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, United States of America.
Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
bioRxiv. 2024 Dec 20:2024.12.17.628923. doi: 10.1101/2024.12.17.628923.
The relentless emergence of antibiotic-resistant pathogens, particularly Gram-negative bacteria, highlights the urgent need for novel therapeutic interventions. Drug-resistant infections account for approximately 5 million deaths annually, yet the antibiotic development pipeline has largely stagnated. Venoms, representing a remarkably diverse reservoir of bioactive molecules, remain an underexploited source of potential antimicrobials. Venom-derived peptides, in particular, hold promise for antibiotic discovery due to their evolutionary diversity and unique pharmacological profiles. In this study, we mined comprehensive global venomics datasets to identify new antimicrobial candidates. Using machine learning, we explored 16,123 venom proteins, generating 40,626,260 venom-encrypted peptides (VEPs). Using APEX, a deep learning model combining a peptide-sequence encoder with neural networks for antimicrobial activity prediction, we identified 386 VEPs structurally and functionally distinct from known antimicrobial peptides. Our analyses showed that these VEPs possess high net charge and elevated hydrophobicity, characteristics conducive to bacterial membrane disruption. Structural studies revealed considerable conformational flexibility, with many VEPs transitioning to α-helical conformations in membrane-mimicking environments, indicative of their antimicrobial potential. Of the 58 VEPs selected for experimental validation, 53 displayed potent antimicrobial activity. Mechanistic assays indicated that VEPs primarily exert their effects through bacterial membrane depolarization, mirroring AMP-like mechanisms. studies using a mouse model of infection demonstrated that lead VEPs significantly reduced bacterial burdens without notable toxicity. This study highlights the value of venoms as a resource for new antibiotics. By integrating computational approaches and experimental validation, venom-derived peptides emerge as promising candidates to combat the global challenge of antibiotic resistance.
抗生素耐药性病原体，尤其是革兰氏阴性菌的不断出现，凸显了对新型治疗干预措施的迫切需求。耐药性感染每年导致约500万人死亡，但抗生素研发管道在很大程度上已经停滞不前。毒液作为生物活性分子的一个极为多样的宝库，仍然是一种未得到充分开发的潜在抗菌剂来源。特别是毒液衍生肽，由于其进化多样性和独特的药理学特性，在抗生素发现方面具有潜力。在本研究中，我们挖掘了全面的全球毒液组学数据集，以确定新的抗菌候选物。我们使用机器学习方法，对16123种毒液蛋白进行了探索，生成了40626260个毒液加密肽（VEP）。我们使用APEX（一种将肽序列编码器与神经网络相结合用于抗菌活性预测的深度学习模型），鉴定出386个在结构和功能上与已知抗菌肽不同的VEP。我们的分析表明，这些VEP具有高净电荷和高疏水性，这有利于破坏细菌膜。结构研究揭示了相当大的构象灵活性，许多VEP在模拟膜的环境中转变为α-螺旋构象，这表明它们具有抗菌潜力。在选择用于实验验证的58个VEP中，有53个显示出强大的抗菌活性。机制分析表明，VEP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。使用感染小鼠模型的研究表明，先导VEP能显著降低细菌载量，且无明显毒性。这项研究突出了毒液作为新抗生素资源的价值。通过整合计算方法和实验验证，毒液衍生肽成为应对抗生素耐药性这一全球挑战的有希望的候选物。
