This proposal aims at further development in the QM/MM methodology and its application to investigate the mechanism of chemical reactions in important enzymes and to understand the role of protein conformation dynamics in protein functions. The long-term goal is to develop and establish the QM/MM simulation as an equal partner with experiments for the study of structure and chemical reactions in enzymes and to provide insight into chemical reaction mechanisms in biological systems. This project has the following specific aims:
Aim 1. The reaction path potential (RPP) is an analytical energy expression of the combined quantum mechanical and molecular mechanical (QM/MM) potential energy along the minimum energy path. We plan to develop the RPP method into a practical tool for capturing the dynamic interaction and interplay between the chemical process and the protein conformation changes for enzyme reaction simulation.
Aim 2. We will investigate the amino acid activation process catalyzed by tryptophanyl-tRNA synthetase (TrpRS) and address the following questions: Is the catalytic mechanism associative or dissociative? What are the changes of energetics and dynamics correlated to the conformational changes observed in multiple X-ray crystallography structures? What is the coupling between the conformational dynamics and the chemical process? We plan to build a practical simulation method based on RPP to study the role of protein conformation changes and dynamics in enzymatic catalysis process.
Aim 3. We plan to investigate the mechanism of cell-division cycle25 (Cdc25B) phosphatase and generate a complete picture of the possible reaction mechanisms of Cdc25B with the small molecule substrate phenyl phosphate and also with its protein substrate.
Aim 4. We will investigate the reaction mechanism for the decarboxylation and dehalogenation reactions catalyzed by 4-Oxalocrotonate tautomerase (4OT) and elucidate the role of protein backbone in 4OT catalysis. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM061870-05
Application #
6872658
Study Section
Molecular and Cellular Biophysics Study Section (BBCA)
Program Officer
Wehrle, Janna P
Project Start
2000-07-01
Project End
2008-11-30
Budget Start
2004-12-01
Budget End
2005-11-30
Support Year
5
Fiscal Year
2005
Total Cost
$253,440
Indirect Cost
Name
Duke University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Sutton, Christopher; Yang, Yang; Zhang, Du et al. (2018) Single, Double Electronic Excitations and Exciton Effective Conjugation Lengths in ?-Conjugated Systems. J Phys Chem Lett 9:4029-4036
Su, Neil Qiang; Li, Chen; Yang, Weitao (2018) Describing strong correlation with fractional-spin correction in density functional theory. Proc Natl Acad Sci U S A 115:9678-9683
Shen, Lin; Zeng, Xiancheng; Hu, Hao et al. (2018) Accurate Quantum Mechanical/Molecular Mechanical Calculations of Reduction Potentials in Azurin Variants. J Chem Theory Comput 14:4948-4957
Shen, Lin; Yang, Weitao (2018) Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks. J Chem Theory Comput 14:1442-1455
Al-Saadon, Rachael; Sutton, Christopher; Yang, Weitao (2018) Accurate Treatment of Charge-Transfer Excitations and Thermally Activated Delayed Fluorescence Using the Particle-Particle Random Phase Approximation. J Chem Theory Comput 14:3196-3204
Wang, Hao; Yang, Weitao (2018) Force Field for Water Based on Neural Network. J Phys Chem Lett 9:3232-3240
Jin, Ye; Zhang, Du; Chen, Zehua et al. (2017) Generalized Optimized Effective Potential for Orbital Functionals and Self-Consistent Calculation of Random Phase Approximations. J Phys Chem Lett 8:4746-4751
Lewis Jr, Charles A; Shen, Lin; Yang, Weitao et al. (2017) Three Pyrimidine Decarboxylations in the Absence of a Catalyst. Biochemistry 56:1498-1503
Wu, Jingheng; Shen, Lin; Yang, Weitao (2017) Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations. J Chem Phys 147:161732
Chen, Zehua; Zhang, Du; Jin, Ye et al. (2017) Multireference Density Functional Theory with Generalized Auxiliary Systems for Ground and Excited States. J Phys Chem Lett 8:4479-4485

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