Succinate:ubiquinone oxidoreductase from Paracoccus denitrificans and particulate methane monooxygenase from Methylococcus capsulatus (Bath): experimental and theoretical EPR studies of the metal cofactors

This thesis summarizes the application of conventional and modern electron paramagnetic resonance (EPR) techniques to establish proximity relationships between paramagnetic metal centers in metalloproteins and between metal centers and magnetic ligand nuclei in two important and timely membrane proteins: succinate:ubiquinone oxidoreductase (SQR) from Paracoccus denitrificans and particulate methane monooxygenase (pMMO) from Methylococcus capsulatus. Such proximity relationships are thought to be critical to the biological function and the associated biochemistry mediated by the metal centers in these proteins. A mechanistic understanding of biological function relies heavily on structure-function relationships and the knowledge of how molecular structure and electronic properties of the metal centers influence the reactivity in metalloenzymes. EPR spectroscopy has proven to be one of the most powerful techniques towards obtaining information about interactions between metal centers as well as defining ligand structures. SQR is an electron transport enzyme wherein the substrates, organic and metallic cofactors are held relatively far apart. Here, the proximity relationships of the metallic cofactors were studied through their weak spin-spin interactions by means of EPR power saturation and electron spin-lattice (T_1) measurements, when the enzyme was poised at designated reduction levels. Analysis of the electron T_1 measurements for the S-3 center when the b-heme is paramagnetic led to a detailed analysis of the dipolar interactions and distance determination between two interacting metal centers. Studies of ligand environment of the metal centers by electron spin echo envelope modulation (ESEEM) spectroscopy resulted in the identication of peptide nitrogens as coupled nuclei in the environment of the S-1 and S-3 centers.

Finally, an EPR model was developed to describe the ferromagnetically coupled trinuclear copper clusters in pMMO when the enzyme is oxidized. The Cu(II) ions in these clusters appear to be strongly exchange coupled, and the EPR is consistent with equilateral triangular arrangements of type 2 copper ions. These results offer the first glimpse of the magneto-structural correlations for a trinuclear copper cluster of this type, which, until the work on pMMO, has had no precedent in the metalloprotein literature. Such trinuclear copper clusters are even rare in synthetic models.

Robust Control of Evolutionary Dynamics

The application of principles from evolutionary biology has long been used to gain new insights into the progression and clinical control of both infectious diseases and neoplasms. This iterative evolutionary process consists of expansion, diversification and selection within an adaptive landscape - species are subject to random genetic or epigenetic alterations that result in variations; genetic information is inherited through asexual reproduction and strong selective pressures such as therapeutic intervention can lead to the adaptation and expansion of resistant variants. These principles lie at the center of modern evolutionary synthesis and constitute the primary reasons for the development of resistance and therapeutic failure, but also provide a framework that allows for more effective control.

A model system for studying the evolution of resistance and control of therapeutic failure is the treatment of chronic HIV-1 infection by broadly neutralizing antibody (bNAb) therapy. A relatively recent discovery is that a minority of HIV-infected individuals can produce broadly neutralizing antibodies, that is, antibodies that inhibit infection by many strains of HIV. Passive transfer of human antibodies for the prevention and treatment of HIV-1 infection is increasingly being considered as an alternative to a conventional vaccine. However, recent evolution studies have uncovered that antibody treatment can exert selective pressure on virus that results in the rapid evolution of resistance. In certain cases, complete resistance to an antibody is conferred with a single amino acid substitution on the viral envelope of HIV.

The challenges in uncovering resistance mechanisms and designing effective combination strategies to control evolutionary processes and prevent therapeutic failure apply more broadly. We are motivated by two questions: Can we predict the evolution to resistance by characterizing genetic alterations that contribute to modified phenotypic fitness? Given an evolutionary landscape and a set of candidate therapies, can we computationally synthesize treatment strategies that control evolution to resistance?

To address the first question, we propose a mathematical framework to reason about evolutionary dynamics of HIV from computationally derived Gibbs energy fitness landscapes -- expanding the theoretical concept of an evolutionary landscape originally conceived by Sewall Wright to a computable, quantifiable, multidimensional, structurally defined fitness surface upon which to study complex HIV evolutionary outcomes.

To design combination treatment strategies that control evolution to resistance, we propose a methodology that solves for optimal combinations and concentrations of candidate therapies, and allows for the ability to quantifiably explore tradeoffs in treatment design, such as limiting the number of candidate therapies in the combination, dosage constraints and robustness to error. Our algorithm is based on the application of recent results in optimal control to an HIV evolutionary dynamics model and is constructed from experimentally derived antibody resistant phenotypes and their single antibody pharmacodynamics. This method represents a first step towards integrating principled engineering techniques with an experimentally based mathematical model in the rational design of combination treatment strategies and offers predictive understanding of the effects of combination therapies of evolutionary dynamics and resistance of HIV. Preliminary in vitro studies suggest that the combination antibody therapies predicted by our algorithm can neutralize heterogeneous viral populations despite containing resistant mutations.