Guan Changge, Torres Marcelo D T, Li Sufen, de la Fuente-Nunez Cesar
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nat Commun. 2025 Jul 12;16(1):6446. doi: 10.1038/s41467-025-60051-6.
The rise of antibiotic-resistant pathogens, particularly gram-negative bacteria, highlights the urgent need for novel therapeutics. Drug-resistant infections now contribute to approximately 5 million deaths annually, yet traditional antibiotic discovery has significantly stagnated. Venoms form an immense and largely untapped reservoir of bioactive molecules with antimicrobial potential. In this study, we mined global venomics datasets to identify new antimicrobial candidates. Using deep learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides. From these, we identified 386 candidates that are structurally and functionally distinct from known antimicrobial peptides. They display high net charge and elevated hydrophobicity, characteristics conducive to bacterial-membrane disruption. Structural studies revealed that many of these peptides adopt flexible conformations that transition to α-helical conformations in membrane-mimicking environments, supporting their antimicrobial potential. Of the 58 peptides selected for experimental validation, 53 display potent antimicrobial activity. Mechanistic assays indicated that they primarily exert their effects through bacterial-membrane depolarization, mirroring AMP-like mechanisms. In a murine model of Acinetobacter baumannii infection, lead peptides significantly reduced bacterial burden without observable toxicity. Our findings demonstrate that venoms are a rich source of previously hidden antimicrobial scaffolds, and that integrating large-scale computational mining with experimental validation can accelerate the discovery of urgently needed antibiotics.
抗生素耐药性病原体的出现，尤其是革兰氏阴性菌，凸显了对新型治疗方法的迫切需求。耐药性感染目前每年导致约500万人死亡，然而传统的抗生素发现已显著停滞。毒液构成了一个巨大且基本上未被开发的具有抗菌潜力的生物活性分子库。在本研究中，我们挖掘了全球毒液组学数据集以识别新的抗菌候选物。利用深度学习，我们探索了16123种毒液蛋白，生成了40626260种毒液加密肽。从中，我们鉴定出386种在结构和功能上与已知抗菌肽不同的候选物。它们表现出高净电荷和增加的疏水性，这些特性有利于破坏细菌膜。结构研究表明，这些肽中的许多呈现出灵活的构象，在模拟膜的环境中转变为α-螺旋构象，支持了它们的抗菌潜力。在选择用于实验验证的58种肽中，53种表现出强大的抗菌活性。机制分析表明，它们主要通过细菌膜去极化发挥作用，类似于抗菌肽的作用机制。在鲍曼不动杆菌感染的小鼠模型中，先导肽显著降低了细菌载量且未观察到毒性。我们的研究结果表明，毒液是以前隐藏的抗菌支架的丰富来源，并且将大规模计算挖掘与实验验证相结合可以加速急需抗生素的发现。
