CBE Doctoral Dissertation Defense: “Computational Strategies for Efficiently Sampling Conformational Changes in Solvated Macromolecules” (Akash Pallath)

Abstract:

Solvated macromolecules such as proteins and polymers undergo conformational changes in response to stimuli such as pressure, temperature, pH, ligand binding, and post-translational modification. These transitions are fundamental to biological function, from cellular signaling to misfolding and aggregation, and are increasingly harnessed in applications such as drug delivery, biosensing, and biomaterials design. Understanding how such stimuli shape macromolecular conformation at atomic resolution is critical, but experimentally challenging. Molecular simulations offer a powerful route to probe these transitions, yet conventional approaches often struggle to capture them, as the relevant timescales exceed those accessible to standard simulations.

In this work, we develop strategies to overcome these challenges for a small but challenging subset of stimuli and systems. We begin with a simple stimulus: hydrostatic pressure. Proteins are known to denature under elevated pressures in the kilobar range. Studying their pressure response can shed light on the molecular basis of extremophilic adaptation and help uncover latent functional sites, particularly those exposed upon allosteric activation. However, simulating pressure-induced responses remains difficult due to slow, solvent-coupled kinetics. We introduce a hydration-based biasing strategy that mimics the thermodynamic effects of pressure while bypassing its kinetic bottlenecks. Applying this approach to proteins such as Ubiquitin, we efficiently sample pressure-denatured ensembles and benchmark predictions against high-pressure NMR data. We then demonstrate how our strategy can identify a functionally relevant allosterically-gated interface in the signaling protein CheY and map pressure-temperature stability landscapes of homologs from mesophilic and thermophilic organisms.

We then briefly turn to more complex or coupled stimuli, which require us to fully resolve the underlying free energy landscape. To do so in a controlled setting, we study linear hydrophobic polymers as a model system. We evaluate a range of collective variables for capturing their collapse transitions, show that sampling becomes increasingly challenging at longer chain lengths, and uncover hidden orthogonal barriers that hinder exploration even in these simple systems.

Together, this work presents a framework for simulating conformational responses to environmental stimuli, enabling mechanistic insight across proteins, polymers, and other solvated macromolecules.