Discovery and Design of Antimicrobial Peptides (AMPs) for Potential Anti-Lung Cancer Therapy through Multi-Omics and Machine Learning
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I worked on selecting peptides with potential anti-cancer activity by analyzing key biochemical properties such as hydrophobicity, net charge, stability, half-life, and Boman index. These properties help determine a peptide’s ability to interact with cancer cell membranes, its stability in biological environments, and targeting specificity. Using correlation heat maps and PCA analysis, our team identified 28 bacterial peptides, 18 probiotic bacterial peptides, 6 fungal peptides, and 4 metagenomic peptides. My specific focus was on peptides from the soil bacterium Streptomyces parvus, contributing to the overall findings of potential anti-cancer candidates. YouTube Video from 47:41