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  1. Understanding antibiotic resistance in bacteria through molecular dynamics simulations [electronic resource]

    Vanatta, Dana K.
    2015.

    An atomic level understanding of how biological molecules and proteins function is an ongoing challenge in chemical biology. Spectroscopic methods are useful for providing information about stable states and overall transition processes but it is impossible to directly observe these processes at an atomistic level of detail. Here, computational techniques are used to supplement experimental measurements in order to provide a more complete picture of a bacterial resistance to antibiotics at the atomic scale. Markov Models are used to analyze Molecular Dynamics simulations in order to propose an activation pathway for the conformational change in a key bacterial signaling protein NtrC. Similar techniques are applied again, in combination with a novel clustering algorithm, to compute the binding affinity of vancomycin to its targets. In sum, the present work demonstrates how simulations can contribute to a better understanding of important biological systems.

  2. Advancing computational prediction of RNA structures and dynamics [electronic resource]

    Chou, Fang-Chieh
    2015.

    RNA plays critical roles in fundamental biological processes, including transcription, translation, post-transcriptional regulation of genetic expression, and catalysis as enzymes. These critical RNA functions are determined by the structures and dynamics of the RNA molecules. Computational methods might be used to predict the structures and dynamics of RNA. Unfortunately, the prediction accuracies of current computational methods are still inferior compared to experiments. In this dissertation, I discuss recent advances I made in improving and developing computational methods to make accurate predictions on the RNA structures and dynamics. The dissertation contains three individual research projects. In the first part, I present a protocol for Enumerative Real-space Refinement ASsisted by Electron density under Rosetta (ERRASER). ERRASER combined RNA structure prediction algorithm with experimental constraints from crystallography, to correct the pervasive ambiguities in RNA crystal structures. On 24 RNA crystallographic datasets, ERRASER corrects the majority of steric clashes and anomalous backbone geometries, improves the average Rfree by 0.014, resolves functionally important structural discrepancies, and refines low-resolution structures to better match higher resolution structures. In the second part, I present HelixMC, a package for simulating kilobase-length double-stranded DNA and RNA (dsDNA and dsRNA) under external forces and torques, which is typical in single-molecule tweezers experiments. It recovered the experimental bending persistence length of dsRNA within the error of the simulations and accurately predicted that dsRNA's "spring-like" conformation would give a two-fold decrease of stretch modulus relative to dsDNA. In the third part, I developed a framework of Reweighting of Energy-function Collection with Conformational Ensemble Sampling (RECCES), to predict the folding free energies of RNA duplexes. With efficient sampling and reweighting, RECCES allows comprehensive exploration of the prediction power of Rosetta energy function, and provides a powerful platform for testing future improvement of the energy function. In all the projects above, I leveraged rich datasets from previous experiments to develop novel algorithms that gave predictions with unprecedented accuracies, which were validated by independent blind tests. These computational methods I developed could also serve as a solid foundation for future efforts of improving prediction accuracies of RNA computational algorithms.

  3. Computational modeling of CO₂ electrocatalysis on surfaces and interfaces towards C₂ products

    Sandberg, Robert
    [Stanford, California] : [Stanford University], 2019.

    Atmospheric carbon dioxide (CO2) concentrations have continually increased to levels that far exceed pre-industrialization, largely due to our dependence on fossil fuels as a source of energy. This demands a renewable and clean source of energy for energy production; the sun is the largest resource at our disposal. The cost of electricity from renewable sources has seen a decline in recent years, leaving an open question of energy storage. To that end, we would like to take nature's design and use this renewable energy to produce solar fuels, similar to the natural process of photosynthesis. Ideally, we would like to reduce CO2 to valuable hydrocarbon products, especially to C2 compounds, which are more energy dense. Decades of research on the electrochemical CO2 reduction reaction (CO2RR) has showed that Cu is the only metal catalyst that can produce a variety of hydrocarbon products but at a low efficiency. Since these early findings, there have been many experimental and theoretical studies to both understand the catalysis on simple transition metal (TM) surfaces and design improved catalysts through a variety of techniques. This thesis is composed of 7 chapters. Chapter 1 is an introduction and details the current energy problem our planet is facing, a potential solution to this problem in the CO2RR, and prior research in CO2RR. Chapter 2 is the methodology and details the methods employed in this work. This includes density functional theory (DFT) to obtain energies of various adsorbed species during catalysis and analysis of this data using methods such as scaling relations and the computational hydrogen electrode. Chapter 3 involves calculations of C-C coupling barriers on various Cu facets at the electrochemical interface, including effects of electric fields, coverage effects, and strain effects. Chapter 4 uses some of these calculations as well as free energy diagrams to elucidate C-C coupling pathways on three facets of Cu: (100), (111), and (211). From our calculations, we advised our experimental collaborators to preferentially expose the (100) facet of Cu nanocubes, which showed an enhanced activity and selectivity towards C2 products. Chapter 5 includes some of these calculations as well as other barriers in the pathways to C2 compounds. These calculations are combined into a microkinetic model to predict reaction rates and compare well with experimental results. After gaining insight into C-C coupling on Cu, the best-known transition metal catalyst, chapter 6 then explores the metal-oxide

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