Enzymes direct chemical reactions with precision and speed, making life as we know it possible. How they do this is still not completely understood, but the relatively recent discovery of subpicosecond protein motion coupled to the reaction coordinate has provided a crucial piece of the puzzle. This type of motion is called a rate-promoting vibration (RPV) and has been seen in a number of different enzymatic systems. It typically involves a compression of the active site of the enzyme which lowers the barrier for the reaction to occur. In this work we present a number of studies that probe these motions in two dehydrogenase enzymes, yeast alcohol dehydrogenase (YADH) and homologs of lactate dehydrogenase (LDH). The goal of the study on the reaction of YADH was to probe the role of the protein in proton tunneling in the enzyme, which was suggested to occur from experimental kinetic isotope effect studies. We did this using transition path sampling (TPS), which perturbatively generates ensembles of reactive trajectories to observe transitions between stable states, such as chemical reactions. By applying a quantum method that can account for proton tunneling, centroid molecular dynamics, and generating reactive trajectory ensembles with and without the method, we were able to observe the change in barrier to proton transfer upon application of the tunneling method. We found that there was little change in the barrier, showing that classical over-the-barrier transfer is dominant over tunneling in the proton transfer in YADH. We also applied the knowledge of RPVs to identify a new class of allosteric molecules, which modulate enzymatic reaction not by changing a binding affinity, but by disrupting the reactive motion of enzymes. We showed, through design of a novel allosteric effector for human heart LDH, applying TPS to a system with and without the small molecule bound, and analysis of the reaction coordinate of the reactive trajectory ensemble, that the molecule was able to disrupt the motion of the protein such that it was no longer coupled to the reaction. We also examined the subpicosecond motions of two other LDHs, from Plasmodium falciparum and Cryptosporidium parvum, which evolved separately from previously studied LDHs. We found, using TPS and reaction coordinate identification, that while the LDH from C. parvum had similar dynamics to the earlier LDHs, the LDH from P. falciparum had a earlier transition-state associated with proton transfer, not hydride transfer. This is likely due to this LDH having a larger active site pocket, increasing the amount of motion necessary for proton transfer, and, thus, the barrier to proton transfer. More work is necessary in this system to determine whether the protein is coupled with the search for the reactive conformation for proton transfer. Protein motion coupled to the particle transfer in dehydrogenases plays an important role in their reactions and there is still much work to be done to understand the extent and role of RPVs.

Employing correlation consistent basis sets coupled with electronic structure methods has enabled accurate predictions of chemical properties for second- and third-row main group and transition metal molecular species. For third-row (Ga-Kr) molecules, the performance of the correlation consistent basis sets (cc-pVnZ, n=D, T, Q, 5) for computing energetic (e.g., atomization energies, ionization energies, electron and proton affinities) and structural properties using the ab initio coupled cluster method including single, double, and quasiperturbative triple excitations [CCSD(T)] and the B3LYP density functional method was examined. The impact of relativistic corrections on these molecular properties was determined utilizing the Douglas-Kroll (cc-pVnZ-DK) and pseudopotential (cc-pVnZ-PP) forms of the correlation consistent basis sets. This work was extended to the characterization of molecular properties of novel chemically bonded krypton species, including HKrCl, FKrCF3, FKrSiF3, FKrGeF3, FKrCCF, and FKrCCKrF, and provided the first evidence of krypton bonding to germanium and the first di-krypton system. For second-row (Al-Ar) species, the construction of the core-valence correlation consistent basis sets, cc-pCVnZ was reexamined, and a revised series, cc-pCV(n+d)Z, was developed as a complement to the augmented tight-d valence series, cc-pV(n+d)Z. Benchmark calculations were performed to show the utility of these new sets for second-row species. Finally, the correlation consistent basis sets were used to study the structural and spectroscopic properties of Au(CO)Cl, providing conclusive evidence that luminescence in the solid-state can be attributed to oligomeric species rather than to the monomer.

Glutathione syntethase (GS) is an enzyme that belongs to the ATP-grasp superfamily and catalyzes the second step in the biosynthesis of glutathione. GS has been purified and sequenced from a variety of biological sources; still, its exact mechanism is not fully understood. Four highly conserved residues were identified in the binding site of human GS. Additionally, the G-loop residues that close the active site during catalysis were found to be conserved. Since these residues are important for catalysis, their function was studied computationally by site-directed mutagenesis. Starting from the reported crystal structure of human GS, different conformations for the wild type and mutants were obtained using molecular dynamics technique. The key interactions between residues and ligands were detected and found to be essential for enzyme activity.

abstract: Elucidation of Antigen-Antibody (Ag-Ab) interactions is critical to the understanding of humoral immune responses to pathogenic infection. B cells are crucial components of the immune system that generate highly specific antibodies, such as IgG, towards epitopes on antigens. Serum IgG molecules carry specific molecular recognition information concerning the antigens that initiated their production. If one could read it, this information can be used to predict B cell epitopes on target antigens in order to design effective epitope driven vaccines, therapies and serological assays. Immunosignature technology captures the specific information content of serum IgG from infected and uninfected individuals on high density microarrays containing ~105 nearly random peptide sequences. Although the sequences of the peptides are chosen to evenly cover amino acid sequence space, the pattern of serum IgG binding to the array contains a consistent signature associated with each specific disease (e.g., Valley fever, influenza) among many individuals. Here, the disease specific but agnostic behavior of the technology has been explored by profiling molecular recognition information for five pathogens causing life threatening infectious diseases (e.g. DENV, WNV, HCV, HBV, and T.cruzi). This was done by models developed using a machine learning algorithm to model the sequence dependence of the humoral immune responses as measured by the peptide arrays. It was shown that the disease specific binding information could be accurately related to the peptide sequences used on the array by the machine learning (ML) models. Importantly, it was demonstrated that the ML models could identify or predict known linear epitopes on antigens of the four viruses. Moreover, the models identified potential novel linear epitopes on antigens of the four viruses (each has 4-10 proteins in the proteome) and of T.cruzi (a eukaryotic parasite which has over 12,000 proteins in its proteome). Finally, the predicted epitopes were tested in serum IgG binding assays such as ELISAs. Unfortunately, the assay results were inconsistent due to problems with peptide/surface interactions. In a separate study for the development of antibody recruiting molecules (ARMs) to combat microbial infections, 10 peptides from the high density peptide arrays were tested in IgG binding assays using sera of healthy individuals to find a set of antibody binding termini (ABT, a ligand that binds to a variable region of the IgG). It was concluded that one peptide (peptide 7) may be used as a potential ABT. Overall, these findings demonstrate the applications of the immunosignature technology ranging from developing tools to predict linear epitopes on pathogens of small to large proteomes to the identification of an ABT for ARMs. / Dissertation/Thesis / Doctoral Dissertation Biochemistry 2020

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The presence of excess free radicals in the body can result in severe health consequences because of oxidative damage to cells. Spin traps may be used as a probe to examine radical reactions in cells, but there is a need for less toxic and more lipid soluble examples. Melatonin is one of the numerous antioxidants used to scavenge free radicals in the body and reportedly one of the most efficient radical scavengers known. It is relatively nontoxic and easily crosses the lipid bilayer in cell membranes. Melatonin is thought to undergo a multistep oxidation process and this work investigates the potential for it to be used as a spin trap. The presence of electron withdrawing or donating groups added to melatonin may stabilize an intermediate and allow it to function as a spin trap. The essence of this study is to conduct a computational inquiry into the relative stability of melatonin, selected derivatives, and the partial oxidation products formed from the scavenging of hydroxyl radical. To determine this, geometries were optimized for each molecule at the DFT/B3LYP/6-31G(d) and HF/6-31G(d) levels of theory.

Computational methods were employed to investigate catalytic processes. First, DFT calculations predicted the important geometry metrics of a copper–nitrene complex. MCSCF calculations supported the open-shell singlet state as the ground state of a monomeric copper nitrene, which was consistent with the diamagnetic character deduced from experimental observations. The calculations predicted an elusive terminal copper nitrene intermediate. Second, DFT methods were carried out to investigate the mechanism of C–F bond activation by a low-coordinate cobalt(I) complex. The computational models suggested that oxidative addition, which is very rare for 3d metals, was preferred. A π–adduct of PhF was predicted to be a plausible intermediate via calculations. Third, DFT calculations were performed to study ancillary ligand effects on C(sp3)–N bond forming reductive elimination from alkylpalladium(II) amido complexes with different phosphine supporting ligands. The dimerization study of alkylpalladium(II) amido complexes indicated an unique arrangement of dative and covalent Pd-N bonds within the core four-membered ring of bimetallic complexes. In conclusion, computational methods enrich the arsenal of methods available to study catalytic processes in conjunction with experiments.

<div>Data scarcity is intrinsic to many problems in chemical engineering due to physical constraints or cost. This challenge is acute in chemical and materials design applications, where a lack of data is the norm when trying to develop something new for an emerging application. Addressing novel chemical design under these scarcity constraints takes one of two routes: the traditional forward approach, where properties are predicted based on chemical structure, and the recent inverse approach, where structures are predicted based on required properties. Statistical methods such as machine learning (ML) could greatly accelerate chemical design under both frameworks; however, in contrast to the modeling of continuous data types, molecular prediction has many unique obstacles (e.g., spatial and causal relationships, featurization difficulties) that require further ML methods development. Despite these challenges, this work demonstrates how transfer learning and active learning strategies can be used to create successful chemical ML models in data scarce situations.<br></div><div>Transfer learning is a domain of machine learning under which information learned in solving one task is transferred to help in another, more difficult task. Consider the case of a forward design problem involving the search for a molecule with a particular property target with limited existing data, a situation not typically amenable to ML. In these situations, there are often correlated properties that are computationally accessible. As all chemical properties are fundamentally tied to the underlying chemical topology, and because related properties arise due to related moieties, the information contained in the correlated property can be leveraged during model training to help improve the prediction of the data scarce property. Transfer learning is thus a favorable strategy for facilitating high throughput characterization of low-data design spaces.</div><div>Generative chemical models invert the structure-function paradigm, and instead directly suggest new chemical structures that should display the desired application properties. This inversion process is fraught with difficulties but can be improved by training these models with strategically selected chemical information. Structural information contained within this chemical property data is thus transferred to support the generation of new, feasible compounds. Moreover, transfer learning approach helps ensure that the proposed structures exhibit the specified property targets. Recent extensions also utilize thermodynamic reaction data to help promote the synthesizability of suggested compounds. These transfer learning strategies are well-suited for explorative scenarios where the property values being sought are well outside the range of available training data.</div><div>There are situations where property data is so limited that obtaining additional training data is unavoidable. By improving both the predictive and generative qualities of chemical ML models, a fully closed-loop computational search can be conducted using active learning. New molecules in underrepresented property spaces may be iteratively generated by the network, characterized by the network, and used for retraining the network. This allows the model to gradually learn the unknown chemistries required to explore the target regions of chemical space by actively suggesting the new training data it needs. By utilizing active learning, the create-test-refine pathway can be addressed purely in silico. This approach is particularly suitable for multi-target chemical design, where the high dimensionality of the desired property targets exacerbates data scarcity concerns.</div><div>The techniques presented herein can be used to improve both predictive and generative performance of chemical ML models. Transfer learning is demonstrated as a powerful technique for improving the predictive performance of chemical models in situations where a correlated property can be leveraged alongside scarce experimental or computational properties. Inverse design may also be facilitated through the use of transfer learning, where property values can be connected with stable structural features to generate new compounds with targeted properties beyond those observed in the training data. Thus, when the necessary chemical structures are not known, generative networks can directly propose them based on function-structure relationships learned from domain data, and this domain data can even be generated and characterized by the model itself for closed-loop chemical searches in an active learning framework. With recent extensions, these models are compelling techniques for looking at chemical reactions and other data types beyond the individual molecule. Furthermore, the approaches are not limited by choice of model architecture or chemical representation and are expected to be helpful in a variety of data scarce chemical applications.</div>

<p> Metal-stabilized carbenes, most commonly formed through the decomposition of  diazoacetates, are extensively employed in organic synthesis. However, several classes of carbenes,  such as vinylidenes, are challenging to utilize in transition metal catalysis due to the instability of  the required diazo precursors. To overcome this challenge, most transition metal-catalyzed  vinylidene transfer and insertion methods rely on alkynes as vinylidene precursors. Only catalysts  that form stable M=C multiple bonds and weak M(π-C≡C) interactions can promote this alkyne  isomerization, and the resultant metal(vinylidene) species is often less reactive compared to free  vinylidenes. The discovery of 1,1-dihaloalkenes as precursors to transition metal vinylidene  complexes has significantly expanded the scope of vinylidene transfer and insertion reactions.  Dinuclear catalysts were found to promote the reductive cyclization of 1,1-dichloroalkenes  containing pendant alkenes to form methylenecycloalkenes, and mechanistic studies are consistent  with the formation of a Ni2(vinylidene) species. Furthermore, these catalysts promote reductive  three-component cycloaddition reactions with 1,1-dichloroalkenes and aldehydes to generate  methylenedioxolanes, which upon treatment with aqueous acid provides access in one step to new,  unsymmetrical aliphathic α-hydroxy ketones that would be difficult to access with existing  methods. Under dilute conditions, an enone byproduct is formed and a DFT model is presented  that accounts for concentration-based reaction selectivity.</p>