Laederach Alain, Reilly Peter J
Department of Chemical Engineering, Iowa State University, 2114 Sweeney Hall, Ames, IA 50011, USA.
J Comput Chem. 2003 Nov 15;24(14):1748-57. doi: 10.1002/jcc.10288.
We present an automated docking protocol specifically optimized to predict the structure and affinity of a protein-carbohydrate complex. A scoring function was developed based on a training set of 30 protein-carbohydrate complexes of known structure and affinity. Combinations of several models for hydrogen bonding, torsional entropy loss, and solvation were tested for their ability to fit the training set data, and the best model was used with AutoDock. The electrostatic empirical coefficient is larger than in a previously obtained model using a training set comprised of various types of protein-ligand complexes, indicating that electrostatic interactions play a more important role in determining the affinity between a carbohydrate and a protein. The differences in the relative weighting of the empirical coefficients in the model yields predicted free energies for the training set with a standard error of 1.403 kcal/mol. The new scoring function was tested on 17 Aspergillus niger glucoamylase inhibitors for which binding energies had been determined experimentally. Free energies of complex formation were predicted with a residual standard error of 1.101 kcal/mol. The new scoring function therefore provides a robust method for predicting free energies of formation and optimal conformations of carbohydrate-protein complexes.
我们提出了一种专门优化的自动对接协议，用于预测蛋白质 - 碳水化合物复合物的结构和亲和力。基于一组由已知结构和亲和力的30种蛋白质 - 碳水化合物复合物组成的训练集开发了一种评分函数。测试了几种氢键、扭转熵损失和溶剂化模型的组合对训练集数据的拟合能力，并将最佳模型与AutoDock一起使用。静电经验系数比之前使用由各种类型蛋白质 - 配体复合物组成的训练集获得的模型中的系数更大，这表明静电相互作用在确定碳水化合物与蛋白质之间的亲和力方面起着更重要的作用。模型中经验系数相对权重的差异产生了训练集预测自由能，标准误差为1.403千卡/摩尔。新的评分函数在17种黑曲霉葡糖淀粉酶抑制剂上进行了测试，这些抑制剂的结合能已通过实验确定。复合物形成的自由能预测的残差标准误差为1.101千卡/摩尔。因此，新的评分函数为预测碳水化合物 - 蛋白质复合物形成的自由能和最佳构象提供了一种可靠的方法。
