Department of Chemistry , Indiana University , Bloomington , Indiana 47405 , United States.
J Chem Theory Comput. 2019 Nov 12;15(11):6025-6035. doi: 10.1021/acs.jctc.9b00606. Epub 2019 Oct 9.
Despite the numerous computational efforts on estimating acid dissociation constant (p's), an accurate estimation of p's of bio-organic molecules in the aqueous medium is still a challenge. The major difficulty lies in the accurate description of the aqueous environment and the cost and accuracy of quantum mechanical (QM) methods. Herein, we report a well-defined quantum chemical protocol for accurately calculating p's of a wide range of bio-organic molecules in aqueous media. The performance of our method has been assessed using test sets containing molecules with a range of sizes and a variety of functional groups, including alcohols, phenols, amines, and carboxylic acids, and obtained an impressive mean absolute accuracy of 0.5 p units. For the smaller molecules, we use a high-level QM method (CBS-QB3) and a calibrated explicit-implicit solvation model that yields accurate p values for a range of functional groups. For the larger molecules, we combine this approach with an efficient error-cancellation scheme that eliminates the systematic errors in different density functional methods to yield accurate p values for simple to complex molecular systems. Our protocol is efficient, applicable to large molecules, covers all the common functional groups present in bio-organic molecules, and should find widespread applications in diverse research areas including drug-protein binding, catalysis, and chemical synthesis.
尽管在估计酸离解常数 (pKa) 方面已经进行了大量的计算工作,但在水相介质中准确估计生物有机分子的 pKa 仍然是一个挑战。主要的困难在于准确描述水相环境以及量子力学 (QM) 方法的成本和准确性。在此,我们报告了一种明确定义的量子化学方案,用于准确计算水相介质中广泛的生物有机分子的 pKa。我们的方法的性能已通过包含具有不同大小和多种官能团的分子的测试集进行评估,包括醇、酚、胺和羧酸,获得了令人印象深刻的 0.5 p 单位的平均绝对精度。对于较小的分子,我们使用高精度 QM 方法 (CBS-QB3) 和经过校准的显式-隐式溶剂化模型,为各种官能团生成准确的 p 值。对于较大的分子,我们将这种方法与一种有效的误差消除方案相结合,该方案消除了不同密度泛函方法中的系统误差,从而为简单到复杂的分子系统生成准确的 p 值。我们的方案高效、适用于大分子、涵盖生物有机分子中存在的所有常见官能团,并且应该在包括药物-蛋白质结合、催化和化学合成在内的各个研究领域得到广泛应用。