Unraveling the hemolytic toxicity tapestry of peptides using chemical space complex networks

• DOI: 10.1093/toxsci/kfae115
• Publication Date: 2024-09-10T14:39:54Z
• Authors: Kevin Castillo-Me...; Guillermin Agüero...; José R Mora; Noel Pérez; Ernesto Contreras...; José R Valdes-Mar...; Felix Martinez-Rios; Yovani Marrero-Ponce
• Keywords: Chemical space

Abstract Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel tapestry peptides. CSNs are powerful tools visualizing analyzing relationships between based on physicochemical properties structural features. We constructed from StarPepDB database, encompassing 2,004 hemolytic peptides, explored impact seven different (dis)similarity measures network topology cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing motif discovery enrichment process. identified 12 consensus motifs, whose amino acid composition unveiled a high abundance lysine, leucine, valine residues, whereas aspartic acid, methionine, histidine, asparagine, glutamine were depleted. Additionally, used characterize clusters/communities To predict activity directly peptide sequences, multi-query similarity searching models, which outperformed cutting-edge machine learning-based demonstrating robust prediction capabilities. Overall, this novel in silico approach uses science its central strategy develop model classifiers, space, discover new motifs This will help enhance design/selection with low toxicity.