Tsai Cheng-Ting, Lin Chia-Wei, Ye Gen-Lin, Wu Shao-Chi, Yao Philip, Lin Ching-Ting, Wan Lei, Tsai Hui-Hsu Gavin
Department of Chemistry, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan 32001, Taiwan.
Aurora High School, 109 W Pioneer Trail, Aurora, Ohio 44202, United States.
ACS Omega. 2024 Feb 13;9(8):9357-9374. doi: 10.1021/acsomega.3c08676. eCollection 2024 Feb 27.
The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.
多重耐药（MDR）病原体构成的威胁不断升级，这使得我们有必要从传统抗生素转向创新替代品，实现范式转变。抗菌肽（AMPs）在这一领域成为了极具竞争力的候选者。通过采用各种方法，我们能够开创AMPs发现的新时代，简化从大量候选序列中进行识别的过程，从而优化实验室筛选成本。在此，我们推出了前沿的机器学习（ML）模型，这些模型兼具预测性和可解释性，专为识别针对世界卫生组织（WHO）高优先级病原体的强效AMPs而量身定制。此外，我们还开发了考虑人类红细胞溶血情况的ML模型，突出了它们的治疗潜力。基于从AMPs的三维（3D）螺旋构象中获取的细微物理化学属性，我们优化后的模型展现出了令人称赞的性能——在针对低序列识别肽和最近公布的AMPs进行评估时，准确率超过75%。为证明其有效性，我们运用这些模型对源自PEM - 2的肽序列进行优先级排序，随后针对WHO的优先级病原体探究了我们算法预测肽的生物活性。有趣的是，其中一些新型AMPs在抗菌能力方面优于天然的PEM - 2，从而凸显了我们建模方法的稳健性。为阐明ML模型的结果，我们通过Shapley加性解释（SHAP）值进行探究，揭示了指导针对细菌采取不同行动的复杂机制。我们的先进预测模型加快了新型AMPs的设计，为抗生素耐药性提供了有力的应对措施。我们的预测工具可在https://ai - meta.chem.ncu.edu.tw/amp - meta上向公众开放使用。
