MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China.
Protein Sci. 2009 Dec;18(12):2550-8. doi: 10.1002/pro.257.
Quantitative prediction of protein-protein binding affinity is essential for understanding protein-protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein-protein binding affinity. The potential is obtained by statistically analyzing X-ray structures of protein-protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard-Jones potential. To evaluate the prediction ability for protein-protein binding affinity, six test sets are examined. Sets 1-5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1-5, with a total of 86 protein-protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1-5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.
定量预测蛋白质-蛋白质结合亲和力对于理解蛋白质-蛋白质相互作用至关重要。本文提出了一种考虑体积校正的原子水平平均力势能（PMF），用于预测蛋白质-蛋白质结合亲和力。该势能通过对蛋白质-蛋白质复合物在蛋白质数据库中的 X 射线结构进行统计分析得到。这种方法避免了体积校正过程中的复杂步骤，在实践中非常容易实现。与传统 PMF 相比，它可以获得更合理的对势能，并且表现出经典的非键原子对相互作用图像，如 Lennard-Jones 势能。为了评估对蛋白质-蛋白质结合亲和力的预测能力，我们检验了六个测试集。前五个测试集分别用于五项已发表研究中的测试集，第六个测试集是前五个测试集的并集，共有 86 个蛋白质-蛋白质复合物。通过计算拟合预测亲和力与实验数据的相关系数（R）和标准偏差（SD），将我们的表现与文献中的结果进行比较。我们对前五个测试集的预测结果与已发表研究中最好的预测结果相当，对于并集 6，R = 0.76，SD = 2.24 kcal/mol。此外，我们发现体积校正可以显著提高预测能力。这种方法还可以促进对接和蛋白质结构预测的研究。
