Bioinformatics Advance Access published online on November 17, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti784
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1 Department of Physics, University of Cincinnati, Cincinnati, OH 45221
* To whom correspondence should be addressed.
Motivation: Membrane domain prediction has recently been re-evaluated by several groups, suggesting that the accuracy of existing methods is still rather limited. In this work, we revisit this problem and propose novel methods for prediction of alpha-helical as well as beta-sheet transmembrane (TM) domains. The new approach is based on a compact representation of an amino acid residue and its environment, which consists of predicted solvent accessibility and secondary structure of each amino acid. A recently introduced and trained on a set of soluble proteins method for solvent accessibility prediction is used here to indicate segments of residues that are predicted not to be accessible to water and, therefore, may be "buried" in the membrane. While evolutionary profiles in the form of a multiple alignment are used to derive these simple "structural profiles", they are not used explicitly for the membrane domain prediction and the overall number of parameters in the model is significantly reduced. This offers the possibility of a more reliable estimation of the free parameters in the model with a limited number of experimentally resolved membrane protein structures. Results: Using cross-validated training on available sets of structurally resolved and non-redundant alpha and beta membrane proteins, we demonstrate that membrane domain prediction methods based on such a compact representation outperform approaches that utilize explicitly evolutionary profiles and multiple alignments. Moreover, using an external evaluation by the TMH Benchmark server we show that our final prediction protocol for the TM helix prediction is competitive with the state-of-the-art methods, achieving per-residue accuracy of about 89% and per-segment accuracy of about 80% on the set of high resolution structures used by the TMH Benchmark server. At the same time the observed rates of confusion with signal peptides and globular proteins are the lowest among the tested methods. The new method is available on-line at http://minnou.cchmc.org.
Received August 19, 2005
Revised October 28, 2005
Accepted November 14, 2005
Article
Enhanced recognition of protein transmembrane domains with prediction-based structural profiles
Baoqiang Cao 1,
Aleksey Porollo 2,
Rafal Adamczak 2,
Mark Jarrell 1,
and
Jaroslaw Meller 3 *
2 Biomedical Informatics, Children's Hospital Research Foundation, Cincinnati, OH 45229
3 Biomedical Informatics, Children's Hospital Research Foundation, Cincinnati, OH 45229; Department of Informatics, Nicholas Copernicus University, 87-100 Torun, Poland
Jaroslaw Meller, E-mail: jmeller{at}chmcc.org
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