Department of Life Sciences and Chemistry, Jacobs University Bremen , Campus Ring 1, 28759 Bremen, Germany.
Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie, University of Bonn , Beringstr. 4, D-53115 Bonn, Germany.
J Phys Chem B. 2017 Dec 14;121(49):11144-11162. doi: 10.1021/acs.jpcb.7b09175. Epub 2017 Dec 1.
The host-guest complexation of hydrocarbons (22 guest molecules) with cucurbit[7]uril was investigated in aqueous solution using the indicator displacement strategy. The binding constants (10-10 M) increased with guest size, pointing to the hydrophobic effect and dispersion interactions as driving forces. The measured affinities provide unique benchmark data for the binding of neutral guest molecules. Consequently, a computational blind challenge, the HYDROPHOBE challenge, was conducted to allow a comparison with state-of-the-art computational methods for predicting host-guest affinity constants. In total, three quantum-chemical (QM) data sets and two explicit-solvent molecular dynamics (MD) submissions were received. When searching for sources of uncertainty in predicting the host-guest affinities, the experimentally known hydration energies of the investigated hydrocarbons were used to test the employed solvation models (explicit solvent for MD and COSMO-RS for QM). Good correlations were obtained for both solvation models, but a rather constant offset was observed for the COSMO data, by ca. +2 kcal mol, which was traced back to a required reference-state correction in the QM submissions (2.38 kcal mol). Introduction of the reference-state correction improved the predictive power of the QM methods, particularly for small hydrocarbons up to C5.
采用指示剂置换策略,在水溶液中研究了烃类(22 种客体分子)与瓜环的主客体络合作用。结合常数(10-10 M)随客体分子尺寸的增加而增加,表明疏水效应和色散相互作用是驱动力。所测量的亲和力为中性客体分子的结合提供了独特的基准数据。因此,进行了计算盲挑战,即 HYDROPHOBE 挑战,以便与预测主客体亲和力常数的最先进计算方法进行比较。总共收到了三个量子化学(QM)数据集和两个显式溶剂分子动力学(MD)提交。在寻找预测主客体亲和力时不确定性的来源时,使用实验测定的所研究烃类的水合能来测试所采用的溶剂模型(MD 中的显式溶剂和 QM 中的 COSMO-RS)。两种溶剂模型都得到了很好的相关性,但 COSMO 数据的偏移量相当恒定,约为+2 kcal/mol,这可以追溯到 QM 提交中所需的参考状态校正(2.38 kcal/mol)。参考状态校正的引入提高了 QM 方法的预测能力,特别是对于直到 C5 的小烃类。