Paving the Way for Protein (Un)Folding Pathways: Towards Machine-Learning Guided Weighted Ensemble Simulations of Rare-Event Sampling

Molecular dynamics simulations of rare events such as protein folding and unfolding are difficult due the large differences in time scales between the process of interest and stable states. In my dissertation, I will present advances to the weighted ensemble (WE) path sampling strategy that I have developed and demonstrate how the WE strategy can be used to efficiently simulate a protein-folding process. In Chapter 1, I motivate the need for studying biological processes using molecular dynamics simulations, introduce the WE method, and present potential future directions. In Chapters 2 and 3, I will detail joint experimental-simulation studies in which I have characterized both the thermodynamics and kinetics of a folding process for a small alpha-helical protein and five artificially modified variants of this protein. In the second half of my thesis, I will further describe some of the improvements to the WE strategy that allow for simulation of long time scale processes. In Chapter 4, I will detail changes in the open-source WESTPA software package for running WE simulations. In Chapter 5, I will present how machine learning-based progress coordinates can be used to accelerate WE simulation sampling on-the-fly. These chapters, together, demonstrate the current capabilities of the WE strategy, including enhancements provided by machine learning.