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1.
J Comput Chem. 2004 Apr 15;25(5):762-7.

Neural network-based prediction of transmembrane beta-strand segments in outer membrane proteins.

Author information

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi Frontier Building 17F, 2-43 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp

Abstract

Prediction of transmembrane beta-strands in outer membrane proteins (OMP) is one of the important problems in computational chemistry and biology. In this work, we propose a method based on neural networks for identifying the membrane-spanning beta-strands. We introduce the concept of "residue probability" for assigning residues in transmembrane beta-strand segments. The performance of our method is evaluated with single-residue accuracy, correlation, specificity, and sensitivity. Our predicted segments show a good agreement with experimental observations with an accuracy level of 73% solely from amino acid sequence information. Further, the predictive power of N- and C-terminal residues in each segments, number of segments in each protein, and the influence of cutoff probability for identifying membrane-spanning beta-strands will be discussed. We have developed a Web server for predicting the transmembrane beta-strands from the amino acid sequence, and the prediction results are available at http://psfs.cbrc.jp/tmbeta-net/.

Copyright 2004 Wiley Periodicals, Inc. J Comput Chem 25: 762-767, 2004

PMID:
14978719
[PubMed - indexed for MEDLINE]
Icon for John Wiley & Sons, Inc.
2.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W164-7.

TMBETA-NET: discrimination and prediction of membrane spanning beta-strands in outer membrane proteins.

Author information

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan. michael-gromiha@aist.go.jp

Abstract

We have developed a web-server, TMBETA-NET for discriminating outer membrane proteins and predicting their membrane spanning beta-strand segments. The amino acid compositions of globular and outer membrane proteins have been systematically analyzed and a statistical method has been proposed for discriminating outer membrane proteins. The prediction of membrane spanning segments is mainly based on feed forward neural network and refined with beta-strand length. Our program takes the amino acid sequence as input and displays the type of the protein along with membrane-spanning beta-strand segments as a stretch of highlighted amino acid residues. Further, the probability of residues to be in transmembrane beta-strand has been provided with a coloring scheme. We observed that outer membrane proteins were discriminated with an accuracy of 89% and their membrane spanning beta-strand segments at an accuracy of 73% just from amino acid sequence information. The prediction server is available at http://psfs.cbrc.jp/tmbeta-net/.

PMID:
15980447
[PubMed - indexed for MEDLINE]
PMCID:
PMC1160128
Free PMC Article
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