摘要
About 20%-30% of genome products have been predicted as membrane proteins, which have sig-nificant 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 predic-tion accuracy of MSCWT for TMHs is 91.6% and that for membrane protein is 89.3%.
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%.
基金
the National Natural Science Foundation of China (Grant Nos. 20775060 and 20335030), the Teaching
Research Award Program for Out-standing Young Teachers in Higher Education Institutions of China
the Key Lab of Polymer Material Science of Gansu Province, China
关键词
横跨膜构造
TMHs
光谱学
子波转移
Biochemistry and Molecular Biophysics
Computer Applications: Computational Biology
Membranes: Cell Biology
Mathematical Biology: Computational Biology
