{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:20:16Z","timestamp":1783509616512,"version":"3.55.0"},"reference-count":62,"publisher":"Oxford University Press (OUP)","issue":"19","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Obtaining soluble proteins in sufficient concentrations is a recurring limiting factor in various experimental studies. Solubility is an individual trait of proteins which, under a given set of experimental conditions, is determined by their amino acid sequence. Accurate theoretical prediction of solubility from sequence is instrumental for setting priorities on targets in large-scale proteomics projects.<\/jats:p><jats:p>Results: We present a machine-learning approach called PROSO to assess the chance of a protein to be soluble upon heterologous expression in Escherichia coli based on its amino acid composition. The classification algorithm is organized as a two-layered structure in which the output of primary support vector machine (SVM) classifiers serves as input for a secondary Naive Bayes classifier. Experimental progress information from the TargetDB database as well as previously published datasets were used as the source of training data. In comparison with previously published methods our classification algorithm possesses improved discriminatory capacity characterized by the Matthews Correlation Coefficient (MCC) of 0.434 between predicted and known solubility states and the overall prediction accuracy of 72% (75 and 68% for positive and negative class, respectively). We also provide experimental verification of our predictions using solubility measurements for 31 mutational variants of two different proteins.<\/jats:p><jats:p>Availability: A web server for protein solubility prediction is available at http:\/\/webclu.bio.wzw.tum.de:8080\/proso<\/jats:p><jats:p>Contact: \u00a0d.frishman@wzw.tum.de<\/jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online<\/jats:p>","DOI":"10.1093\/bioinformatics\/btl623","type":"journal-article","created":{"date-parts":[[2006,12,7]],"date-time":"2006-12-07T01:14:12Z","timestamp":1165454052000},"page":"2536-2542","source":"Crossref","is-referenced-by-count":138,"title":["Protein solubility: sequence based prediction and experimental verification"],"prefix":"10.1093","volume":"23","author":[{"given":"Pawel","family":"Smialowski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio J.","family":"Martin-Galiano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Mikolajka","sequence":"additional","affiliation":[{"name":"1 Department of Genome Oriented Bioinformatics, Technische Universit\u00e4t M\u00fcnchen, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany and 1Max Planck Institute for Biochemistry, 82152 Martinsried, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Girschick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tad A.","family":"Holak","sequence":"additional","affiliation":[{"name":"1 Department of Genome Oriented Bioinformatics, Technische Universit\u00e4t M\u00fcnchen, Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany and 1Max Planck Institute for Biochemistry, 82152 Martinsried, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitrij","family":"Frishman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2006,12,6]]},"reference":[{"key":"2023041208435802500_","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1110\/ps.8.7.1475","article-title":"A new protein folding screen: application to the ligand binding domains of a glutamate and kainate receptor and to lysozyme and carbonic anhydrase","volume":"8","author":"Armstrong","year":"1999","journal-title":"Protein Sci"},{"key":"2023041208435802500_","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"key":"2023041208435802500_","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1093\/nar\/29.13.2884","article-title":"SPINE: an integrated tracking database and data mining approach for identifying feasible targets in high-throughput structural proteomics","volume":"29","author":"Bertone","year":"2001","journal-title":"Nucleic Acids Res"},{"key":"2023041208435802500_","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/protein\/2.3.185","article-title":"Secondary structure prediction: combination of three different methods","volume":"2","author":"Biou","year":"1988","journal-title":"Protein Eng"},{"key":"2023041208435802500_","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/0022-5193(82)90191-6","article-title":"The structural dependence of amino acid hydrophobicity parameters","volume":"99","author":"Charton","year":"1982","journal-title":"J. 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