Departments of Systems and Computational Biology, and Biochemistry, Albert Einstein College of Medicine, Bronx, New York, USA.
Protein Sci. 2024 Jun;33(6):e5026. doi: 10.1002/pro.5026.
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.
许多生物医学应用,如结合特异性的分类或生物工程,都依赖于蛋白质结合界面的准确定义。根据所使用方法的不同,可以将大量不同的残基分类为属于蛋白质界面的一部分。验证这些定义的一种典型方法是突变残基并测量这些变化对结合的影响。除了缺乏详尽的数据之外,这种方法还存在一个根本问题,即突变会在界面中引入未知量的改变,这可能会改变界面的结合特性。在这项研究中,我们使用药效基团方法探索了替代结合位点定义对蛋白质识别其同源配体能力的影响,该方法不会影响界面。研究还表明,蛋白质结合界面预测方法的性能应该高于大约 F 分数=0.7 的准确度水平,以捕捉蛋白质的生物学功能。