The development of carbon dioxide (CO2) reduction electrocatalysts is an intensively studied area in the development of CO2 capture, utilization, and storage strategies. In this work, density functional theory (DFT) and ab initio molecular dynamics (AIMD) are employed to study redox properties and the pathway of a side reaction of CO2 reduction electrocatalyst [Fe4N(CO)12]-. The material of this chapter was published as an article titled “Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst” in Chemical Science (2018). I also present the multifaceted development of a molecular mechanics force field, an important piece of molecular mechanics simulations that is widely used for molecular structure and property prediction. An open-source Python package for restrained electrostatic potential (RESP), respyte is implemented for developing improved electrostatic potentials (ESP) based charge models for a better description of electrostatic interactions in force fields. I describe further improvements in Open Force Field small molecule force field after the first optimized small molecule force field from Open Force Field Initiative, which includes modification of parameter set for improved performance in certain chemical spaces, and a more careful design of quantum mechanics (QM) training data used to re-optimize bonded parameters.

Hydrogenases are a family of enzymes that catalyze the reversible redox reaction of molecule hydrogen (H2). There are several kinds of hydrogenases, including [Fe] hydrogenases, [NiFe] hydrogenases, and [FeFe] hydrogenases, found in a variety of organisms. Hydrogenases have attracted much attention from chemists, physicists, and biologists due to their special roles in energy metabolism, as they can produce the cleanest carbon-neutral fuel, H2. Among these hydrogenases, [FeFe] hydrogenases are characterized by their special di-iron ([FeFe]) cluster called the “H-cluster”. The maturation process of the [FeFe] hydrogenases is the biosynthesis of the H-cluster by the enzymes HydE, HydF, and HydG, and the delivery of the H-cluster into HydA. In this thesis, we describe hybrid quantum mechanics (QM)/ molecular mechanics (MM) simulations of the maturation process, including the catalytic processes in HydG and HydE. The results were published in Biochemistry journal as an article titled ”Quantum chemical study of a radical relay mechanism for the HydG-catalyzed synthesis of a Fe(II)(CO)2(CN)cysteine precursor to the H-cluster of [FeFe] hydrogenase”. We proposed a radical-relay mechanism for how HydG catalyzes the decomposition of the tyrosine substrate into COO•− and HCN. These species are converted into CO and CN at the [5Fe-5S] auxiliary cluster in HydG, which bind to the fifth “dangler” Fe and result in the [Fe(II)(CO)2(CN)cysteine] “synthon” product. HydE, as the downstream protein of HydG, modifies this Fe(II) complex into a 5-coordinate Fe(I) cluster via a radical mechanism. Using QM/MM simulations we proposed a feasible radical mechanism for this conversion, as well as a dimerization pathway from the 5-coordinated Fe(I) cluster to a diamagnetic di-iron cluster that is proposed to be the product of HydE.In addition, studies have been done to understand the behavior of a circadian clock protein, KaiB. KaiB is a key component of the KaiABC circadian clock system in cyanobacteria. KaiB changes its folding to bind to KaiA and KaiC respectively to adjust the expression of different kinase proteins. The folding change in KaiB, also named fold-switching, is classified as metamorphic behavior because it involves changes between the secondary structures and three-dimensional structures in contrast to conventional protein conformational changes. Classical, all-atom molecular dynamics simulations exceeding (>100 μs) in length were carried out to study the fold-switching process of the KaiB in explicit solvent. We also developed a new feature, named diversity index (DI), that can distinguish the metamorphic protein sequences from other monomorphic sequences (i.e. one native fold sequences), and this work was published in Biophysical Journal in 2021 titled ”Sequence-based Prediction of Metamorphic Behavior in Proteins”.

The development of environmentally friendly synthetic methodologies is a central and urgent goal of modern chemical science and industry. Advancing mechanistic understanding is crucial to accelerating the development of new reactions involving biobased platform molecules which can be applied to sustainable process design. The ab initio nanoreactor is a method for automated generation of mechanistic trajectories, in which high temperatures, high pressures, and external forces are applied to assemblies of reactants to advance discovery using first-principles MD simulations. I used this ab initio nanoreactor method to solve mechanistic mysteries of green and biological chemistry, which has proved pivotal in a synergistic theoretical and experimental studies. In another application, reaction design and accurate predictions of ligand-protein/protein-protein binding energies is critical in pharmaceutical drug development, and can drive efficient production from predicted reagents, minimize waste, and elucidate the core organizations of biology. Many disease-causing viruses target sialic acids (Sias) on the surface of host cells. Some viruses bind preferentially to sialic acids with O-acetyl modification at the hydroxyl group of C7, C8, or C9 on the glycerol-like side chain. Binding studies of proteins to sialosides containing O-acetylated sialic acids are crucial in understanding the related diseases, but experimentally difficult due to the lability of the ester group. As such, N-acetyl sialic acids have been proposed as stable mimics. I have studied the instability of the ester group across modified Sias, and the structural and biological similarities of these Sias in ligand-protein binding with MERS-CoV S and SARS-CoV-2 S proteins. I have found N-acetyl and O-acetyl Sias interchangeable, suggesting an experimentally reasonable mimic to probe viral mechanisms.

The exploration of reaction pathways and mechanisms is an important part of computationalchemistry. With known reactant molecules and condition it is always intriguing to predict the product, study the mechanism and calculate different chemical properties to match the experimental results. However, due to the limit of computational resources and theory development, it is hard to simulate all reactions with ideal level of theory and simulation time length to overcome energy barriers for desired product. Meanwhile, different computational softwares also have respective disadvantages, which requires scientific programming and problem-solving skills to fix during the research. In chapter one of this thesis, a simple introduction of computational chemistry is written as prerequisite information about all the work. Chapter two and three are projects aiming at a biochemical system with copper, C-peptide and serum albumin. In this work, we propose the structural model of copper binding site in each compound and calculate the UV-Vis spectra which have a good agreement with published experimental results. A new energy-penalty geometry optimization method is proposed for searching the specific structures of desired absorbance. A hypothetical ternary complex containing all three species is tested with this method and provides good agreement with experiments. Effects that determine UV-Vis absorbance of Cu(II)-protein compound is also suggested. A dummy atom copper model is implemented in OPLS-AA force field for Cu(II) molecular dynamics simulation. By tuning the non-bonded interaction between Cu(II) and residue in protein, structure of binding sites in protein&C-peptide are successfully reproduced. Meanwhile, a good match between binding free energy calculations and experiment is obtained. Part of the results are published in: San Juan, J. A. et al. Copper(II) Affects the Biochemical Behavior of Proinsulin C-peptide by Forming Ternary Complexes with Serum Albumin. J. Am. Chem. Soc. 145, 16726–16738 (2023). Remaining results are scheduled to be submitted very soon. Chapter three is a project focusing on methanol to olefin (MTO) reaction. By using the nanoreactor technique in ab initio molecular dynamics simulations, two experiment detected mechanisms are confirmed with corresponding transition states. QM/MM method is also applied for reaching the balance between accuracy and cost of the simulation. In order to simplify the preparing procedure of running QM/MM calculation on software TeraChem, a simple workflow is proposed for non standard residue to generate necessary input files. Nanoreactor code in TeraChem is also modified to accommodate the QM/MM method.

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.

Understanding metabolism is essential for all living organisms [1] . The science ofstudying all metabolites and lipids is metabolomics [1] . One of the most important aims in metabolomics is to identify all detectable structures; yet, the enormous chemical space of natural products and enzymes makes this aim a moving target. Mass spectrometry (MS/MS) is the most commonly used method to identify compounds due to its high sensitivity, selectivity and the information content of mass spectra that come from breaking chemical bonds [2] . However, compounds can only be identified if their mass spectra are included in MS/MS libraries. Compared to over 100 million compounds presented in the PubChem database, MS/MS libraries are small with less than 200,000 structures. Computational chemistry and cheminformatics may bridge this gap by predicting mass spectra from chemical structures (structure-to-spectrum), or by fitting experimental MS/MS information to lists of candidate structures (spectrum-to-structure). For structure-to-spectrum approaches, the (1) a rule-based heuristic methods work fast and efficient if rules can be learned and expressed for wide ranges of compound classes. A successful example are lipids, where MS/MS spectra are mainly governed by loss of head groups and loss of fatty acyl groups [3] . (2) Machine learning algorithm-based are promising but require a very large set of experimental MS/MS spectra for training and validation sets. Examples such as CFM-ID currently lack predictive accuracy [4] . (3) Quantum chemistry based methods, in principle, offer a solution by applying first-principles in physical chemistry to the problem of breaking chemical bonds and assessing the stability of product ions. While there are published examples and tools, these have not been widely tested or explored. Therefore, this dissertation presents a collection of computational chemistry work ranging from analyzing and evaluating quantum chemistry-based software, performing molecular dynamic simulations, investigating mechanistic details of fragmentation reactions, and developing a computational framework. This framework is now called CIDMD, a quantum chemistry-based structure-to-spectrum approach in computational metabolomics, and the thesis explores its analysis package. With thorough investigations and evaluations, this dissertation offers insights into modeling in silico mass spectra and better understanding towards a better approach and design in the future. Chapter 1 provides a brief introduction to quantum chemistry and describes the foundation of collision-induced dissociation process that governs CID-MS/MS spectra. The current state of MS/MS predictions for compound identification are summarized. Chapter 2 presents the application of the quantum chemistry electron-ionization package, the QCEIMS software developed by Dr. Stefan Grimme, Bonn/Germany. Chapter 2 describes the accuracy of QCEIMS software for predicting EIMS spectra of 80 purines and pyrimidines. Chapter 3 introduces the computational framework CIDMD (collision-induced-dissociation molecular dynamics). CIDMD is a quantum chemistry-based method combined with a structure-to- spectrum approach. Chapter 3 showcases the application of CIDMD to 12 molecules and evaluates the accuracy and information from CIDMD predicted theoretical mass spectra. Specifically, CIDMD predicted fragmentation reaction pathways yielded mechanistic details in MS/MS fragmentations that may lead to future improvements in the software. Importantly, we found that the initial structural protomer used as input into CIDMD calculations may largely affect the resulting CIDMD mass spectra. Consequently, this thesis ends by studying CIDMD fragmentation of protomers in chapter 4 in relation to corresponding experimental MS/MS spectra.