Neural Network-Based Prediction of Transmembrane beta-Strand Segments in Outer Membrane Proteins

摘要

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/.