Enhanced Sampling In Biomolecular Recognition Simulations

Molecular recognition is the key process in regulating macromolecular function states. As the major form of molecular recognition, simulations of receptor-ligand binding interaction have been a central interest in recent decades. However, development of novel enhanced configuration sampling as well as free energy approaches lay the major technical obstacles. This project is devoted to developing a novel sampling approach for ligand-receptor binding free energy simulation. We propose to develop a novel sampling algorithm for biomolecular simulation and implementation with computer programming and High Performance Computing; exploring basic rules of computer aided drug design through computational chemistry. We seek to incorporate Free Energy Perturbation(FEP) and relative binding free energy calculations, which calculate the free energy difference between two different molecular species of similar structures, to improve the accuracy of receptor-ligand binding predictions. We will then apply these methods in Nanoscale Molecular Dynamics(NAMD), using it to calculate the binding free energy of ligands to receptors and optimize receptor-ligand binding simulations. By utilizing these computational chemistry techniques, we aim to improve the efficiency of computer-aided drug design.