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Volume 30
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10.3390/molecules30030550
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics

by
Ricardo Alexandre Barroso
1,2,
Guillermin Agüero-Chapin
1,2,
Rita Sousa
1,2,
Yovani Marrero-Ponce
3,4 and
Agostinho Antunes
1,2,*
1
Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, 4450-208 Porto, Portugal
2
Department of Biology, Faculty of Sciences of University of Porto (FCUP), Rua do Campo Alegre s/n, 4169-007 Porto, Portugal
3
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de Mexico 03920, Mexico
4
Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador
*
Author to whom correspondence should be addressed.
Molecules 2025, 30(3), 550; https://doi.org/10.3390/molecules30030550
Submission received: 7 December 2024 / Revised: 20 January 2025 / Accepted: 21 January 2025 / Published: 25 January 2025
(This article belongs to the Special Issue Computational Approaches in Antimicrobial and Antiviral Drug Discovery: Deciphering Drug Binding and Molecular Mechanisms)
Versions Notes

Abstract

Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as corals, sea anemones, and jellyfish, are a promising valuable resource of these bioactive peptides due to their robust innate immune systems yet are still poorly explored. Hence, we employed an in silico proteolysis strategy to search for novel AMPs from omics data of 111 Cnidaria species. Millions of peptides were retrieved and screened using shallow- and deep-learning models, prioritizing AMPs with a reduced toxicity and with a structural distinctiveness from characterized AMPs. After complex network analysis, a final dataset of 3130 Cnidaria singular non-haemolytic and non-toxic AMPs were identified. Such unique AMPs were mined for their putative antibacterial activity, revealing 20 favourable candidates for in vitro testing against important ESKAPEE pathogens, offering potential new avenues for antibiotic development.
Keywords: Cnidaria; antimicrobial; omics; artificial intelligence; complex networks Cnidaria; antimicrobial; omics; artificial intelligence; complex networks

Share and Cite

MDPI and ACS Style

Barroso, R.A.; Agüero-Chapin, G.; Sousa, R.; Marrero-Ponce, Y.; Antunes, A. Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics. Molecules 2025, 30, 550. https://doi.org/10.3390/molecules30030550

AMA Style

Barroso RA, Agüero-Chapin G, Sousa R, Marrero-Ponce Y, Antunes A. Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics. Molecules. 2025; 30(3):550. https://doi.org/10.3390/molecules30030550

Chicago/Turabian Style

Barroso, Ricardo Alexandre, Guillermin Agüero-Chapin, Rita Sousa, Yovani Marrero-Ponce, and Agostinho Antunes. 2025. "Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics" Molecules 30, no. 3: 550. https://doi.org/10.3390/molecules30030550

APA Style

Barroso, R. A., Agüero-Chapin, G., Sousa, R., Marrero-Ponce, Y., & Antunes, A. (2025). Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics. Molecules, 30(3), 550. https://doi.org/10.3390/molecules30030550

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