Research Information System
The 17th International Symposium on Health Informatics and Bioinformatics, İstanbul, Turkey, 18 - 20 December 2024, pp.31, (Summary Text)
Since their first discovery, peptides that specifically target and impose cytotoxicity upon cancer cells have provided a promising field of research for therapeutic applications against cancer. The goal to discover these anticancer peptides presents novel research opportunities, and the number of studies on these peptides has been growing at an accelerating pace. Shotgun metagenomics datasets hold great potential as extensive sources for the discovery of anticancer peptides. As these datasets withhold great amounts of data, in silico detection tools are required for fast, efficient, and accurate anticancer peptide detection. Here, we present the MetaPepticon, a bioinformatics pipeline that accepts the raw shotgun metagenomic data as input and outputs anticancer peptide candidates. MetaPepticon utilizes a consensus approach that incorporates multiple anti-cancer peptide prediction tools with distinct algorithms and methodologies, thereby identifying overlapping anticancer peptide candidates with the highest prediction scores across multiple tools. Furthermore, it checks the overall highest scored sequences for potential toxicity and scores any potentially toxic peptides. Ultimately, MetaPepticon provides a selection of peptides that exhibit the highest prediction scores alongside the lowest toxicity scores, providing researchers with optimized candidates for further investigation. Moreover, MetaPepticon provides user with the flexibility to select parameters, such as the number of overlapping predictions or best scored peptides across multiple tools. MetaPepticon provides a reproducible, flexible, end-to-end standardized workflow for the prediction of anticancer peptide candidates from shotgun metagenomics datasets. By integrating a consensus approach, it enhances prediction consistency and enables the discovery of novel anti-cancer peptide candidates from a variety of environmental microbiome samples.