Specific empirical free energy function for automated docking of carbohydrates to proteins.

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.