PhD
Associate Professor, Pharmacology
Computational Medicine Program
UNC-Chapel Hill
Cancer Therapeutics Research Program
Area of Interest
Remarkable advances in supercomputing and artificial intelligence (AI) are transforming computational biology and chemistry in studies of molecules to cells. However, large gaps remain between the time scales of modern computer simulations (typically microseconds) and those of biological processes (milliseconds or longer). It has proven challenging to achieve sufficient sampling and compute thermodynamics and kinetics of biological systems, hindering effective drug design.
Our research is focused on the development of novel theoretical and computational methods and AI techniques, which greatly enhance computer simulations and facilitate simulation analysis, and the application of these methods, making unprecedented contributions to biomolecular modeling and drug discovery. In collaboration with leading experimental groups, we combine complementary simulations and experiments to uncover functional mechanisms and design drugs of important biomolecules, including G-protein-coupled receptors (GPCRs), membrane-embedded proteases, RNA-binding proteins and RNA.
Our cancer drug targets include RNA, RNA-binding proteins (RBPs), GPCRs and G proteins. Particularly, RNA and RBPs have emerged as exciting targets for discovering cancer drugs of new mechanisms. However, they have proven difficult for drug design especially due to their extremely high flexibility and poorly defined targets sites. Mechanisms of ligand-RNA/RBP and RNA-RBP interactions remain largely unknown. My lab has performed Gaussian accelerated Molecular Dynamics (GaMD) simulations, which, for the first time, captured multiple times of spontaneous and highly accurate binding of RNA from bulk solvent to a Musashi RBP as determined in the NMR structure (Current Research in Structural Biology, 2022).
In collaborations with leading experimental groups, my lab has successfully carried out computer-aided design of inhibitors for the Musashi and human antigen R (HuR) RBPs (Cancers, 2020; Journal of Medicinal Chemistry, 2023). In collaboration with the Jingxin Wang lab in Medicinal Chemistry, my lab has performed GaMD simulations that captured spontaneous ligand binding to flexible RNA structures (Nucleic Acids Research, 2021; BioRxiv, 2025). We have uncovered a novel ligand-binding pocket formed by two sequential GAAG loop-like structures in pre-mRNA of the survival of motor neuron 2. Our simulations were highly consistent with NMR and structure-affinity-relationship experiments (Nucleic Acids Research, 2021). In addition, we have successfully characterized the mechanism, thermodynamics and kinetics of ligand binding to the theophylline RNA aptamer (Journal of Chemical Information and Modeling, 2023).
For GPCRs that are implicated in human cancers, we have carried all-atom accelerated molecular simulations on particularly the chemokine and adhesion GPCRs. Our simulations have revealed the mechanisms of ligand binding and/or dissociation, as well as deactivation and activation of the target receptors (Future Medicinal Chemistry, 2020; Biochemistry, 2025). In addition, we have been combining simulations and collaborative experiments to investigate the functional mechanisms and design selective inhibitors of the G protein mutants that cause cancers.
Awards and Honors
- OpenEye Outstanding Junior Faculty Award, ACS Computational Chemistry, 2021
- Scientist Development Grant Award, American Heart Association, 2017-19
