Unveiling Putative Functions of Mucus Proteins and Their Tryptic Peptides in Seven Gastropod Species Using Comparative Proteomics and Machine Learning-Based Bioinformatics Predictions

Molecules. 2021 Jun 7;26(11):3475. doi: 10.3390/molecules26113475.

Abstract

Gastropods are among the most diverse animals. Gastropod mucus contains several glycoproteins and peptides that vary by species and habitat. Some bioactive peptides from gastropod mucus were identified only in a few species. Therefore, using biochemical, mass spectrometric, and bioinformatics approaches, this study aimed to comprehensively identify putative bioactive peptides from the mucus proteomes of seven commonly found or commercially valuable gastropods. The mucus was collected in triplicate samples, and the proteins were separated by 1D-SDS-PAGE before tryptic digestion and peptide identification by nano LC-MS/MS. The mucus peptides were subsequently compared with R scripts. A total of 2818 different peptides constituting 1634 proteins from the mucus samples were identified, and 1218 of these peptides (43%) were core peptides found in the mucus of all examined species. Clustering and correspondence analyses of 1600 variable peptides showed unique mucous peptide patterns for each species. The high-throughput k-nearest neighbor and random forest-based prediction programs were developed with more than 95% averaged accuracy and could identify 11 functional categories of putative bioactive peptides and 268 peptides (9.5%) with at least five to seven bioactive properties. Antihypertensive, drug-delivering, and antiparasitic peptides were predominant. These peptides provide an understanding of gastropod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications.

Keywords: bioactive peptides; gastropod; machine-learning prediction; mucus; proteomics.

MeSH terms

  • Animals
  • Chromatography, Liquid / methods
  • Computational Biology / methods
  • Electrophoresis, Polyacrylamide Gel / methods
  • Gastropoda / metabolism*
  • Machine Learning
  • Mucus / metabolism*
  • Peptides / metabolism*
  • Proteome / metabolism*
  • Proteomics / methods
  • Tandem Mass Spectrometry / methods

Substances

  • Peptides
  • Proteome