Muddana Hari S, Gilson Michael K
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093-0736.
J Chem Theory Comput. 2012 Jun 12;8(6):2023-2033. doi: 10.1021/ct3002738. Epub 2012 May 11.
The prediction of protein-ligand binding affinities is of central interest in computer-aided drug discovery, but it is still difficult to achieve a high degree of accuracy. Recent studies suggesting that available force fields may be a key source of error motivate the present study, which reports the first mining minima (M2) binding affinity calculations based on a quantum mechanical energy model, rather than an empirical force field. We apply a semi-empirical quantum-mechanical energy function, PM6-DH+, coupled with the COSMO solvation model, to 29 host-guest systems with a wide range of measured binding affinities. After correction for a systematic error, which appears to derive from the treatment of polar solvation, the computed absolute binding affinities agree well with experimental measurements, with a mean error 1.6 kcal/mol and a correlation coefficient of 0.91. These calculations also delineate the contributions of various energy components, including solute energy, configurational entropy, and solvation free energy, to the binding free energies of these host-guest complexes. Comparison with our previous calculations, which used empirical force fields, point to significant differences in both the energetic and entropic components of the binding free energy. The present study demonstrates successful combination of a quantum mechanical Hamiltonian with the M2 affinity method.
蛋白质-配体结合亲和力的预测是计算机辅助药物发现的核心关注点，但仍难以实现高度准确。近期研究表明，现有的力场可能是误差的一个关键来源，这促使了本研究的开展。本研究报告了首个基于量子力学能量模型而非经验力场的挖掘极小值（M2）结合亲和力计算。我们将半经验量子力学能量函数PM6-DH+与COSMO溶剂化模型相结合，应用于29个具有广泛测量结合亲和力的主客体系统。在对似乎源于极性溶剂化处理的系统误差进行校正后，计算得到的绝对结合亲和力与实验测量值吻合良好，平均误差为1.6千卡/摩尔，相关系数为0.91。这些计算还描绘了各种能量成分，包括溶质能量、构型熵和溶剂化自由能，对这些主客体复合物结合自由能的贡献。与我们之前使用经验力场的计算结果相比，结合自由能的能量和熵成分存在显著差异。本研究证明了量子力学哈密顿量与M2亲和力方法的成功结合。
