Abstract
Antimicrobial peptides (AMPs) are promising cationic and amphipathic molecules to fight antibiotic resistance. To search for novel AMPs, we applied a computational strategy to identify peptide sequences within the organisms' proteome, including in-house developed software and artificial intelligence tools. After analyzing 150.450 proteins from eight proteomes of bacteria, plants, a protist, and a nematode, nine peptides were selected and modified to increase their antimicrobial potential. The 18 resulting peptides were validated by bioassays with four pathogenic bacterial species, one yeast species, and two cancer cell-lines. Fourteen of the 18 tested peptides were antimicrobial, with minimum inhibitory concentrations (MICs) values under 10 µM against at least three bacterial species; seven were active against Candida albicans with MICs values under 10 µM; six had a therapeutic index above 20; two peptides were active against A549 cells, and eight were active against MCF-7 cells under 30 µM. This study's most active antimicrobial peptides damage the bacterial cell membrane, including grooves, dents, membrane wrinkling, cell destruction, and leakage of cytoplasmic material. The results confirm that the proposed approach, which uses bioinformatic tools and rational modifications, is highly efficient and allows the discovery, with high accuracy, of potent AMPs encrypted in proteins.
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Funding
This study was supported by Universidad Nacional de Colombia—Sede Medellin, the HERMES research grants numbers 42990 and 55740, and by the Instituto Tecnológico Metropolitano de Medellín (ITM) research grant number PE20202.
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SO, CM, and JB designed the study, worked on data analysis and interpretation, participated in the writing of the manuscript, and reviewed its final version. DM AM and LM participated in the sampling, data collection, laboratory essays, and analysis. They also collaborate on writing the draft of the manuscript. All authors read and approved the final manuscript.
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Monsalve, D., Mesa, A., Mira, L.M. et al. Antimicrobial peptides designed by computational analysis of proteomes. Antonie van Leeuwenhoek 117, 55 (2024). https://doi.org/10.1007/s10482-024-01946-0
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DOI: https://doi.org/10.1007/s10482-024-01946-0