# Computer Simulations of Enzymes

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $360,797

## Abstract

Computer simulations using molecular dynamics (MD) and the combined quantum 
mechanical/molecular mechanical (QM/MM) approach are capable of describing structures and dynamics 
of proteins and chemical reactions catalyzed by enzymes. An accurate and 
computationally efficient energy function is necessary. However, challenges remain: the 
accuracy of QM method, the compatibility between the electron density of the QM subsystem and 
classical force fields for the MM subsystem, and the cost of ab initio QM/MM methods capitalizing 
on the accuracy and reliability of the associated QM approaches. To address these challenges, we 
have developed a series of ab initio QM/MM approaches on reaction path optimizations and free 
energy calculations, the QM/MM minimum free-energy path (QM/MM-MFEP) and the QM/MM neural 
network (QM/MM-NN) methods. This proposal aims to develop further the ab initio QM/MM methodology 
and its applications to the studies of redox processes in important enzymes, and the construction 
of ab initio force fields combined with neural network representations.
Our long-term goals are to develop and establish accurate first-principles based and density 
functional theory (DFT) based MD and QM/MM simulation as an equal partner with experiments for the 
study of enzymes and proteins and to provide insight into chemical and redox processes in 
biological systems. Our aims are as follows: (1) We aim to make ab initio QM/MM models for much 
more accurate QM/MM energies, for the QM description and for the electrostatic and vdW interactions 
 between the QM and MM subsystems. (2) We aim to develop a combined computational 
model to explore the key molecular determinants of the reduction potential variability in 
metalloproteins. We will provide detailed insight into chemical and redox reaction mechanisms in 
biological systems, in particular laccases. (3) We aim at the development of accurate 
force fields of water, and proteins for simulations in biological applications, going beyond the 
traditional force field forms and limitation in accuracy.
The proposed developments will capitalize on the theoretical developments in quantum 
electronic theory, such as the linear response theory and accurate many-electron approach for 
non-covalent interactions, and leverage machine-learning methods in data science for biological 
system simulations. The proposed work will lead to the major advancement of the ab initio QM/MM 
method and force fields, and insights into the structure-function paradigm for proteins 
and important redox process and reaction mechanisms in enzymes. In addition, it will also lead to 
methodology development for design of new drugs and enzyme inhibitors.

## Key facts

- **NIH application ID:** 10001530
- **Project number:** 5R01GM061870-18
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Weitao Yang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $360,797
- **Award type:** 5
- **Project period:** 2000-07-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001530

## Citation

> US National Institutes of Health, RePORTER application 10001530, Computer Simulations of Enzymes (5R01GM061870-18). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10001530. Licensed CC0.

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