Hou Tingjun, Zhang Wei, Huang Qin, Xu Xiaojie
College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
J Mol Model. 2005 Feb;11(1):26-40. doi: 10.1007/s00894-004-0214-9. Epub 2004 Nov 24.
A new method is proposed for calculating aqueous solvation free energy based on atom-weighted solvent accessible surface areas. The method, SAWSA v2.0, gives the aqueous solvation free energy by summing the contributions of component atoms and a correction factor. We applied two different sets of atom typing rules and fitting processes for small organic molecules and proteins, respectively. For small organic molecules, the model classified the atoms in organic molecules into 65 basic types and additionally. For small organic molecules we proposed a correction factor of "hydrophobic carbon" to account for the aggregation of hydrocarbons and compounds with long hydrophobic aliphatic chains. The contributions for each atom type and correction factor were derived by multivariate regression analysis of 379 neutral molecules and 39 ions with known experimental aqueous solvation free energies. Based on the new atom typing rules, the correlation coefficient (r) for fitting the whole neutral organic molecules is 0.984, and the absolute mean error is 0.40 kcal mol(-1), which is much better than those of the model proposed by Wang et al. and the SAWSA model previously proposed by us. Furthermore, the SAWSA v2.0 model was compared with the simple atom-additive model based on the number of atom types (NA). The calculated results show that for small organic molecules, the predictions from the SAWSA v2.0 model are slightly better than those from the atom-additive model based on NA. However, for macromolecules such as proteins, due to the connection between their molecular conformation and their molecular surface area, the atom-additive model based on the number of atom types has little predictive power. In order to investigate the predictive power of our model, a systematic comparison was performed on seven solvation models including SAWSA v2.0, GB/SA_1, GB/SA_2, PB/SA_1, PB/SA_2, AM1/SM5.2R and SM5.0R. The results showed that for organic molecules the SAWSA v2.0 model is better than the other six solvation models. For proteins, the model classified the atoms into 20 basic types and the predicted aqueous free energies of solvation by PB/SA were used for fitting. The solvation model based on the new parameters was employed to predict the solvation free energies of 38 proteins. The predicted values from our model were in good agreement with those from the PB/SA model and were much better than those given by the other four models developed for proteins.
提出了一种基于原子加权溶剂可及表面积计算水合溶剂化自由能的新方法。该方法SAWSA v2.0通过对组成原子的贡献和一个校正因子求和来给出水合溶剂化自由能。我们分别对小分子有机化合物和蛋白质应用了两组不同的原子类型划分规则和拟合过程。对于小分子有机化合物，该模型将有机分子中的原子额外分类为65种基本类型。对于小分子有机化合物，我们提出了一个“疏水碳”校正因子，以考虑碳氢化合物和具有长疏水脂肪链的化合物的聚集。通过对379个中性分子和39个离子的已知实验水合溶剂化自由能进行多元回归分析，得出了每种原子类型和校正因子的贡献。基于新的原子类型划分规则，拟合整个中性有机分子的相关系数（r）为0.984，绝对平均误差为0.40 kcal mol⁻¹，这比Wang等人提出的模型以及我们之前提出的SAWSA模型要好得多。此外，将SAWSA v2.0模型与基于原子类型数量（NA）的简单原子加和模型进行了比较。计算结果表明，对于小分子有机化合物，SAWSA v2.0模型的预测略优于基于NA的原子加和模型。然而，对于蛋白质等大分子，由于其分子构象与其分子表面积之间的联系，基于原子类型数量的原子加和模型几乎没有预测能力。为了研究我们模型的预测能力，对包括SAWSA v2.0、GB/SA_1、GB/SA_2、PB/SA_1、PB/SA_2、AM1/SM5.2R和SM5.0R在内的七种溶剂化模型进行了系统比较。结果表明，对于有机分子，SAWSA v2.0模型优于其他六种溶剂化模型。对于蛋白质，该模型将原子分类为20种基本类型，并使用PB/SA预测的水合自由能进行拟合。基于新参数的溶剂化模型用于预测38种蛋白质的溶剂化自由能。我们模型的预测值与PB/SA模型的预测值吻合良好，并且比为蛋白质开发的其他四种模型给出的预测值要好得多。
