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Unveiling putative functions of mucus proteins and their tryptic peptides in seven gastropod species using comparative proteomics and machine learning-based bioinformatics predictions
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Metadata
Document Title
Unveiling putative functions of mucus proteins and their tryptic peptides in seven gastropod species using comparative proteomics and machine learning-based bioinformatics predictions
Author
Tachapuripunya V., Roytrakul S., Chumnanpuen P., E-kobon T.
Name from Authors Collection
Affiliations
Department of Genetics, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand; Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand; Functional Ingredients and Food Innovation Research Group, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency, Pathum Thani, 12120, Thailand; Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
Type
Article
Source Title
Molecules
ISSN
14203049
Year
2021
Volume
26
Issue
11
Open Access
All Open Access, Gold, Green
Publisher
MDPI AG
DOI
10.3390/molecules26113475
Format
Abstract
Gastropods are among the most diverse animals. Gastropod mucus contains several gly-coproteins 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% aver-aged 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 gastro-pod mucus, and the putative bioactive peptides are expected to be experimentally validated for further medical, pharmaceutical, and cosmetic applications. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keyword
Bioactive peptides | Gastropod | Machine-learning prediction | Mucus | Proteomics
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
Funding Sponsor
National Center for Genetic Engineering and Biotechnology; Thailand Science Park
License
N/A
Rights
N/A
Publication Source
Scopus