Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.

Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides generated by artificial intelligence (AI) strategies are potential alternatives that reduce costs and development time. This work optimized a set of peptides generated by two deep learning algorithms. The modifications made to the peptides were evaluated with bioinformatic and other AI tools as predictors of antimicrobial activity, hemolytic capacity, and toxicity. As a result, 26 synthetic peptides generated in silico were obtained with a high probability of being antimicrobial and biologically safe. Finally, 12 peptides were synthesized to perform in vitro tests against four bacterial species, Candida albicans, and cancer cells. Results indicate that 9 of the peptides have a MIC below 10 μM, and some have an inhibitory concentration at 2 μM, such as OrP1M for Escherichia coli, OrP9M for Pseudomonas aeruginosa, and VeP1 for Staphylococcus aureus. In addition, six peptides have activity against the breast cancer cell line (MCF-7), and peptide OrP1M had an IC of < 6.25 μM. It is concluded that the synthetic-generated peptides have high antimicrobial activity, but in most cases, their MICs were improved after the modifications were made.