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Exploratory chemistry is an important branch of computational chemistry where precursor molecules are simulated in reaction-prone conditions without pre-supposed hypotheses. These molecular dynamics simulations can yield many unexpected reaction pathways, confirming existing mechanisms or suggesting new ones. Decreasing cost of hardware and increasing savviness of software make it enticing to explore reaction spaces in this un-guided manner. However, reactions are still rare events buried in data where the molecules spend most of their time not clearing reaction barriers. Therefore, advanced data processing techniques that can parse the simulations for reaction events with high accuracy are valuable ina field with ever increasing data generation.In this work, a method is described in Chapter 2 which addresses the problem of extracting valuable reaction location data from exploratory simulations with high accuracy and low cost. The method uses first derivatives on bond order time series obtained from Ab Initio molecular dynamics data to predict temporal reaction locations with high accuracy and speed. The two tunable parameters (low pass filtering cutoff and threshold placement on the derivative) are parameterized against a “gold standard” approach that represents the ideal data processing scenario given infinite time and computing resources. This reaction event classifier is showcased on two single-molecule reactive test systems. The first, simpler test case being a heptanylium alkyl carbocation, C7H15+. The other more complicated system is Fe3(CO)9, which is a highly unsaturated iron carbonyl cluster. The effectiveness of the reaction event classifier is analyzed for both systems using heat maps and other custom plots and metrics.Deep Eutectic Solvents (DESs) are a relatively new type of mixture which exhibits similar properties to ionic liquids, but with lower cost and environmental impact. Until recently, most force fields describing DESs were not polarizable and therefore lacked certain behaviors created by their extremely partially-charged environment. This second project began with the goal of creating a polarizable force field for DESs, but eventually morphed into creating a program to automate the Tinker polarizable force field initialization process with Python. The test molecule for refining the initialization process is urea. Urea is a hydrogen bond donor which when paired with choline chloride in the correct whole-number ratio create a DES. A semi-automated Python method which creates polarizable Tinker force fields with simple setup is explained.

Pantetheine-containing ligands (PCLs) play key roles in the biosynthesis of polyketides, a large family of natural products with various bioactivities. A major hurdle that remains is our poor understanding of the protein-substrate interactions of ketoreductase (KR), a key component of polyketide synthases (PKSs). Since the poly-β-ketone intermediates are highly reactive and cannot be isolated, the first project of this dissertation employed molecular dynamics (MD) simulations to interpret the transient KR-substrate interactions. Several key factors guiding KR-substrate interactions were identified, which will help further engineering of PKSs and directing biosynthesis of novel polyketides.The reliability of MD simulations depends on the quality of the employed force field, which comprises a mathematical formula and a set of parameters to represent the potential energy of molecular systems. The parameter sets for simulating amino acids, nucleic acids, sugars, and lipids are already available. However, lack of scalable parameter sets for PCLs has hampered the computational studies of various biomolecules containing PCLs. Therefore, in the second project of this dissertation, the first Pantetheine Force Field (PFF) library containing parameter sets for various PCLs compatible with additive force fields was developed and validated.The extensively used additive force fields use fixed atom-centered partial charges to model atomic electrostatic interactions. However, additive force fields cannot accurately model atomic polarization effects, leading to unrealistic simulations in polarization-sensitive processes. The polarizable Gaussian Multipole (pGM) model with all atomic multipoles represented by Gaussian densities has been recently developed. In the third project of this dissertation, an electrostatic parameterization scheme for the pGM model was developed, and the accuracy and transferability of the pGM model were assessed. Encouragingly, the pGM model was shown to be accurate and transferable, which has the potential to serve as the template for developing the next-generation polarizable force field for modeling various biological systems.

Computer simulation methods have the power to greatly optimize drug design, reducing the time and cost that it takes to bring a new drug from lab to market. Two factors of concern in rational drug design are the binding affinity of some small molecule to a biomolecular target and the molecule's ability to permeate the cellular membrane to reach said target. In the first half of this work, I describe my efforts analyzing the protein-ligand binding affinity and membrane permeability of inhibitors of the Hv1 proton channel. Hv1 is a voltage-gated proton channel with clinical relevance, being highly expressed in highly metastatic cancer cell lines. The inhibition of Hv1 has been found to slow the growth of cancer cells. My research on Hv1 channel inhibitors lays the groundwork for future studies on adapting these molecules for more effective channel blocking.In the second half of this work, I examine the tools that are foundational to molecular simulations--force fields--as well as explore the conventional wisdom behind proton assignment in carboxylic acids. Biomolecular simulation represents a valuable computational microscope for drug discovery efforts; however, it is only through correct setup protocols and well-calibrated tools that we can obtain the most accurate and meaningful results.

In this thesis, I have used computational methods to the study of flexibility in metal-organic frameworks (MOFs) and the behavior of adsorbed gases and liquids in MOFs. Among nanoporous materials, MOFs are of particular interest for adsorption-based applications for reasons that include (1) their huge chemical space, which raises the possibility of tuning them for specific industrial processes and (2) the ability of the under-coordinated metals present in some MOFs to endow their respective materials with impressive adsorbate binding energies.Chapters 2 and 4 relate to analogs of a series of MOFs with under-coordinated metals (the M-MOF-74 series). In Chapter 2, I report the discovery of a novel argon adsorbate-induced deformation pattern for this framework series. This result was arrived at using a flexible framework model, and is presented as an explanation for an intriguing signal observed in experimental small-angle X-ray scattering profiles upon argon adsorption. This hypothesis is supported by a complementary investigation of an alternative explanation for the X-ray signal, in which argon atoms are proposed to adsorb at different densities in adjacent pores.In Chapter 3, I studied the dynamics of adsorbed xylene isomers in MOF-5. I compare the translational and rotational motion of the xylene isomers, and explain the differences based on molecular geometries. The most rod-like molecule, para-xylene, is more rotationally constricted in the pore due to its intermolecular interactions with the aromatic group of the MOF linker. This finding has implications for the rational design of MOFs, as a process that can take advantage of the MOF-induced variations in xylene isomer dynamics could be used for lucrative liquid-phase xylene separations.In Chapter 4, I present a method for parameterizing the type of flexible framework model used in Chapter 2 from ab initio quantum chemistry calculations. The goal of this work is to facilitate the rapid development of flexible, versatile MOF models that can capture changes in the coordination bonding of metals in MOFs. This type of model can be used to study structural transitions and may be extended to study MOF formation. We demonstrated that our approach yields models for the M-MOF-74 series that are stable and have simulated structural properties in good agreement with quantum chemistry calculations.

The early stages of drug discovery is a long and costly process. A way to reduce the time, resources, and financial investment spent is to apply computational tools. With the tremendous progress and achievements in computational chemistry, the application of computational tools in the drug lead discovery and design has increased. Today, computational chemistry is considered a highly valuable and well established tool in drug discovery. A variety of computational chemistry methods are used for guiding molecular design and finding new potential drugs and targets. Some examples of these methods are molecular dynamics simulations, free energy calculations, virtual screening, structure-activity relationship analysis, and so on. Despite the fact that computational chemistry technics are widely used in industry and academic environment, there is still room for improvement. Here, I present several studies in which I developed and applied computational chemistry tools in drug discovery problems. The first study is the development of a tool as part of the Open Force Field consortium to learn chemical perception of force fields typing rules. Secondly, I describe my work on using a new hybrid method to calculate free energies of small molecules. Thirdly, I present a binding mode prediction study to help the understanding of structure-activity relationship in lissoclimides. Lastly, I present a study applying molecular dynamic simulation to guide the redesigning of a macrocyclic peptide.

Enzymes in the acetate and non-ribosomal peptide pathways generate chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains. The innate reactivity of pathway intermediates has obfuscated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of “unnatural” natural products.Molecular dynamics can be used to provide models of these key interactions that are difficult to capture experimentally, providing the potential to expand the diversity in these systems. Current force fields support basic biochemical building blocks, and specialized force fields support post-translational modifications and non-canonical amino acids, yet none currently exist that are capable of modeling bound intermediates from these systems.The objective of this dissertation is to address this gap in knowledge and available technology and to present the description of the development of a force field that can be used with MD and other computational techniques to provide models of these experimentally intractable transient interactions. This objective will be divided into three aims.Aim 1: The development and validation of a force field for use in investigations and engineering efforts involving these pathways. A fragmentation approach was used, providing a degree of modularity to the force field while at the same time reducing the size, complexity and degrees of freedom during the charge fitting and parameterization steps. Tutorials and a web interface were also developed to provide end users access to this tool.Aim 2: The application of the force field towards understanding dynamics and interactions in these pathways. Regulation of the transient protein-protein interactions and discrete steps in fatty acid biosynthesis remain poorly understood. MD was able to show that specific interactions with its partner are either strengthened or weakened depending on the loaded state of the carrier protein. The force field was also used to model a plant-based polyketide synthase at different pHs, resulting in the identification of an allosterically modulated pH sensor on the surface of the polyketide synthase. Aim 3: The application of the force field in engineering efforts in these systems to produce biofuels. MD simulations were employed to provide a deeper understanding of protein-substrate interactions in a reductase domain from a non-ribosomal peptide megasynthase. This led to a deeper understanding of this domain, and further identified residues critical for structure integrity and substrate binding, leading to a rationally altered variant with improved activity toward highly reduced substrates.