Integrative Biomedical Informatics group, Research Programme on Biomedical Informatics, IMIM-Hospital del Mar, UPF, Barcelona, Spain.
Curr Pharm Des. 2012;18(30):4607-18. doi: 10.2174/138161212802651599.
Most processes in living organisms occur through an intricate network of protein-protein interactions, in which any malfunctioning can lead to pathological situations. Therefore, current research in biomedicine is starting to focus on protein interaction networks. A detailed structural knowledge of these interactions at molecular level will be necessary for drug discovery targeting protein-protein interactions. The challenge from a structural biology point of view is determining the structure of the specific complex formed upon interaction of two or several proteins, and/or locating the surface residues involved in the interaction and identify which of them are the most important ones for binding (hot-spots). In this line, an increasing number of computer tools are available to complement experimental efforts. Docking algorithms can achieve successful predictive rates in many complexes, as shown in the community assessment experiment CAPRI, and have already been applied to a variety of cases of biomedical interest. On the other side, many methods for interface and hotspot prediction have been reported, based on a variety of evolutionary, geometrical and physico-chemical parameters. Computer predictions are reaching a significant level of maturity, and can be very useful to guide experiments and suggest mutations, or to provide a mechanistic framework to the experimental results on a given interaction. We will review here existing computer approaches for proteinprotein docking, interface prediction and hot-spot identification, with focus to drug discovery targeting protein-protein interactions.
大多数生物体内的过程都是通过蛋白质-蛋白质相互作用的复杂网络进行的，其中任何功能失调都可能导致病理情况。因此，当前的生物医学研究开始专注于蛋白质相互作用网络。为了针对蛋白质-蛋白质相互作用进行药物发现，有必要在分子水平上详细了解这些相互作用的结构知识。从结构生物学的角度来看，挑战在于确定两个或多个蛋白质相互作用形成的特定复合物的结构，以及/或者定位参与相互作用的表面残基，并确定哪些是结合（热点）最重要的残基。在这方面，越来越多的计算机工具可用于补充实验工作。对接算法在许多复合物中都能达到成功的预测率，如 CAPRI 社区评估实验所示，并且已经应用于多种具有生物医学意义的情况。另一方面，已经报道了许多基于各种进化、几何和物理化学参数的界面和热点预测方法。计算机预测已经达到了相当成熟的水平，对于指导实验、建议突变，或者为特定相互作用的实验结果提供机制框架都非常有用。我们将在这里回顾现有的针对蛋白质-蛋白质对接、界面预测和热点识别的计算机方法，重点是针对蛋白质-蛋白质相互作用的药物发现。
