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Bioinformatics Advance Access originally published online on November 15, 2007
Bioinformatics 2008 24(4):513-520; doi:10.1093/bioinformatics/btm548
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins

Arlo Randall 1,2, Jianlin Cheng 3, Michael Sweredoski 1,2 and Pierre Baldi 1,2,*

1School 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

*To whom correspondence should be addressed.


  Abstract

Motivation: Transmembrane β-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.

Results: We develop a suite (TMBpro) of specialized predictors for predicting secondary structure (TMBpro-SS), β-contacts (TMBpro-CON) and tertiary structure (TMBpro-3D) of transmembrane β-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 β-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 <6.0 Å for 9 of 14 proteins. For 6 of 14 predictions, the RMSD is <5.0 Å, with a GDT_TS score greater than 60.0.

Availability: http://www.igb.uci.edu/servers/psss.html

Contact: pfbaldi{at}ics.uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alfonso Valencia

Received on July 11, 2007; revised on October 6, 2007; accepted on October 29, 2007

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