Liu Shiyong, Li Qingliang, Lai Luhua
State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
Proteins. 2006 Jul 1;64(1):68-78. doi: 10.1002/prot.20954.
With the large amount of protein-protein complex structural data available, to understand the key features governing the specificity of protein-protein recognition and to define a suitable scoring function for protein-protein interaction predictions, we have analyzed the protein interfaces from geometric and energetic points of view. Atom-based potential of mean force (PMFScore), packing density, contact size, and geometric complementarity are calculated for crystal contacts in 74 homodimers and 91 monomers, which include real biological interactions in dimers and nonbiological contacts in monomers and dimers. Simple cutoffs were developed for single and combinatorial parameters to distinguish biological and nonbiological contacts. The results show that PMFScore is a better discriminator between biological and nonbiological interfaces comparable in size. The combination of PMFScore and contact size is the most powerful pairwise discriminator. A combinatorial score (CFPScore) based on the four parameters was developed, which gives the success rate of the homodimer discrimination of 96.6% and error rate of the monomer discrimination of 6.0% and 19.8% according to Valdar's and our definition, respectively. Compared with other statistical learning models, the cutoffs for the four parameters and their combinations are directly based on physical models, simple, and can be easily applied to protein-protein interface analysis and docking studies.
鉴于现有大量蛋白质 - 蛋白质复合物结构数据，为了解决定蛋白质 - 蛋白质识别特异性的关键特征，并为蛋白质 - 蛋白质相互作用预测定义合适的评分函数，我们从几何和能量角度分析了蛋白质界面。针对74个同二聚体和91个单体中的晶体接触计算了基于原子的平均力势（PMFScore）、堆积密度、接触大小和几何互补性，其中包括二聚体中的真实生物相互作用以及单体和二聚体中的非生物接触。针对单个参数和组合参数制定了简单的截止值，以区分生物接触和非生物接触。结果表明，PMFScore在区分大小相当的生物和非生物界面方面是更好的判别指标。PMFScore和接触大小的组合是最强大的成对判别指标。基于这四个参数开发了一个组合评分（CFPScore），根据瓦尔达尔的定义和我们的定义，同二聚体判别成功率分别为96.6%，单体判别错误率分别为6.0%和19.8%。与其他统计学习模型相比，这四个参数及其组合的截止值直接基于物理模型，简单且可轻松应用于蛋白质 - 蛋白质界面分析和对接研究。
