Molecules in cells constantly move. The motions of proteins in living cells can be simple fluctuations or functional. Therefore, investigating protein dynamics is crucial for understanding protein function and for accurately compute ligand-protein binding free energy landscape. Because experimental structures are static conformations, classical or enhanced molecular dynamics (MD) simulations are commonly used for conformational sampling. Machine/deep learning approaches can then be used to analyze MD results and assist conformational sampling and energy calculations.

In this presentation, we will focus on modeling ligand-receptor binding/unbinding pathways to compute protein-drug binding thermodynamics and kinetics for drug development. We will show the binding free energy landscape constructed by Binding Kinetics Toolkit (BKiT), a program using post-analysis, principal component analysis and milestoning theory to predict drug binding kinetics. We will also discuss use of machine learning and deep learning to enhance protein conformational sampling to model protein conformational transition and other applications.

Dr. Chia-en Chang