Browsing by Subject "computational chemistry"

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    Factors Affecting Recognition and Chemical Reactivity in Macromolecular Systems
    (2015-07) Isley III, William
    As chemists characterize molecular systems in greater detail, it becomes clear that some observables can only be properly studied at the macromolecular scale. However, elucidating the physical principles behind such phenomena as molecular recognition or chemical reactivity can be difficult when moving into the macromolecular regime. The objective of this work is to provide insights and predictions to complement experimental undertakings. The work is divided into two categories: 1) modeling molecular recognition through prediction of intermolecular interactions with highly accurate methods and 2) the modeling of chemical reactivity. The separation of N2 and CH4 is particularly pertinent for the natural gas industry, and improved materials for performing this separation would provide an enormous cost savings. This work is focused on the prediction of a new material capable of performing this separation. Through application of multiple tiers of quantum chemical methods, and comparison to similar known experimentally synthesized materials, a novel material is predicted to effect the separation of N2 and CH4 through selective binding of N2 to an open vanadium metal site. A particularly valuable tool for monitoring target delivery or guest encapsulation in macromolecular systems is an easily observed signal that indicates the status of a host-guest complex. Guest complexation can alter the observable properties of the host, including the spin crossover properties of a host macromolecule. Particular care was taken to correlate guest recognition to changes in paramagnetic NMR chemical shifts induced in the host system. The monitoring of subtle changes in a protein's environment is a challenging and complex problem; however, the observation of ligand complexation in biomolecular systems is extremely important in the design of new medicinal therapeutic drugs. This work aims to develop a quantum chemical method to assist experimental assignment of challenging 19F NMR spectra in proteins. The importance of accurately modeling the hydration environment is extremely critical for accurate comparison to experimental measurements. Selective detection of chemical impurities is an attractive capability to have for any chemical process. A key impurity in industrially synthesized explosive TNT is DNT. Given the high prevalence of DNT in TNT, detection of DNT through electrochemistry is a useful sensor for explosives. This work characterizes the mechanism of DNT electrochemical reduction A new material was found to rapidly catalyze the decomposition of extremely toxic chemical warfare agents. The macromolecular metal organic framework NU-1000 was demonstrated to be extremely effective in catalyzing the hydrolysis of phosphoester based chemical warfare agents. Predictive computations were performed on a nerve agent simulant DMNP, and toxic nerve agents GD (Soman) and VX agents, uncovering the key role that the metal nodes of NU-1000 play in activation of the phosphoester bonds for hydrolytic attack
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    Synthetic, Biochemical, X-ray Crystallographic, Computational and High-Throughput Screening Approaches Toward Anthrax Toxin Lethal Factor Inhibition
    (2015-10) Kurbanov, Elbek
    The lethal factor (LF) enzyme secreted by Bacillus anthracis is chiefly responsible for anthrax-related cytotoxicity. In this dissertation, I present the computational design, synthesis, biochemical testing, structural biology, and virtual and high-throughput screening approaches to identify binding requirements for LF inhibition. To this end, we designed ~50 novel compounds to probe design principles and structural requirements for LF. Specifically, in Chapters 2 and 3, computational, synthetic, biochemical and structural biology methods to explore the underinvestigated LF S2′ binding subsite are described. We discovered that LF domain 3 is very flexible and results in a relatively unconstrained S2′ binding site region. Additionally, we found that the S1′ subsite can undergo a novel conformational change resulting in a previously unreported tunnel region, which we term S1′*, that we expect can further be explored to design potent and selective LF inhibitors. Using this novel LF configuration, we virtually screened ~11 million drug-like compounds for activity against LF and have identified a novel compound that inhibits LF with an IC50 of 126 μM. In the course of this work, we found that reliable representation of zinc and other transition metal centers in macromolecules is nontrivial, due to the complexity of the coordination environment and charge distribution at the catalytic center. In Chapter 7, I will present work on applying and optimizing quantum mechanical methods developed by the Truhlar group to accurately calculate bond dissociation energies at low computational cost for various representative Zn2+ and Cd2+ model systems. By analyzing errors, we developed a prescription for an optimal system fragmentation strategy for our models. With this scheme, we find that the EE-3B-CE method is able to reproduce 53 conventionally calculated bond energies with an average absolute error of only 0.59 kcal/mol. Therefore, one could use the EE 3B CE approximation to obtain accurate results for large systems and/or identify better parameters for Zn centers for use in virtual screening. Finally, we present the results of a large-scale in vitro HTS campaign of ~250,000 small-molecules against LF. After extensive validation, involving secondary assays and hit synthesis we were able to prioritize a key lead for further prosecution.