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

Prediction of transmembrane β‐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 β‐strands. We introduce the concept of “residue probability” for assigning residues in transmembrane β‐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 β‐strands will be discussed. We have developed a Web server for predicting the transmembrane β‐strands from the amino acid sequence, and the prediction results are available at http://psfs.cbrc.jp/tmbeta‐net/. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 762–767, 2004

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