Gromiha M Michael, Ahmad Shandar, Suwa Makiko
Computational 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.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W164-7. doi: 10.1093/nar/gki367.
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/.
我们开发了一个网络服务器TMBETA-NET，用于鉴别外膜蛋白并预测其跨膜β链片段。我们系统地分析了球状蛋白和外膜蛋白的氨基酸组成，并提出了一种鉴别外膜蛋白的统计方法。跨膜片段的预测主要基于前馈神经网络，并根据β链长度进行优化。我们的程序以氨基酸序列作为输入，并显示蛋白质类型以及作为一段突出显示的氨基酸残基的跨膜β链片段。此外，还通过一种着色方案给出了残基位于跨膜β链中的概率。我们观察到，仅从氨基酸序列信息就能以89%的准确率鉴别出外膜蛋白，其跨膜β链片段的预测准确率为73%。该预测服务器可在http://psfs.cbrc.jp/tmbeta-net/获取。
