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While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single- and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming (LP) methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM) and the Lasso. Using statistics for leave-one-out (LOO), 10-fold, and 3-fold cross validations (CV) for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Availability<\/jats:title>\n            <jats:p>Executables of programs, tables of weights, and datasets of mutants are available from the following web page: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.wsu.edu\/~kbala\/OptSolMut.html\" ext-link-type=\"uri\">http:\/\/www.wsu.edu\/~kbala\/OptSolMut.html<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1748-7188-5-33","type":"journal-article","created":{"date-parts":[[2010,10,7]],"date-time":"2010-10-07T18:15:56Z","timestamp":1286475356000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Scoring function to predict solubility mutagenesis"],"prefix":"10.1186","volume":"5","author":[{"given":"Ye","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Deutsch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bala","family":"Krishnamoorthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2010,10,7]]},"reference":[{"issue":"19","key":"106_CR1","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1093\/bioinformatics\/btp445","volume":"25","author":"Y Dehouck","year":"2009","unstructured":"Dehouck Y, Grosfils A, Folch B, Gilis D, Bogaerts P, Rooman M: Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. 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