The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens
Open accessAbstract
Background: Antibiotic resistance is a growing global health concern prompting researchers to seek alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are attracting attention again as therapeutic agents with promising utility in this domain, and using in silico methods to discover novel AMPs is a strategy that is gaining interest. Such methods can sift through large volumes of candidate sequences and reduce lab screening costs. Results: Here we introduce AMPlify, an attentive deep learning model for AMP prediction, and demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome. We tested the bioactivity of our predicted peptides against a panel of bacterial species, including representatives from the World Health Organization’s priority pathogens list. Four of our novel AMPs were active against multiple species of bacteria, including a multi-drug resistant isolate of carbapenemase-producing Escherichia coli . Conclusions: We demonstrate the utility of deep learning based tools like AMPlify in our fight against antibiotic resistance. We expect such tools to play a significant role in discovering novel candidates of peptide-based alternatives to classical antibiotics.
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Cited by
Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36994-z · Cited by 231
Li, W., Huang, B., Guo, M., Zeng, Z., Cai, T., Feng, L., Zhang, X., Guo, L., Jiang, X., Yin, Y., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115773 · Cited by 13
Mining the UniProtKB /Swiss‐Prot database for antimicrobial peptides
Li, C., Sutherland, D., Salehi, A., Richter, A., Lin, D., Aninta, S. I., Ebrahimikondori, H., Yanai, A., Coombe, L., Warren, R. L., et al. (2025) · Protein Science · doi:10.1002/pro.70083 · Cited by 9
Self-citationThe Antimicrobial Peptide Pipeline: A Bacteria-Centric AMP Predictor
Veldsman, W. P., Yuanjie, Z., Zhang, Q., Zhao, Q., & Zhang, L. (2025) · Current Gene Therapy · doi:10.2174/0115665232343790250120071445 · Cited by 1
Metadata
- Authors
- Li, C., Sutherland, D., Hammond, S. A., Yang, C., Taho, F., Bergman, L., Houston, S., Warren, R. L., Wong, T., Hoang, L. M. N., Cameron, C. E., Helbing, C. C., & Birol, I.
- Venue
- BMC Genomics
- Published
- 2022-01-25
- Volume
- 23
- Issue
- 1
- Pages
- 77-77
- ISSN
- 1471-2164 (ISSN-L)
- OpenAlex
- W4226148785
- Cited by
- 221
- References
- 9
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- gold
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- Li, C., Sutherland, D., Hammond, S. A., Yang, C., Taho, F., Bergman, L., Houston, S., Warren, R. L., Wong, T., Hoang, L. M. N., Cameron, C. E., Helbing, C. C., & Birol, I. (2022). AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics. https://doi.org/10.1186/s12864-022-08310-4
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