Design of antimicrobial and cytolytic peptides by computational analysis of bacterial, algal, and invertebrate proteomes

• Published in: Amino acids 2020-10, Vol.52 (10), p.1403-1412
• Main Authors: Duque-Salazar, Geraldine, Mendez-Otalvaro, Edward, Ceballos-Arroyo, Alberto M., Orduz, Sergio
• Format: Article
• Language: English
• Subjects: Algae; Analytical Chemistry; Antibiotic resistance; Antibiotics; Antiinfectives and antibacterials; Antimicrobial agents; Antimicrobial peptides; Artificial intelligence; Bacteria; Biochemical Engineering; Biochemistry; Biomedical and Life Sciences; Cell membranes; Computer applications; Cytotoxicity; Erythrocytes; Invertebrates; Life Sciences; Minimum inhibitory concentration; Neurobiology; Original Article; Peptides; Proteins; Proteomes; Proteomics; Tumor cell lines
• ISSN: 0939-4451; 1438-2199
• DOI: 10.1007/s00726-020-02900-w

The increase of antibiotic resistance in bacterial species has raised the need to search for novel antimicrobial molecules. Antimicrobial peptides are molecules that commonly display an amphipathic character. In this work, we developed a computational strategy to search for new peptide sequences within the proteome of any organism that includes in-house developed software and the use of artificial intelligence tools available online. Eleven peptides were selected after analyzing 63,343 proteins from the proteomes of bacteria, algae and invertebrates. Then, we validated the results by means of several assays which were carried out against five (5) pathogenic bacterial species and two (2) cancer cell lines. As a result, we found that ten of the peptides were antimicrobial, with minimum inhibitory concentration values between 4 and 64 μ M . Furthermore, two of the more active peptides were also cytotoxic to human red blood cells and cancer cells. In general, the antimicrobial peptides we discovered produced damage on the bacterial cell membrane that included membrane wrinkling, cell blebbing, and leakage of cytoplasmic material. Based on these results, we concluded that the computational approach proposed for finding sequences encrypted in proteins is appropriate for the discovery of selective and non-selective antimicrobial and anticancer peptides.