iACP: a sequence-based tool for identifying anticancer peptides

TLDR: A sequence-based predictor called iACP is reported, developed by the approach of optimizing the g-gap dipeptide components, that remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability.

Abstract: // Wei Chen 1, 4 , Hui Ding 2 , Pengmian Feng 3 , Hao Lin 2, 4 , Kuo-Chen Chou 4, 5 1 Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China 2 Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China 3 School of Public Health, North China University of Science and Technology, Tangshan, China 4 Gordon Life Science Institute, Belmont, Massachusetts, United States of America 5 Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia Correspondence to: Wei Chen, e-mail: wchen@gordonlifescience.org , chenweiimu@gmail.com Hao Lin, e-mail: hlin@uestc.edu.cn Kuo-Chen Chou, e-mail: kcchou@gordonlifescience.org Keywords: anticancer peptides, PseAAC, g-gap dipeptide mode, incremental feature selection, iACP webserver Received: January 06, 2016      Accepted: February 11, 2016      Published: March 01, 2016 ABSTRACT Cancer remains a major killer worldwide. Traditional methods of cancer treatment are expensive and have some deleterious side effects on normal cells. Fortunately, the discovery of anticancer peptides (ACPs) has paved a new way for cancer treatment. With the explosive growth of peptide sequences generated in the post genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying ACPs, so as to speed up their application in treating cancer. Here we report a sequence-based predictor called iACP developed by the approach of optimizing the g-gap dipeptide components. It was demonstrated by rigorous cross-validations that the new predictor remarkably outperformed the existing predictors for the same purpose in both overall accuracy and stability. For the convenience of most experimental scientists, a publicly accessible web-server for iACP has been established at http://lin.uestc.edu.cn/server/iACP , by which users can easily obtain their desired results.