Predicting Transmembrane Topology of β-barrel Membrane Proteins with A Hidden Markov Model
Very few methods address the problem of predicting β-barrel membrane proteins directly from sequence because ofvery few highresolution structures for transmembrane β-barrel proteins have been determined thus far. In this paper, we developed a novel method based on Hidden Markov Model (HMM) to model and predict the transmembrane regions of β-barrel membrane proteins. This model is evolved from an existing method with two important improvements in architectures of strand and β-turns. The resulting method achieved a prediction accuracy of 88％ on a two-state test, which is better than many existing methods based on HMM or nonHMM. Furthermore, this model achieved a high accuracy of 89.7％ on four-state topology predictions. All these results indicate that our method is powerful for transmembrane topology prediction ofβ-barrel membrane proteins.
β-barrel membrane protein transmembrane topology Hidden Markov model jackknife procedure
Lingyun Zou Zhengzhi Wang
Bioinformatics Group, College of Mechanism&Automatization National University of Defence Technology Changsha, China
国际会议
武汉
英文
147-150
2007-07-06（万方平台首次上网日期，不代表论文的发表时间）