Yu Hongwei

Prediction of the Enantioselectivity of Lipases and Esterases by Molecular Docking Method With Modified Force Field Parameters

In the work, molecular docking method was applied to extensively predict the enantioselectivity of lipases and esterases. A ligand library consisted of 69 chiral substrates was docked to four lipases and two esterases to set up the prediction model. During the docking process, necessary modification was carried out on van de Waals and hydrogen bond parameters of enzyme/substrate pair so that the ligands were able to adopt productive geometry in the enzymes. The docking results correctly indicated the enantiopreference for 91% (63/69) of docking pairs and the docking energy difference between substrate enantiomers (ΔΔGdocking) was significantly (correlation coefficient = 0.72, P < 0.05) correlated with the activation free energy difference (ΔΔG≠) that was quantitatively correlated with enantioselectivity of the enzymes. The prediction method was further validated by docking with another 12 enzyme/substrate pairs. Moreover, the prediction error was susceptible to the size of groups bonded to substrates chiral center and expected ΔΔG≠ values but was not related to the substrate type and reaction medium. The possible reasons of observed error were discussed. It is demonstrated that the docking method has great application potential in high performance prediction of enzyme enantioselectivity. Biotechnol. Bioeng. 2010. 105: 687–696.

Modeling of Transition State by Molecular Dynamics. Prediction of Catalytic Efficiency of the Mutants of Mandelate Racemase

Abstract Modeling of transition state by molecular dynamic method often requires modification of the force field parameters to describe energy profile accurately. In this work, we avoided the modification by modeling a series of mutants at binding-related site. In predicting the catalytic efficiency (k cat /K m ) of the mutants of mandelate racemase (MR), the prediction performance of three energy subsets was investigated. It was indicated that the interaction-energy subset exhibited better prediction performance than whole-system subset and binding-site subset in both quantity and trend. When prediction error (PE) criterion was equal to 5%, 10 out of 12 samples were predicted correctly within interaction-energy subset, which demonstrated a great application potential of this method in prediction of enzyme catalytic efficiency and enzyme rational design.