Zhang Chi, Liu Song, Zhu Qianqian, Zhou Yaoqi
Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology & Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, New York 14214, USA.
J Med Chem. 2005 Apr 7;48(7):2325-35. doi: 10.1021/jm049314d.
We developed a knowledge-based statistical energy function for protein-ligand, protein-protein, and protein-DNA complexes by using 19 atom types and a distance-scale finite ideal-gas reference (DFIRE) state. The correlation coefficients between experimentally measured protein-ligand binding affinities and those predicted by the DFIRE energy function are around 0.63 for one training set and two testing sets. The energy function also makes highly accurate predictions of binding affinities of protein-protein and protein-DNA complexes. Correlation coefficients between theoretical and experimental results are 0.73 for 82 protein-protein (peptide) complexes and 0.83 for 45 protein-DNA complexes, despite the fact that the structures of protein-protein (peptide) and protein-DNA complexes were not used in training the energy function. The results of the DFIRE energy function on protein-ligand complexes are compared to the published results of 12 other scoring functions generated from either physical-based, knowledge-based, or empirical methods. They include AutoDock, X-Score, DrugScore, four scoring functions in Cerius 2 (LigScore, PLP, PMF, and LUDI), four scoring functions in SYBYL (F-Score, G-Score, D-Score, and ChemScore), and BLEEP. While the DFIRE energy function is only moderately successful in ranking native or near native conformations, it yields the strongest correlation between theoretical and experimental binding affinities of the testing sets and between rmsd values and energy scores of docking decoys in a benchmark of 100 protein-ligand complexes. The parameters and the program of the all-atom DFIRE energy function are freely available for academic users at http://theory.med.buffalo.edu.
我们通过使用19种原子类型和距离尺度有限理想气体参考（DFIRE）状态，为蛋白质-配体、蛋白质-蛋白质和蛋白质-DNA复合物开发了一种基于知识的统计能量函数。对于一个训练集和两个测试集，实验测量的蛋白质-配体结合亲和力与DFIRE能量函数预测的亲和力之间的相关系数约为0.63。该能量函数对蛋白质-蛋白质和蛋白质-DNA复合物的结合亲和力也能做出高度准确的预测。尽管蛋白质-蛋白质（肽）和蛋白质-DNA复合物的结构未用于训练能量函数，但对于82个蛋白质-蛋白质（肽）复合物，理论结果与实验结果之间的相关系数为0.73，对于45个蛋白质-DNA复合物，相关系数为0.83。将DFIRE能量函数对蛋白质-配体复合物的结果与其他12种基于物理、基于知识或经验方法生成的评分函数的已发表结果进行了比较。它们包括AutoDock、X-Score、DrugScore、Cerius 2中的四个评分函数（LigScore、PLP、PMF和LUDI）、SYBYL中的四个评分函数（F-Score、G-Score、D-Score和ChemScore）以及BLEEP。虽然DFIRE能量函数在对天然或接近天然构象进行排名时仅取得了一定程度的成功，但在100个蛋白质-配体复合物的基准测试中，它在测试集的理论和实验结合亲和力之间以及对接诱饵的均方根偏差值和能量得分之间产生了最强的相关性。全原子DFIRE能量函数的参数和程序可供学术用户在http://theory.med.buffalo.edu免费获取。
