Gromiha M Michael, Ahmad Shandar, Suwa Makiko
Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi Frontier Building 17F, 2-43 Aomi, Koto-ku, Tokyo 135-0064, Japan.
J Comput Chem. 2004 Apr 15;25(5):762-7. doi: 10.1002/jcc.10386.
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/.
预测外膜蛋白（OMP）中的跨膜β链是计算化学和生物学中的重要问题之一。在这项工作中，我们提出了一种基于神经网络的方法来识别跨膜β链。我们引入了“残基概率”的概念，用于在跨膜β链片段中分配残基。我们的方法的性能通过单残基准确性、相关性、特异性和敏感性进行评估。仅从氨基酸序列信息来看，我们预测的片段与实验观察结果具有良好的一致性，准确率达到73%。此外，还将讨论每个片段中N端和C端残基的预测能力、每个蛋白质中的片段数量以及识别跨膜β链的截止概率的影响。我们开发了一个用于从氨基酸序列预测跨膜β链的网络服务器，预测结果可在http://psfs.cbrc.jp/tmbeta-net/获取。
