Computational biophysics is a powerful tool for observing and understanding the microscopic machinery that underpins the biological world. Molecular modeling and simulations can help scientists understand a cell’s behavior and the mechanisms that drive it. Empirical evidence can provide information on the structure and organization of biomolecular machines, which serve as the backbone of biomolecular modeling. Experimental data from probing the cell’s inner workings can provide modelers with an initial structure from which they can hypothesize and independently verify function, complex formation, and response. Additionally, molecular data can be used to drive simulations toward less probable but equally interesting states. With the advent of machine learning, researchers now have an unprecedented opportunity to take advantage of the wealth of data collected in a biomolecular experiment. This dissertation presents a comprehensive review of atomistic modeling with cryo-electron microscopy and the development of new simulation strategies to maximize insights gained from experiments. The review covers the integration of cryo-EM and molecular dynamics, highlighting the evolution of their relationship and the recent history of MD innovations in cryo-EM modeling. It also covers the discoveries made possible by the integration of molecular modeling with cryo-EM. Next, this work presents a method for fitting small molecules into cryo-electron microscopy maps, which uses neural network potentials to parameterize a diverse set of ligands. The method obtained fitted structures commensurate with, if not better than, the structures submitted to the Protein Data Bank. Additionally, the work describes the data-guided Multi- Map methodology for ensemble refinement of molecular movies. The method shows that cryo-electron microscopy maps can be used to bias simulations along a specially constructed reaction coordinate and capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. Finally, the study analyzes the SARS-CoV-2 spike protein and the conformational heterogeneity of its receptor binding domain. The simulation was guided along an experimentally determined free energy landscape. The resulting motions from following a pathway of low-energy states show a degree of openness not observed in the static models. This sheds light on the mechanism by which the spike protein is utilized for host infection and provides a rational explanation for the effectiveness of certain therapeutics. This work contributes to the understanding of biomolecular modeling and the development of new strategies to provide valuable insights into the workings of cellular machinery.

Asphalt is a widely used mixture in the paving and roofing industries and its sales are expected to increase by 57% in the next eight years alone (Mandaokar, 2024). However, it is known to have highly toxic constituents such as benzo[a]pyrene (BaP) and catechol, (National Institute, 1977, Hazard Review, 2000, Neghab et al., 2015, and Rozewski et al., 2023). Lemon juice, which is an inexpensive and easily accessible natural substance that is shown to have health benefits such as increasing insulin sensitivity, aiding with weight loss, and preventing heart disease (Tejpal et al., 2020), may counteract the effects of asphalt. The question of what the biological effects of asphalt, lemon juice, and the combination of the two on adipocytes was tested via computational analysis and experiments. It was predicted that catechol and lemon juice components will show biological effects in adipocytes that could be opposing, additive, or synergistic. A computational analysis involving the docking of fourteen components of asphalt and thirty-five components of lemon juice constituents to a targetome of 7,529 proteins (Ovanessians et al., 2024) suggests that asphalt and lemon juice components have many possible protein targets. Experiments were carried out with 3T3L1 mouse adipocytes to study five different lemon extracts (crude, hexane organic and aqueous, and ether organic and aqueous), and two components of asphalt (catechol and BaP): 1) Thiazolyl Blue Tetrazolium Bromide (MTT) cell viability and toxicity assay, 2) reactive oxygen species fluorescence assay, 3) Nile red staining assay, 4) red oil o staining assay, and a 5) lipidomics analysis on the hexane and ether organic extracts of lemon juice. This study has shown that asphalt components BaP and catechol and lemon juice components combined have the following biological effects on adipocytes: 1) Of the 5 lemon extracts tested, the organic layer of the hexane extract solubilized in DMSO (LE4) decreases differentiation the most. 2) Nile red staining is inhibited by 0.1 mg/mL of LE4, 1 µM BaP, and 20 µM catechol at a similar level. 3) Cell morphology differs between LE4, BaP, and catechol. Future work will include an insulin sensitivity assay to confirm the indicative inhibitory relationship found between lemon juice and asphalt. Expanding upon the lipidomic results of the lemon juices, as well as maximizing the potential of dockings by connecting results with the experiments, may also prove to be useful in future studies.

The FOF1 ATP synthase is responsible for generating the majority of adenosine triphosphate (ATP) in almost all organisms on Earth. A major unresolved question is the mechanism of the FO motor that converts the transmembrane flow of protons into rotation that drives ATP synthesis. Using single-molecule gold nanorod experiments, rotation of individual FOF1 were observed to measure transient dwells (TDs). TDs occur when the FO momentarily halts the ATP hydrolysis rotation by the F1-ATPase. The work presented here showed increasing TDs with decreasing pH, with calculated pKa values of 5.6 and 7.5 for wild-type (WT) Escherichia coli (E. coli) subunit-a proton input and output half-channels, respectively. This is consistent with the conclusion that the periplasmic proton half-channel is more easily protonated than the cytoplasmic half-channel. Mutation in one proton half-channel affected the pKa values of both half-channels, suggesting that protons flow through the FO motor via the Grotthuss mechanism. The data revealed that 36° stepping of the E. coli FO subunit-c ring during ATP synthesis consists of an 11° step caused by proton translocations between subunit-a and the c-ring, and a 25° step caused by the electrostatic interaction between the unprotonated c-subunit and the aR210 residue in subunit-a. The occurrence of TDs fit to the sum of three Gaussian curves, which suggested that the asymmetry between the FO and F1 motors play a role in the mechanism behind the FOF1 rotation. Replacing the inner (N-terminal) helix of E. coli c10-ring with sequences derived from c8 to c17-ring sequences showed expression and full assembly of FOF1. Decrease in anticipated c-ring size resulted in increased ATP synthesis activity, while increase in c-ring size resulted in decreased ATP synthesis activity, loss of Δψ-dependence to synthesize ATP, decreased ATP hydrolysis activity, and decreased ACMA quenching activity. Low levels of ATP synthesis by the c12 and c15-ring chimeras are consistent with the role of the asymmetry between the FO and F1 motors that affects ATP synthesis rotation. Lack of a major trend in succinate-dependent growth rates of the chimeric E. coli suggest cellular mechanisms that compensates for the c-ring modification.

Secondary active transporters play significant roles in maintaining living cells' homeostasis by utilizing the electrochemical gradient in driving ions or protons as the source of free energy to transport substrate through biological membranes.A broadly recognized molecular framework, the alternating access model, describes the transport mechanism as the transporter undergoes conformational changes between different conformations and alternatingly exposes its binding site to intracellular and extracellular sides and, thus, exchanges ion and substrate in a cyclical manner. Recent progress in structural biology brought the first-ever structural insights into the mammalian Cation-Proton Antiporters (CPA) family of proteins. However, the dynamic atomic-level information about the interactions between the newly discovered structures and the bound ion or the corresponding substrate remains unknown. With Molecular Dynamics (MD), multiple spontaneous ion binding events were observed in the equilibrium simulations, revealing the binding site topology of Horse Sodium-Proton Exchanger 9 (NHE9) and Bison Sodium-Proton Antiporter 2 (NHA2) in their preferred protonation state. Further investigation into more CPA homologs compared various aspects, including sequence identity, binding site topology, and energetic properties, and obtained general insights into the similarities shared by the binding process of CPA members. The putative binding site and other conserved residues in their actively ion-bound poses were identified for each model, and their similarities were compared. The energetic properties accessed by the three-dimensional free energy profile, initially found to be binding unfavorable for the experimental structures, were recalculated based on the simulation data. The updated results show consistency with the correct binding affinity as indicated by the experimental methods. This work provided a general picture of the structures and the ion-protein interaction of CPA proteins and serves as comprehensive guidance for any related future structural and computational work.

Computational models have long been used to describe and predict the outcome of complex immunological processes. The dissertation work described here centers on the construction of multiscale computational immunology models that derives biological insights at the population, systems, and atomistic levels. First, SARS-CoV-2 mortality is investigated through the lens of the predicted robustness of CD8+ T cell responses in 23 different populations. The robustness of CD8+ T cell responses in a given population was modeled by predicting the efficiency of endemic MHC-I protein variants to present peptides derived from SARS-CoV-2 proteins to circulating T cells. To accomplish this task, an algorithm, called EnsembleMHC, was developed to predict viral peptides with a high probability of being recognized by CD T cells. It was discovered that there was significant variation in the efficiency of different MHC-I protein variants to present SARS-CoV-2 derived peptides, and countries enriched with variants with high presentation efficiency had significantly lower mortality rates. Second, a biophysics-based MHC-I peptide prediction algorithm was developed. The MHC-I protein is the most polymorphic protein in the human genome with polymorphisms in the peptide binding causing striking changes in the amino acid compositions, or binding motifs, of peptide species capable of stable binding. A deep learning model, coined HLA-Inception, was trained to predict peptide binding using only biophysical properties, namely electrostatic potential. HLA-Inception was shown to be extremely accurate and efficient at predicting peptide binding motifs and was used to determine the peptide binding motifs of 5,821 MHC-I protein variants. Finally, the impact of stalk glycosylations on NL63 protein dynamics was investigated. Previous data has shown that coronavirus crown glycans play an important role in immune evasion and receptor binding, however, little is known about the role of the stalk glycans. Through the integration of computational biology, experimental data, and physics-based simulations, the stalk glycans were shown to heavily influence the bending angle of spike protein, with a particular emphasis on the glycan at position 1242. Further investigation revealed that removal of the N1242 glycan significantly reduced infectivity, highlighting a new potential therapeutic target. Overall, these investigations and associated innovations in integrative modeling.

Barnase-Barstar is a protein complex that has a strong association constant. The purpose of this research is to investigate the effects of conformational fluctuations on protein-water interactions, resulting water-mediated interactions, and the binding free energy of the protein complex. Using all-atom molecular dynamics simulations, the sets of simulations for flexible and rigid proteins to identify the effects on water-mediated interactions were prepared for analysis. To analyze the properties and interactions that result in the strong association of the Barnase-Barstar protein complex, the molecular dynamics simulations were prepared. A thorough review of the GROMACS manual and completion of the GROMACS Lysozyme in Water tutorial was completed to understand the steps and commands to write and run molecular dynamics simulations. The preliminary data investigated the impact of water-mediated interactions on the solvation free energy in the Barnase-Barstar protein complex where the proteins are kept rigid. This was achieved by observing the change in solvation free energy with respect to separation distance. From the data obtained, it is concluded that solvent-mediated interactions do not contribute to the negative binding free energy. With increasing separation distance, the change in solvation free energy decreased. Therefore, thermodynamically, water-mediated interactions destabilize the protein complex, while the binding free energy is dominated by direct protein-protein interactions. The follow-up simulations of flexible proteins with controlled protein-protein separation distances, for which a fully automated simulation and analysis protocol has been prepared in this project, will allow us to quantify the impact of conformational fluctuations on water-mediated interactions and the binding free energy of the protein complex by comparison to the simulations of rigid proteins.

Increasing energy and environmental problems describe the need to develop renewable chemicals and fuels. Global research has been targeting using microbial systems on a commercial scale for synthesis of valuable compounds. The goal of this project was to refactor and overexpress b6-f complex proteins in cyanobacteria to improve photosynthesis under dynamic light conditions. Improvement in the photosynthetic system can directly relate to higher yields of valuable compounds such as carotenoids and higher yields of biomass which can be used as energy molecules. Four engineered strains of cyanobacteria were successfully constructed and overexpressed the corresponding four large subunits in the cytochrome b6-f complex. No significant changes were found in cell growth or pigment titer in the modified strains compared to the wild type. The growth assay will be performed at higher and/or dynamic light intensities including natural light conditions for further analysis.