Virtual screening using molecular simulations.
Virtual screening using molecular simulations.
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DOI：
10.1002/prot.23018
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发表时间：
2011-06
影响因子：
2.9
通讯作者：
Ren, Pengyu
中科院分区：
文献类型：
--
作者：
Yang, Tianyi;Wu, Johnny C.;Yan, Chunli;Wang, Yuanfeng;Luo, Ray;Gonzales, Michael B.;Dalby, Kevin N.;Ren, Pengyu
关键词：
有效的虚拟筛选依赖于我们对蛋白质-配体结合做出准确预测的能力，这仍然是一个巨大的挑战。在这项工作中，我们利用分子力学的泊松-玻尔兹曼(或广义Born)表面积方法，评估了一组15 6个配体与7个蛋白质家族的结合亲和力，这些蛋白质包括胰酶β、凝血酶α、细胞周期蛋白依赖激酶、cAMP依赖激酶、尿激酶型纤溶酶原激活剂、β-葡萄糖苷酶A和凝血因子Xa。结果表明，隐式溶剂模型中蛋白质的介电常数对结合自由能的计算有重要影响。用隐式溶剂方法计算的结合能与实验自由能之间的统计关联度在0.56~0.79之间。这一性能优于典型的对接程序，特别是考虑到后者是使用已知的结合数据直接训练的，而分子力学是基于一般物理参数的。对熵贡献的估计仍然是精确计算自由能的障碍。结果表明，传统的刚性转子谐振子近似不能改进束缚自由能的预测。包括构象限制似乎是有希望的，但需要进一步的研究。另一方面，我们的初步研究表明，基于隐含溶剂的炼金术微扰提供了组态熵的显式采样，可能是显著提高结合自由能预测的可行方法。总体而言，分子力学方法具有中到高通量计算药物发现的潜力。
Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) Surface Area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range 0.56~0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data while the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.
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影响因子：
4.1
作者：
BENNETT, CH
通讯作者：
BENNETT, CH
影响因子：
2.8
作者：
BEUTLER, TC;MARK, AE;VANGUNSTEREN, WF
通讯作者：
VANGUNSTEREN, WF
影响因子：
6.1
作者：
CONNOLLY, ML
通讯作者：
CONNOLLY, ML
影响因子：
15
作者：
Colizzi, Francesco;Perozzo, Remo;Cavalli, Andrea
通讯作者：
Cavalli, Andrea
影响因子：
2.9
作者：
Bas, Delphine C.;Rogers, David M.;Jensen, Jan H.
通讯作者：
Jensen, Jan H.