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iACP: a sequence-based tool for identifying anticancer peptides
Wei Chen1,2, Hui Ding3, Pengmian Feng4
1Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, China.
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.
Insights
Identifying anticancer peptides (ACPs) is crucial for new cancer therapies. A new computational tool, iACP, accurately predicts ACPs using sequence data, offering a faster alternative to traditional methods.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Cancer is a leading global health concern, with conventional treatments causing significant side effects.
- Anticancer peptides (ACPs) offer a promising alternative therapeutic strategy.
- The rapid increase in peptide sequence data necessitates efficient computational identification of ACPs.
Purpose of the Study:
- To develop a robust computational method for identifying anticancer peptides (ACPs).
- To enhance the speed and accuracy of ACP discovery for potential cancer treatment applications.
Main Methods:
- Development of a sequence-based predictor named iACP.
- Optimization of the predictor using g-gap dipeptide components.
- Rigorous validation through cross-validation techniques.
Main Results:
- The iACP predictor demonstrated superior performance compared to existing methods.
- Achieved high accuracy and stability in identifying anticancer peptides.
- The predictor effectively utilizes sequence information for ACP identification.
Conclusions:
- iACP provides a reliable and efficient computational tool for identifying anticancer peptides.
- This method accelerates the discovery and application of ACPs in cancer therapy.
- A publicly accessible web server is available for researchers to utilize iACP.
