Prediction of cell-penetrating peptides with feature selection techniques.
Cell-penetrating peptides are a group of peptides which can transport different types of cargo molecules such as drugs across plasma membrane and have been applied in the treatment of various diseases. Thus, the accurate prediction of cell-penetrating peptides with bioinformatics methods will accelerate the development of drug delivery systems. The study aims to develop a powerful model to accurately identify cell-penetrating peptides. At first, the peptides were translated into a set of vectors with the same dimension by using dipeptide compositions. Secondly, the Analysis of Variance-based technique was used to reduce the dimension of the vector and explore the optimized features. Finally, the support vector machine was utilized to discriminate cell-penetrating peptides from non-cell-penetrating peptides. The five-fold cross-validated results showed that our proposed method could achieve an overall prediction accuracy of 83.6%. Based on the proposed model, we constructed a free webserver called C2Pred (http://lin.uestc.edu.cn/server/C2Pred).
Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China. Electronic address: tanghua771211@aliyun.com.
Hua
H
Tang
Key Laboratory for NeuroInformation 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.
Zhen-Dong
ZD
Su
Key Laboratory for NeuroInformation 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.
Huan-Huan
HH
Wei
Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China; Key Laboratory for NeuroInformation 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.
Wei
W
Chen
Key Laboratory for NeuroInformation 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. Electronic address: hlin@uestc.edu.cn.
Hao
H
Lin
D057846:Cell-Penetrating Peptides / Q000737:chemistry*; D030562:Databases, Protein; D020407:Internet; D060388:Support Vector Machine
D057846:Cell-Penetrating Peptides
Analysis of variance; Cell-penetrating peptides; Support vector machine; g-gap dipeptide composition