In the past several decades, exciting research has elucidated that the structural dynamics of proteins have a profound impact on their activities. Molecular dynamics (MD) simulations have become a powerful means of exploring the ensembles of structures accessible to proteins, in order to compute thermodynamic properties that allow us to understand the forces that drive the binding of a drug to its receptor, or even the formation of macromolecular assemblages. The research presented in this dissertation utilizes MD simulations, among other computational techniques, introduced in Chapter 1, to further our understanding of the activation mechanism of histone deacetylase 3 (HDAC3), a promising target for epigenetic cancer therapy. The enzymatic activity of HDAC3 depends on its binding to other proteins and a small moelcule, inositol tetraphosphate (IP4). In Chapter 2, we show how the binding of HDAC3 to a deacetylase activating domain and to IP4 restrain the dynamics of HDAC3, suggesting a single active HDAC3 conformation. In Chapter 3, we show that the dynamics of HDAC3 can be similarly stabilized by mutating a single residue near the IP4-binding site, shedding light on the long-range allosteric networks that must be conserved for the deacetylase activity of HDAC3. In Chapter 4, we show that the restrained conformational ensembles of HDAC3 have predictive power in identifying known HDAC inhibitors among decoy drug-like molecules, and perform a structure-based virtual screen for novel active site and allosteric inhibitors. One of the challenges in using MD simulations to address these types of questions for systems is the extent to which an MD trajectory can indeed represent the conformational ensemble of the system. In Chapter 5, we present a method that aims to enhance the sampling of relevant conformations, particularly for a problematic system with kinetically "trapped" conformations

Histone deacetylases (HDACs), together with histone acetyltransferases (HATs), regulate gene expression by modulating the acetylation level of chromatin. HDAC3 is implicated in many important cellular processes, particularly in cancer cell proliferation and metastasis, making inhibition of HDAC3 a promising epigenetic treatment for certain cancers. HDAC3 is activated upon complex formation with both inositol tetraphosphate (IP4) and the deacetylase-activating domain (DAD) of multi-protein nuclear receptor corepressor complexes. In previous studies, we have shown that binding of DAD and IP4 to HDAC3 significantly restricts its conformational space towards its stable ternary complex conformation, and suggest this to be the active conformation. Here, we report a single mutation of HDAC3 that is capable of mimicking the stabilizing effects of DAD and IP4, without the presence of either. This mutation, however, results in a total loss of deacetylase activity, prompting a closer evaluation of our understanding of the activation of HDAC3.

Conformational changes that occur upon ligand binding may be too slow to observe on the time scales routinely accessible using molecular dynamics simulations. The adaptive integration method (AIM) leverages the notion that when a ligand is either fully coupled or decoupled, according to λ, barrier heights may change, making some conformational transitions more accessible at certain λ values. AIM adaptively changes the value of λ in a single simulation so that conformations sampled at one value of λ seed the conformational space sampled at another λ value. Adapting the value of λ throughout a simulation, however, does not resolve issues in sampling when barriers remain high regardless of the λ value. In this work, we introduce a new method, called Accelerated AIM (AcclAIM), in which the potential energy function is flattened at intermediate values of λ, promoting the exploration of conformational space as the ligand is decoupled from its receptor. We show, with both a simple model system (Bromocyclohexane) and the more complex biomolecule Thrombin, that AcclAIM is a promising approach to overcome high barriers in the calculation of free energies, without the need for any statistical reweighting or additional processors.