Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning-Reference-Cited by-同舟云学术

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Published:2024-05-13 Issue:10 Volume:64 Page:4310-4321
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Cordoves-Delgado Greneter¹,García-Jacas César R.²^ORCID

Affiliation:

1. Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México

2. Cátedras CONAHCYT − Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México

Funder

Consejo Nacional de Ciencia y Tecnolog?a

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c02061

Reference72 articles.

1. Increasing Antimicrobial Resistance Poses Global Threat, WHO Says

2. WHO Antimicrobial resistance. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance (Oct 10, 2023).

3. Bacterial resistance to antibacterial agents: Mechanisms, control strategies, and implications for global health

4. CDC About Antimicrobial Resistance. https://www.cdc.gov/drugresistance/about.html (Oct 10, 2023).

5. The immunology of host defence peptides: beyond antimicrobial activity

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