{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T02:33:14Z","timestamp":1790735594630,"version":"4.1.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2018,10,17]],"date-time":"2018-10-17T00:00:00Z","timestamp":1539734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"EBA-PRISM"},{"name":"Israel Ministry of Science and Technology"},{"name":"Italian Ministry of Foreign Affair and International Cooperation","award":["NB-T\u2019s"],"award-info":[{"award-number":["NB-T\u2019s"]}]},{"name":"Abraham E. Kazan Chair in Structural Biology"},{"DOI":"10.13039\/501100004375","name":"Tel Aviv University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004375","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Accurate prediction of protein stability changes upon single-site variations (\u0394\u0394G) is important for protein design, as well as for our understanding of the mechanisms of genetic diseases. The performance of high-throughput computational methods to this end is evaluated mostly based on the Pearson correlation coefficient between predicted and observed data, assuming that the upper bound would be 1 (perfect correlation). However, the performance of these predictors can be limited by the distribution and noise of the experimental data. Here we estimate, for the first time, a theoretical upper-bound to the \u0394\u0394G prediction performances imposed by the intrinsic structure of currently available \u0394\u0394G data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Given a set of measured \u0394\u0394G protein variations, the theoretically \u201cbest predictor\u201d is estimated based on its similarity to another set of experimentally determined \u0394\u0394G values. We investigate the correlation between pairs of measured \u0394\u0394G variations, where one is used as a predictor for the other. We analytically derive an upper bound to the Pearson correlation as a function of the noise and distribution of the \u0394\u0394G data. We also evaluate the available datasets to highlight the effect of the noise in conjunction with \u0394\u0394G distribution. We conclude that the upper bound is a function of both uncertainty and spread of the \u0394\u0394G values, and that with current data the best performance should be between 0.7 and 0.8, depending on the dataset used; higher Pearson correlations might be indicative of overtraining. It also follows that comparisons of predictors using different datasets are inherently misleading.<\/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>","DOI":"10.1093\/bioinformatics\/bty880","type":"journal-article","created":{"date-parts":[[2018,10,16]],"date-time":"2018-10-16T15:15:47Z","timestamp":1539702947000},"page":"1513-1517","source":"Crossref","is-referenced-by-count":42,"title":["A natural upper bound to the accuracy of predicting protein stability changes upon mutations"],"prefix":"10.1093","volume":"35","author":[{"given":"Ludovica","family":"Montanucci","sequence":"first","affiliation":[{"name":"Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[{"name":"Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nir","family":"Ben-Tal","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, George S. 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