Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks

• Published in: Toxicological sciences
• Main Authors: Castillo-Mendieta, Kevin, Agüero-Chapin, Guillermin, Mora, José R, Pérez, Noel, Contreras-Torres, Ernesto, Valdes-Martini, José R, Martinez-Rios, Felix, Marrero-Ponce, Yovani
• Format: Journal Article
• Language: English
• Published: United States 10.09.2024
• Subjects: Similarity searching model; Multiple sequence alignment; Hemolytic peptides; Motif discovery; Chemical space complex networks; Drug discovery; StarPep toolbox
• DOI: 10.1093/toxsci/kfae115
• ISSN: 1096-6080; 1096-0929

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.