UC Riverside

The broad objectives of this work are to provide and apply computational tools to reveal how structural and dynamic protein features affect substrate binding processes and enzymatic efficiency. New findings are also used to design enzyme mutants with enhanced catalytic activity. Static structures have been available for many proteins, however, only recently, advances in experimental and computational methods allow scientists to explore the relationship between protein dynamics and conformational changes involved in function. It has long been of interest to identify which mechanisms and rules proteins utilize for allosteric and synergetic regulations. Although experiments provide information from biochemical assays, it is challenging to understand why and how enzymes/proteins have the behavior we obtain from data. Computational methods bridge the gap by allowing an atomistic level insight into conformational changes and protein-ligand interactions. While various experimental methods can find the overall effect on catalysis from a mutation distant from the active site, experiments cannot explain why. To answer the above questions, in this study, we have employed two model systems – tryptophan synthase and papain-like protease. We have applied a combination of various computational methods such as molecular dynamics simulations, molecular docking, binding energy calculations and pair-wise force distribution, to understand how dynamics and conformational changes are induced by ligand binding and protein mutants. Thorough understanding of allosteric regulations, ligand-protein interactions, and mutation effect assists designing potent inhibitors and more efficient enzymes.