Liu HongDe, Wang Rui, Lu XiaoQuan, Chen Jing, Liu Xiuhui, Ding Lan
1College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China.
2College of Life Science, Northwest Normal University, Lanzhou, 730070 China.
Chin Sci Bull. 2008;53(7):1011-1014. doi: 10.1007/s11434-008-0055-5. Epub 2008 May 23.
About 20%-30% of genome products have been predicted as membrane proteins, which have significant biological functions. The prediction of the amount and position for the transmembrane protein helical segments (TMHs) is the hot spot in bioinformatics. In this paper, a new approach, maximum spectrum of continuous wavelet transform (MSCWT), is proposed to predict TMHs. The predictions for eight SARS-CoV membrane proteins indicate that MSCWT has the same capacity with software TMpred. Moreover, the test on a dataset of 131 structure-known proteins with 548 TMHs shows that the prediction accuracy of MSCWT for TMHs is 91.6% and that for membrane protein is 89.3%.
约20%-30%的基因组产物被预测为具有重要生物学功能的膜蛋白。跨膜蛋白螺旋片段（TMHs）数量和位置的预测是生物信息学中的热点。本文提出了一种新方法——连续小波变换最大谱（MSCWT）来预测TMHs。对8种严重急性呼吸综合征冠状病毒（SARS-CoV）膜蛋白的预测表明，MSCWT与软件TMpred具有相同的能力。此外，对一个包含131个已知结构蛋白和548个TMHs的数据集进行测试表明，MSCWT对TMHs的预测准确率为91.6%，对膜蛋白的预测准确率为89.3%。
