Exploring free energy landscapes in complex biomolecular systems with advanced computer simulations and neural networks
Embargo Date
2025-08-05
OA Version
Citation
Abstract
Recent advances in computer simulation and experimental techniques have motivated computational chemists and biophysicists to better understand the function of complex biomolecular systems by exploring the underlying free energy landscapes with extensive sampling and/or accurate potential functions. With creative application of existing techniques and continuing development of new methodologies, increasingly complex mechanistic problems can now be solved with computational techniques. In this dissertation, we take advantage of state-of-the-art molecular dynamics simulations and free energy approaches, as well as modern neural networks to tackle two major problems in the area of computational biophysics. The first topic was inspired by recent deep mutational scanning experiments on a transcription factor, the Tetracycline repressor (TetR), which revealed an unexpected distribution of allostery hotspots that cannot be explained by existing models. Accordingly, we have developed a new computational framework to understand the molecular basis of allostery and the broad distribution of hotspot residues in TetR. The key was to integrate long timescale molecular dynamics simulations, free energy computations and analyses of the structural and dynamical properties of TetR at both local and global scales. The mechanistic framework and multifaceted analysis strategy is expected to be applicable to many allostery systems. In the second part, we aim at improving the computational efficiency and accuracy of multi-level free energy simulations so that accurate quantum mechanical potential functions can be applied to complex biomolecular systems at the cost of an inexpensive method, such as a semi-empirical quantum mechanical approach. The solution we propose is an innovative combination of modern neural networks and enhanced sampling simulations, resulting in a computational framework that greatly improves the convergence and accuracy of multi-level free energy calculations for condensed phase systems.
Description
2023
License
Attribution 4.0 International