{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:51:23Z","timestamp":1778053883293,"version":"3.51.4"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Transmembrane \u03b2-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structure of TMB proteins. Traditional homology modeling methods, however, fail on most TMB proteins since very few non-homologous TMB structures have been determined. Yet, because TMB structures conform to specific construction rules that restrict the conformational space drastically, it should be possible for methods that do not depend on target-template homology to be applied successfully.<\/jats:p>\n               <jats:p>Results: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), \u03b2-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane \u03b2-barrel proteins. We compare our results to the recent state-of-the-art predictors transFold and PRED-TMBB using their respective benchmark datasets, and leave-one-out cross-validation. Using the transFold dataset TMBpro predicts secondary structure with per-residue accuracy (Q2) of 77.8%, a correlation coefficient of 0.54, and TMBpro predicts \u03b2-contacts with precision of 0.65 and recall of 0.67. Using the PRED-TMBB dataset, TMBpro predicts secondary structure with Q2 of 88.3% and a correlation coefficient of 0.75. All of these performance results exceed previously published results by 4% or more. Working with the PRED-TMBB dataset, TMBpro predicts the tertiary structure of transmembrane segments with RMSD &amp;lt;6.0 \u00c5 for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is &amp;lt;5.0 \u00c5, with a GDT_TS score greater than 60.0.<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/www.igb.uci.edu\/servers\/psss.html<\/jats:p>\n               <jats:p>Contact: \u00a0pfbaldi@ics.uci.edu<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm548","type":"journal-article","created":{"date-parts":[[2007,11,16]],"date-time":"2007-11-16T01:43:16Z","timestamp":1195177396000},"page":"513-520","source":"Crossref","is-referenced-by-count":69,"title":["TMBpro: secondary structure, \u03b2-contact and tertiary structure prediction of transmembrane \u03b2-barrel proteins"],"prefix":"10.1093","volume":"24","author":[{"given":"Arlo","family":"Randall","sequence":"first","affiliation":[{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"},{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianlin","family":"Cheng","sequence":"additional","affiliation":[{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Sweredoski","sequence":"additional","affiliation":[{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"},{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Baldi","sequence":"additional","affiliation":[{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"},{"name":"1 School of Information and Computer Sciences, 2Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697 and 3Department of Computer Science, University of Missouri, Columbia, MO 65203, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2007,11,15]]},"reference":[{"key":"2023020209511073300_B1","doi-asserted-by":"crossref","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"key":"2023020209511073300_B2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/1471-2105-5-29","article-title":"A hidden markov model method, capable of predicting and discriminating beta-barrel outer membrane proteins","volume":"5","author":"Bagos","year":"2004","journal-title":"BMC Bioinformatics"},{"key":"2023020209511073300_B3","doi-asserted-by":"crossref","first-page":"W400","DOI":"10.1093\/nar\/gkh417","article-title":"PRED-TMBB: a web server for predicting the topology of beta-barrel outer membrane proteins","volume":"32","author":"Bagos","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023020209511073300_B4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-6-7","article-title":"Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method","volume":"6","author":"Bagos","year":"2005","journal-title":"BMC Bioinformatics"},{"key":"2023020209511073300_B5","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: an overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"2023020209511073300_B6","first-page":"575","article-title":"The principled design of large-scale recursive neuralnetwork architectures-DAG-RNNs and the protein structure prediction problem","volume":"4","author":"Baldi","year":"2003","journal-title":"J. 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