Cysteine pattern barcoding-based dataset filtration enhances the machine learning-assisted interpretation of Conus venom peptide therapeutics
Rimsha Bibi, Noshaba Qasmi, Sajid Rashid
TL;DR
This study uses machine learning to analyze cone snail venom peptides and identify those with therapeutic potential by examining their cysteine patterns.
Contribution
A novel dataset filtration method using cysteine pattern barcoding improves machine learning predictions of venom peptide therapeutic potential.
Findings
Cysteine pattern barcodes were generated for 5,985 cone snail peptides across 82 species.
A Random Forest model achieved 90.48% accuracy in classifying peptides based on therapeutic potential.
Structural and binding pattern analysis revealed similarities between approved and novel peptides.
Abstract
Crude cone snail venom is a rich source of bioactive compounds with significant therapeutic potential. In this study, we conducted a comprehensive analysis of 5,985 cone snail peptides across 82 Conus species to identify unique cysteine (Cys) patterns and associated frameworks. The classification of these Cys patterns, based on conserved framework combinations, enabled the generation of species-level pattern barcodes. These barcodes were then evaluated to assess the species correlations of individual sequences. By analyzing 151 known Conus peptide PDB files, we computed Cys disulfide linkages to assess overall stability profiles. Incorporating barcode data allowed us to filter the dataset and prepare it for machine learning (ML) processing. Random Forest (RF) modeling, a supervised learning technique, was used to predict the therapeutic potential of venom peptides. Feature extraction…
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Taxonomy
TopicsNicotinic Acetylcholine Receptors Study · Antimicrobial Peptides and Activities · Chemical Synthesis and Analysis