부산대학교 도서관

Protein–carbohydrate recognition is crucial in many vital biological processes including host–pathogen recognition, cell-signaling, and catalysis. Accordingly, computational prediction of protein–carbohydrate binding free energies is of enormous interest for drug design. However, the accuracy of current force fields (FFs) for predicting binding free energies of protein–carbohydrate complexes is not well understood owing to technical challenges such as the highly polar nature of the complexes, anomerization, and conformational flexibility of carbohydrates. The present study evaluated the performance of alchemical predictions of binding free energies with the GAFF1.7/AM1-BCC and GLYCAM06j force fields for modeling protein–carbohydrate complexes. Mean unsigned errors of 1.1 ± 0.06 (GLYCAM06j) and 2.6 ± 0.08 (GAFF1.7/AM1-BCC) kcal·mol–1are achieved for a large data set of monosaccharide ligands for Ralstonia solanacearumlectin (RSL). The level of accuracy provided by GLYCAM06j is sufficient to discriminate potent, moderate, and weak binders, a goal that has been difficult to achieve through other scoring approaches. Accordingly, the protocols presented here could find useful applications in carbohydrate-based drug and vaccine developments.