A-CaMP: a tool for anti-cancer and antimicrobial peptide generation

Abstract

Anti-cancer peptides (ACPs) play a vital role in the cell signaling process. Antimicrobial peptides (AMPs) provide immunity against pathogenic microbes, AMPs present activity against pathogenic microbes. Some of them are known to possess both anticancer and antimicrobial activity. However, so far, no tools have been developed that could predict potential ACPs from wild and mutated cancerous protein sequences in the numerous public databases. In the present study, we developed a A-CaMP tool that allows rapid fingerprinting of the anti-cancer and antimicrobial peptides, which play a crucial role in current bioinformatics research. Besides, we compared the performance and functionality of our A-CaMP tool with those of other methods available online. A-CaMP scans the target protein sequences provided by the user against the datasets. It possesses a robust coding architecture, has been developed in PERL language and is scalable of therefore has extensive applications in bioinformatics. It was observed to achieve a prediction accuracy of 93.4%, which is much higher than that of any of the existing tools. Sequence alignment studies also highlight the potential use of A-CaMP as a tool for the identification of AMPs. A-CaMP is the first open source tool that uses clinical data and proposes final peptides along with the necessary information; this includes wild and mutant sequence and peptides, which lays the foundation for its application in therapies for cancer and bacterial infections.

Communicated by Ramaswamy H. Sarma.

Keywords:

• Anti-cancer
• A-CaMP
• antimicrobial peptides
• machine learning
• artificial neural network

Disclosure statement

The authors declare that they have no competing interests.

Additional information

Funding

This work is supported by the grants from the Key Research Area Grant 2016YFA0501703, 2018ZX10302205-004-002, 2018ZX10302-205-004 of the Ministry of Science and Technology of China, the National Natural Science Foundation of China (Contract No. 61832019, 31871274, 61503244, BK20161130), the State Key Lab of Microbial Metabolism and Joint Research Funds for Medical and Engineering and Scientific Research at Shanghai Jiao Tong University (YG2017ZD14), and Chongqing Natural Science Foundation and Frontier Research Planning Project (No. cstc2018jcyjA2487). The Six Talent Peaks Project in Jiangsu Province (Grant No. SWYY-128), Major Project of Science and Technology in Henan Province (Grant No. 161100311400), The Technology Development Funding of Wuxi (Grant No. WX18IVJN017), and Research Funds for the Medical School of Jiangnan University ESI special cultivation project (Grant No. 1286010241170320). These funding sources have no role in the writing of the manuscript or the decision to submit it for publication.