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Calculation of Host-Guest Binding Affinities Using a Quantum-Mechanical Energy Model

机译:主客体的计算结合亲和力使用量子力学能量模型

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摘要

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.
机译:蛋白质 - 配体结合亲和力的预测是对计算机辅助药物发现的核心兴趣,但仍然难以达到高精度。最近的研究表明可用力场可以是误差的关键来源,其激励了本研究,其基于量子机械能量模型而不是经验力场报告第一矿物最小值(M2)结合亲和计算。我们应用了半经验量子 - 机械能功能,PM6-DH +,与COSMO溶剂化模型相结合,达到29个具有宽范围测量的结合亲和力的宿主访客系统。在校正后,似乎从治疗极性溶剂化的治疗后,计算的绝对结合亲和力与实验测量相一致,平均误差1.6千卡/摩尔和0.91的相关系数。这些计算还描绘了各种能量分量的贡献,包括溶质能量,配置熵和溶剂化自由能,这些宿主客户复合物的粘合能量。与我们以前的计算使用经验力领域的计算比较,指向具有束缚自由能的能量和熵组分的显着差异。本研究证明了量子机械汉密尔顿中与M2亲和法的成功组合。

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  • 年(卷),期 -1(8),6
  • 年度 -1
  • 页码 2023–2033
  • 总页数 22
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