Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China.
Shenzhen Bay Laboratory, Shenzhen, 518055, China.
J Chem Inf Model. 2023 Jun 12;63(11):3230-3237. doi: 10.1021/acs.jcim.2c01499. Epub 2023 May 26.
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, , for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. is available for free at: https://affinity.cuhk.edu.cn/.
蛋白质-蛋白质结合亲和力反映了结合伴侣之间的结合强度。预测蛋白质-蛋白质结合亲和力对于阐明蛋白质功能以及设计基于蛋白质的治疗方法非常重要。蛋白质-蛋白质复合物结构中的几何特征，如面积（界面和表面面积），在确定蛋白质-蛋白质相互作用及其结合亲和力方面起着重要作用。在这里，我们为学术用途免费提供一个网络服务器，即，用于根据蛋白质-蛋白质复合物结构中的界面和表面面积预测蛋白质-蛋白质或抗体-蛋白质抗原结合亲和力。 实现了 60 个有效的基于面积的蛋白质-蛋白质亲和力预测模型和 37 个有效的基于面积的抗体-蛋白质抗原结合亲和力预测模型，这些模型是我们最近研究中开发的。这些模型通过使用具有不同生物物理性质的不同氨基酸类型分类的面积来考虑界面和表面面积在结合亲和力中的作用。性能最佳的模型集成了神经网络或随机森林等机器学习方法。与常用的现有方法相比，这些新开发的模型具有更好或相当的性能。 可在以下网址免费获取：https://affinity.cuhk.edu.cn/。
