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 \(64\,\upmu \hbox {M}\). 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.

细菌物种中抗生素耐药性的增加，增加了寻找新型抗菌分子的需求。抗菌肽是通常表现出两亲特性的分子。在这项工作中，我们开发了一种计算策略，可在任何生物的蛋白质组中搜索新的肽序列，包括内部开发的软件和使用在线提供的人工智能工具。在分析了细菌，藻类和无脊椎动物蛋白质组中的63,343种蛋白质后，选择了11种肽。然后，我们通过针对五（5）种致病细菌物种和两（2）种癌细胞系进行的几种测定方法验证了结果。结果，我们发现其中十种肽具有抗菌性，最小抑菌浓度值在4到5之间。\（64 \，\ upmu \ hbox {M} \）。此外，两种活性更高的肽对人红细胞和癌细胞也具有细胞毒性。通常，我们发现抗菌肽对细菌细胞膜产生损害，包括膜起皱，细胞起泡和细胞质物质泄漏。基于这些结果，我们得出结论，为发现蛋白质中加密的序列而提议的计算方法适合于发现选择性和非选择性抗微生物和抗癌肽。