Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei 115, Taiwan.
Biology (Basel). 2015 Mar 24;4(2):282-97. doi: 10.3390/biology4020282.
Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.
蛋白质-蛋白质对接（PPD）预测通常依赖于使用评分函数来对通过穷举采样生成的对接模型进行排名。为了将好的模型排名高于差的模型，已经开发和评估了大量的评分函数，但用于 PPD 预测计算的方法仍然存在很大的不足。在这里，我们报告了一种基于网络的 PPD 评分函数，即 NPPD，其中网络由两种类型的网络节点组成，一种用于疏水性氨基酸残基，另一种用于亲水性氨基酸残基，当它们所代表的残基处于一定的接触距离内时，节点就会相互连接。我们表明，计算二联体相互作用的网络参数和计算构建的氨基酸网络的异亲相互作用的网络参数使得 NPPD 在对 115 种 PPD 评分函数的基准评估中表现良好，其中大多数评分函数与 NPPD 不同，它们基于某种蛋白质-蛋白质相互作用能。我们还表明，NPPD 与这些基于能量的评分函数高度互补，这表明联合使用传统的评分函数和 NPPD 可能会显著提高当前 PPD 预测的准确性。
