UC Riverside

Molecular Recognition, an important step of association process, guides the association of two molecular entities through physical differentiation in chemical processes. Researchers developed simulation techniques to model a wide range of chemical and biological systems to study the molecular encounters at atomic-level details since these are not available by experimental techniques. The broad objectives of this work are to provide and apply computational tools to model the recognition mechanism of ligand-protein binding and explore the diffusional association pathways of a ligand in diverse environments and arrangements. It also includes the development of new tools for simulations, analysis, and conformation prediction.The dissertation project is mainly focused on Brownian Dynamics (BD) simulations to study the molecular association processes under different environments. GeomBD3, a BD package, has been developed as a robust tool to model and investigate biomolecular systems under various environmental conditions. This program has been applied to study HIV protease and x263 association in a system featuring ligand flow and surface bound protein effects. We explored the effect of ligand flow on xk263’s effective local concentration, association time, and binding probability in HIVp-xk263 system. GeomBD3 has also been applied to evaluate the effect of colocalization in a multi-enzyme system of yeast-ester biosynthesis. Here, we modeled a system of three enzymes placed on a lipid surface and found efficiency gain from induced enzyme colocalization. The other work described herein is the development of a computational method to construct new protein conformations using Variational Autoencoder. This approach has an edge over traditional molecular dynamics and other enhanced sampling methods to explore and bridge the gaps of unknown structures with lower cost and time.