MM/PBSA 和 MM/GBSA 对蛋白-配体自由能计算精度的评估研究
侯廷军*, 李有勇
苏州大学功能纳米与软物质研究院,苏州大学,苏州,215123
*Email: tjhou@suda.edu.cn
基于分子动力学模拟和连续介质模型的自由能计算方法受到了越来越多的关注,其中MM/PBSA和
MM/GBSA 就是其中最具代
表
关于同志近三年现实表现材料材料类招标技术评分表图表与交易pdf视力表打印pdf用图表说话 pdf
性的方法。MM/PBSA和MM/GBSA方法基于分子力学和连续介质模型方法来
计算两个分子间的结合自由能。研究首先采用MM/PBSA和MM/GBSA方法对包含6个蛋白的59个蛋白-配体
体系进行了结合自由能进行了预测。在计算中,对分子动力学模拟的时间、溶质介电常数以及熵效应的贡
献等影响自由能计算的重要因素进行了系统的评估。其次,比较了MM/GBSA和MM/PBSA对结合自由能的
预测能力;计算结果表明,MM/PBSA的预测结果和绝对结合自由能实验数值更为接近,但MM/GBSA则可
以对相对结合自由能给出更好的预测。此外,系统比较了MM/PBSA和MM/GBSA方法识别配体分子结合构
象的能力以及对于不同类蛋白-配体体系结合自由能的预测能力;计算结果表明MM/GBSA对于配体结合构
象以及不同类蛋白-配体体系相对结合能力的预测都表现出较好的性能。
关键词: MM/PBSA; MM/GBSA; 结合自由能;连续介质模型;分子对接
参考文献
[1] Hou, T.J.; Wang, J.M.; Li, Y.Y.; Wang, W. Journal of Chemical Information and Modeling, 2011, 51: 69.
[2]. Hou, T.J.; Wang, J.M.; Li, Y.Y.; Wang, W. Journal of Computational Chemistry, 2011, 32: 866.
[3] Wang, J.M.; Hou, T.J.; Xu, X.J. Current Computer-Aided Drug Design, 2006, 2: 287.
Assessing the performance of the MM/PBSA and MM/GBSA methods for
predicting protein-ligand binding free energies
Tingjun Hou*, Youyong Li,
Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou,
215123
The Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) and the Molecular
Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for
macromolecules by combining molecular mechanics calculations and continuum solvation models. We first
reported here an extensive study of 59 ligands interacting with six different proteins. The effects of the length of
molecular dynamics (MD) simulation, the solute dielectric constant, the contribution of conformational entropy
were evaluated. Next, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free
energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative,
binding free energies than MM/GBSA. In addition, we systematically investigated the performance of MM/PBSA
and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98
protein/ligand complexes. In summary, MM/GBSA performs well for both binding pose predictions and binding
free energy estimations and is efficient to re-score the top-hit poses produced by other less accurate scoring
functions.