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

✍ Scribed by M. Michael Gromiha; Shandar Ahmad; Makiko Suwa

• Publisher: John Wiley and Sons
• Year: 2004
• Tongue: English
• Weight: 76 KB
• Volume: 25
• Category: Article
• ISSN: 0192-8651
• DOI: 10.1002/jcc.10386

✦ Synopsis

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/.