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J Proteomics. 2012 Dec 21;77:321-8. doi: 10.1016/j.jprot.2012.09.006. Epub 2012 Sep 20.

Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions.

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Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.


Mycobacterium can cause many serious diseases, such as tuberculosis and leprosy. Its membrane proteins play a critical role for multidrug-resistance and its tenacious survival ability. Knowing the types of membrane proteins will provide novel insights into understanding their functions and facilitate drug target discovery. In this study, a novel method was developed for predicting mycobacterial membrane protein and their types by using over-represented tripeptides. A total of 295 non-membrane proteins and 274 membrane proteins were collected to evaluate the performance of proposed method. The results of jackknife cross-validation test show that our method achieves an overall accuracy of 93.0% in discriminating between mycobacterial membrane proteins and mycobacterial non-membrane proteins and an overall accuracy of 93.1% in classifying mycobacterial membrane protein types. By comparing with other methods, the proposed method showed excellent predictive performance. Based on the proposed method, we built a predictor, called MycoMemSVM, which is freely available at It is anticipated that MycoMemSVM will become a useful tool for the annotation of mycobacterial membrane proteins and the development of anti-mycobacterium drug design.

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