Research in Dr. Li's Group
Metal ions are crucial to many biological processes such as enzymatic catalysis, electron transfer, and signal transduction. Computational modeling plays a more and more active role in scientific research nowadays. However, the mechanisms of many metal containing biomolecules are poorly understood and, furthermore, modeling metal ions in such systems is quite challenging. The Li research group will perform theoretical and computational studies at the interface of biochemistry and inorganic chemistry. The goals of my research group are to gain fundamental mechanistic insights on important metalloproteins and to apply our understanding to molecular design for catalytic, material, and biomedical purposes. To achieve these goals, we will develop accurate and efficient models as computational tools. In the early stage, the research of Dr. Li's group will focus on the following three projects.
Project 1. Driving Accurate and Efficient Modeling of Metal-Ligand Interactions by Developing an Artificial Intelligence Assisted Polarizable (AI-POL) Model.
Metallodrugs and metalloenzyme inhibitors are two important types of medicines whose operations are dictated by metal-ligand interactions. However, it is challenging to model metal-ligand interactions both accurately and efficiently. Recent advances in machine learning approaches have provided encouraging results; however, the transferability of the generated parameters is usually quite limited. We aim to develop an artificial intelligence assisted polarizable (AI-POL) model for predicting the energetic properties of metal ion containing systems with high accuracy, cheap computational cost, and improved transferability. Specifically, we will (1) develop the AI-POL model, (2) extend the AI-POL model to predict metal-ligand binding affinities, and (3) incorporate the AI-POL model into the AMBER force field. This AI-POL model can then be applied to the development of metal relevant therapeutics, such as the design of drugs with optimal metal-ligand binding for Alzheimer’s disease and the design of inhibitors for metalloenzymes that play a role in cancers.
Project 2. Optimizing Ligand Affinity and Specificity for Ca2+ Binding Proteins.
Ca2+ plays essential roles in signal transduction pathways inside cells, but the molecular basis of Ca2+ binding proteins is not fully established. Elucidating the mechanisms of Ca2+ binding proteins will facilitate advances in biomedical technologies. Specifically, we will use molecular dynamics simulations to understand how the protein scaffold and target-binding influence metal binding in the proteins. In addition, we will investigate the ligand binding mechanism of the S100B protein, a Ca2+ binding protein that is a biomarker and potential therapeutic target for malignant melanoma, in order to optimize the ligand affinity and specificity.
Project 3. Understanding Proton-Coupled Electron Transfer in Nitrogenase
Nitrogenase is the only enzyme known to perform nitrogen fixation. Unfortunately, many mysteries remain about how it reduces one of the most stable molecules, N2, at ambient temperatures and pressures. Understanding this complex process is extremely lucrative for the development of alternative energy technologies. We will apply theoretical and computational approaches to answer two critical questions: (1) how protein dynamics regulate the electron transfer process, and (2) how the multiple protons and multiple electrons transfer to the catalytic center.