The ample peptide field is the best source for discovering clinically available novel antimicrobial peptides (AMPs) to address emerging drug resistance. However, discovering novel AMPs is complex and expensive, representing a major challenge. Recent advances in artificial intelligence (AI) have significantly improved the efficiency of identifying antimicrobial peptides from large libraries, whereas using random peptides as negative data increases the difficulty of discovering antimicrobial peptides from random peptides using discriminative models. In this study, we constructed three multi-discriminator models using deep learning and successfully screened twelve AMPs from a library of 30,000 random peptides. three candidate peptides (P2, P11, and P12) were screened by antimicrobial experiments, and further experiments showed that they not only possessed excellent antimicrobial activity but also had extremely low hemolytic activity. Mechanistic studies showed that these peptides exerted their bactericidal effects through membrane disruption, thus reducing the possibility of bacterial resistance. Notably, peptide 12 (P12) showed significant efficacy in a mouse model of wound infection with low toxicity to major organs at the highest tested dose (400 mg/kg). These results suggest deep learning-based multi-discriminator models can identify AMPs from random peptides with potential clinical applications.

中文翻译：

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通过基于深度学习的模型和生物活性验证从随机肽中发现 AMP


丰富的肽领域是发现临床可用的新型抗菌肽（AMP）以解决新出现的耐药性的最佳来源。然而，发现新型 AMP 既复杂又昂贵，是一项重大挑战。人工智能（AI）的最新进展显着提高了从大型库中识别抗菌肽的效率，而使用随机肽作为负数据增加了使用判别模型从随机肽中发现抗菌肽的难度。在这项研究中，我们利用深度学习构建了三个多鉴别器模型，并成功从 30,000 个随机肽库中筛选出 12 个 AMP。通过抗菌实验筛选出三种候选肽（P2、P11和P12），进一步实验表明它们不仅具有优异的抗菌活性，而且具有极低的溶血活性。机理研究表明，这些肽通过破坏细胞膜发挥杀菌作用，从而降低细菌产生耐药性的可能性。值得注意的是，肽 12 (P12) 在最高测试剂量 (400 mg/kg) 下对小鼠伤口感染模型显示出显着疗效，且对主要器官的毒性较低。这些结果表明，基于深度学习的多鉴别器模型可以从具有潜在临床应用的随机肽中识别 AMP。