Leeds Institute of Molecular Medicine, Section of Experimental Therapeutics, St James's University Hospital, University of Leeds, Leeds, LS9 7TF, UK.
Nucleic Acids Res. 2010 Apr;38(6):e86. doi: 10.1093/nar/gkp1158. Epub 2009 Dec 11.
Protein-protein interactions (PPIs) are ubiquitous in Biology, and thus offer an enormous potential for the discovery of novel therapeutics. Although protein interfaces are large and lack defining physiochemical traits, is well established that only a small portion of interface residues, the so-called hot spot residues, contribute the most to the binding energy of the protein complex. Moreover, recent successes in development of novel drugs aimed at disrupting PPIs rely on targeting such residues. Experimental methods for describing critical residues are lengthy and costly; therefore, there is a need for computational tools that can complement experimental efforts. Here, we describe a new computational approach to predict hot spot residues in protein interfaces. The method, called Presaging Critical Residues in Protein interfaces (PCRPi), depends on the integration of diverse metrics into a unique probabilistic measure by using Bayesian Networks. We have benchmarked our method using a large set of experimentally verified hot spot residues and on a blind prediction on the protein complex formed by HRAS protein and a single domain antibody. Under both scenarios, PCRPi delivered consistent and accurate predictions. Finally, PCRPi is able to handle cases where some of the input data is either missing or not reliable (e.g. evolutionary information).
蛋白质-蛋白质相互作用（PPIs）在生物学中普遍存在，因此为发现新的治疗方法提供了巨大的潜力。尽管蛋白质界面很大，缺乏明确的物理化学特征，但已经确定只有一小部分界面残基，即所谓的热点残基，对蛋白质复合物的结合能贡献最大。此外，最近在开发旨在破坏蛋白质相互作用的新型药物方面取得的成功依赖于针对这些残基。描述关键残基的实验方法既冗长又昂贵；因此，需要能够补充实验工作的计算工具。在这里，我们描述了一种预测蛋白质界面热点残基的新计算方法。该方法称为预测蛋白质界面关键残基（PCRPi），它依赖于通过贝叶斯网络将多种指标集成到独特的概率度量中。我们使用大量经过实验验证的热点残基对我们的方法进行了基准测试，并对 HRAS 蛋白和单个结构域抗体形成的蛋白质复合物进行了盲预测。在这两种情况下，PCRPi 都提供了一致且准确的预测。最后，PCRPi 能够处理输入数据部分缺失或不可靠的情况（例如进化信息）。
