Shum Marcus Ho-Hin, Lee Yang, Tam Leighton, Xia Hui, Chung Oscar Lung-Wa, Guo Zhihong, Lam Tommy Tsan-Yuk
State Key Laboratory of Emerging Infectious Diseases, The University of Hong Kong, Hong Kong, China.
School of Public Health, The University of Hong Kong, Hong Kong, China.
Comput Struct Biotechnol J. 2024 Jan 17;23:759-770. doi: 10.1016/j.csbj.2024.01.009. eCollection 2024 Dec.
Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (K). modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.
冠状病毒(CoVs)因其能够感染多种动物物种并有可能在人类中出现,对全球公共卫生构成重大风险。冠状病毒刺突蛋白介导病毒进入细胞,并在决定与宿主细胞受体的结合亲和力方面发挥关键作用。特别强调感染人类和家畜的α-和β-冠状病毒,目前关于冠状病毒受体使用的研究表明,利用血管紧张素转换酶2(ACE2)受体对具有大流行潜力的病毒出现构成重大威胁。本综述总结了用于研究冠状病毒刺突蛋白与人类ACE2(hACE2)受体之间结合相互作用的方法。固相酶免疫测定和细胞结合测定可对结合进行定性评估,但缺乏对亲和力的定量评估。另一方面,表面等离子体共振、生物层干涉术和微量热泳动通过平衡解离常数(K)提供准确的亲和力测量。建模通过结合结构建模、蛋白质-蛋白质对接模拟和结合能计算预测亲和力,但由于缺乏标准化方法,结果不一致。机器学习和深度学习模型利用模拟和实验的蛋白质-蛋白质相互作用数据来阐明与冠状病毒与hACE2结合亲和力相关的关键残基。进一步优化和标准化现有的研究结合亲和力的方法有助于大流行防范。具体而言,优先监测能够与人类受体结合的冠状病毒,有望降低人畜共患病溢出的风险。