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BioMed Research International
Volume 2016, Article ID 1654623, 8 pages
Research Article

Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition

1Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
3School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
4Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China

Received 24 April 2016; Accepted 30 May 2016

Academic Editor: Qin Ma

Copyright © 2016 Xin-Xin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Owing to the abuse of antibiotics, drug resistance of pathogenic bacteria becomes more and more serious. Therefore, it is interesting to develop a more reasonable way to solve this issue. Because they can destroy the bacterial cell structure and then kill the infectious bacterium, the bacterial cell wall lyases are suitable candidates of antibacteria sources. Thus, it is urgent to develop an accurate and efficient computational method to predict the lyases. Based on the consideration, in this paper, a set of objective and rigorous data was collected by searching through the Universal Protein Resource (the UniProt database), whereafter a feature selection technique based on the analysis of variance (ANOVA) was used to acquire optimal feature subset. Finally, the support vector machine (SVM) was used to perform prediction. The jackknife cross-validated results showed that the optimal average accuracy of 84.82% was achieved with the sensitivity of 76.47% and the specificity of 93.16%. For the convenience of other scholars, we built a free online server called Lypred. We believe that Lypred will become a practical tool for the research of cell wall lyases and development of antimicrobial agents.