208700_bty348-B23","doi-asserted-by":"crossref","first-page":"W314","DOI":"10.1093\/nar\/gku411","article-title":"DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach","volume":"42","author":"Pires","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023012712364208700_bty348-B24","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1093\/bioinformatics\/btt691","article-title":"mCSM: predicting the effects of mutations in proteins using graph-based signatures","volume":"30","author":"Pires","year":"2014","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B25","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1093\/protein\/gzp030","article-title":"Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details","volume":"22","author":"Potapov","year":"2009","journal-title":"Protein Eng. Des. Sel"},{"key":"2023012712364208700_bty348-B26","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.ifacol.2015.05.068","article-title":"Symmetry principles in optimization problems: an application to protein stability prediction","volume":"48","author":"Pucci","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"2023012712364208700_bty348-B27","doi-asserted-by":"crossref","first-page":"023104.","DOI":"10.1063\/1.4947493","article-title":"High-quality thermodynamic data on the stability changes of proteins upon single-site mutations","volume":"45","author":"Pucci","year":"2016","journal-title":"J. Phys. Chem. Ref. Data"},{"key":"2023012712364208700_bty348-B28","doi-asserted-by":"crossref","first-page":"2936","DOI":"10.1093\/bioinformatics\/btw361","article-title":"STRUM: structure-based prediction of protein stability changes upon single-point mutation","volume":"32","author":"Quan","year":"2016","journal-title":"Bioinformatics"},{"key":"2023012712364208700_bty348-B29","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/nprot.2010.5","article-title":"I-TASSER: a unified platform for automated protein structure and function prediction","volume":"5","author":"Roy","year":"2010","journal-title":"Nat. Protoc"},{"key":"2023012712364208700_bty348-B30","doi-asserted-by":"crossref","first-page":"e46084.","DOI":"10.1371\/journal.pone.0046084","article-title":"Assessing predictors of changes in protein stability upon mutation using self-consistency","volume":"7","author":"Thiltgen","year":"2012","journal-title":"PLoS One"},{"key":"2023012712364208700_bty348-B31","article-title":"Size-dependent relationships between protein stability and thermal unfolding temperature have important implications for analysis of protein energetics and high-throughput assays of protein-ligand interactions","author":"Watson","year":"2017","journal-title":"J. Phys. Chem. B"},{"key":"2023012712364208700_bty348-B32","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.copbio.2014.03.002","article-title":"De novo computational enzyme design","volume":"29","author":"Zanghellini","year":"2014","journal-title":"Curr. Opin. Biotechnol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/21\/3659\/48921073\/bioinformatics_34_21_3659.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/21\/3659\/48921073\/bioinformatics_34_21_3659.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T08:26:33Z","timestamp":1674807993000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/21\/3659\/4987874"}},"subtitle":[],"editor":[{"given":"Alfonso","family":"Valencia","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,4,26]]},"references-count":32,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2018,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty348","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/308239","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,11,1]]},"published":{"date-parts":[[2018,4,26]]}}}