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A-CaMP: a tool for anti-cancer and antimicrobial peptide generation
Aman Chandra Kaushik1,2, Aamir Mehmood2, Shaoliang Peng3
1Wuxi School of Medicine, Jiangnan University, Wuxi, China.
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.
Insights
Researchers developed A-CaMP, a novel bioinformatics tool for identifying anti-cancer peptides (ACPs) and antimicrobial peptides (AMPs). This open-source tool accurately predicts potential ACPs and AMPs from protein sequences, aiding cancer and infection therapies.
Area of Science:
- Bioinformatics
- Peptide Science
- Computational Biology
Background:
- Anti-cancer peptides (ACPs) are crucial in cell signaling, while antimicrobial peptides (AMPs) provide immunity.
- Some peptides exhibit both anticancer and antimicrobial activities.
- Existing bioinformatics tools lack the capability to predict ACPs from wild and mutated protein sequences.
Purpose of the Study:
- To develop a novel tool, A-CaMP, for rapid fingerprinting and prediction of anti-cancer and antimicrobial peptides.
- To compare the performance of A-CaMP with existing online methods.
- To establish a foundation for applying A-CaMP in cancer and infectious disease therapies.
Main Methods:
- Development of the A-CaMP tool using PERL language.
- Scanning target protein sequences against curated datasets.
- Comparison of A-CaMP's performance and functionality against other available online tools.
Main Results:
- A-CaMP achieved a prediction accuracy of 93.4%, significantly outperforming existing tools.
- Sequence alignment studies confirmed A-CaMP's utility in identifying AMPs.
- A-CaMP is the first open-source tool utilizing clinical data for peptide prediction.
Conclusions:
- A-CaMP is a highly accurate and robust tool for identifying anti-cancer and antimicrobial peptides.
- The tool's ability to analyze wild and mutant sequences offers potential for therapeutic applications.
- A-CaMP represents a significant advancement in bioinformatics for drug discovery and development.
