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Bioinformatics. 2005 May 1;21(9):1853-8. Epub 2005 Feb 2.

An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes.

Author information

1
Delaware Biotechnology Institute, Newark, 19715, USA.

Abstract

MOTIVATION:

Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins.

RESULTS:

We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans.

AVAILABILITY:

http://liao.cis.udel.edu/website/servers/TMMOD/

PMID:
15691854
DOI:
10.1093/bioinformatics/bti303
[Indexed for MEDLINE]

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