Structural and Biochemical Dissection of the Trehalose Biosynthetic Complex in Pathogenic Fungi

Trehalose is a non-reducing disaccharide essential for pathogenic fungal survival and virulence. The biosynthesis of trehalose requires the trehalose-6-phosphate synthase, Tps1, and trehalose-6-phosphate phosphatase, Tps2. More importantly, the trehalose biosynthetic pathway is absent in mammals, conferring this pathway as an ideal target for antifungal drug design. However, lack of germane biochemical and structural information hinders antifungal drug design against these targets.

In this dissertation, macromolecular X-ray crystallography and biochemical assays were employed to understand the structures and functions of proteins involved in the trehalose biosynthetic pathway. I report here the first eukaryotic Tps1 structures from Candida albicans (C. albicans) and Aspergillus fumigatus (A. fumigatus) with substrates or substrate analogs. These structures reveal the key residues involved in substrate binding and catalysis. Subsequent enzymatic assays and cellular assays highlight the significance of these key Tps1 residues in enzyme function and fungal stress response. The Tps1 structure captured in its transition-state with a non-hydrolysable inhibitor demonstrates that Tps1 adopts an “internal return like” mechanism for catalysis. Furthermore, disruption of the trehalose biosynthetic complex formation through abolishing Tps1 dimerization reveals that complex formation has regulatory function in addition to trehalose production, providing additional targets for antifungal drug intervention.

I also present here the structure of the Tps2 N-terminal domain (Tps2NTD) from C. albicans, which may be involved in the proper formation of the trehalose biosynthetic complex. Deletion of the Tps2NTD results in a temperature sensitive phenotype. Further, I describe in this dissertation the structures of the Tps2 phosphatase domain (Tps2PD) from C. albicans, A. fumigatus and Cryptococcus neoformans (C. neoformans) in multiple conformational states. The structures of the C. albicans Tps2PD -BeF3-trehalose complex and C. neoformans Tps2PD(D24N)-T6P complex reveal extensive interactions between both glucose moieties of the trehalose involving all eight hydroxyl groups and multiple residues of both the cap and core domains of Tps2PD. These structures also reveal that steric hindrance is a key underlying factor for the exquisite substrate specificity of Tps2PD. In addition, the structures of Tps2PD in the open conformation provide direct visualization of the conformational changes of this domain that are effected by substrate binding and product release.

Last, I present the structure of the C. albicans trehalose synthase regulatory protein (Tps3) pseudo-phosphatase domain (Tps3PPD) structure. Tps3PPD adopts a haloacid dehydrogenase superfamily (HADSF) phosphatase fold with a core Rossmann-fold domain and a α/β fold cap domain. Despite lack of phosphatase activity, the cleft between the Tps3PPD core domain and cap domain presents a binding pocket for a yet uncharacterized ligand. Identification of this ligand could reveal the cellular function of Tps3 and any interconnection of the trehalose biosynthetic pathway with other cellular metabolic pathways.

Combined, these structures together with significant biochemical analyses advance our understanding of the proteins responsible for trehalose biosynthesis. These structures are ready to be exploited to rationally design or optimize inhibitors of the trehalose biosynthetic pathway enzymes. Hence, the work described in this thesis has laid the groundwork for the design of Tps1 and Tps2 specific inhibitors, which ultimately could lead to novel therapeutics to treat fungal infections.

Coal-fired Power Plants with Flexible Amine-based CCS and Co-located Wind Power: Environmental, Economic and Reliability Outcomes

Carbon Capture and Storage (CCS) technologies provide a means to significantly reduce carbon emissions from the existing fleet of fossil-fired plants, and hence can facilitate a gradual transition from conventional to more sustainable sources of electric power. This is especially relevant for coal plants that have a CO2 emission rate that is roughly two times higher than that of natural gas plants. Of the different kinds of CCS technology available, post-combustion amine based CCS is the best developed and hence more suitable for retrofitting an existing coal plant. The high costs from operating CCS could be reduced by enabling flexible operation through amine storage or allowing partial capture of CO2 during high electricity prices. This flexibility is also found to improve the power plant’s ramp capability, enabling it to offset the intermittency of renewable power sources. This thesis proposes a solution to problems associated with two promising technologies for decarbonizing the electric power system: the high costs of the energy penalty of CCS, and the intermittency and non-dispatchability of wind power. It explores the economic and technical feasibility of a hybrid system consisting of a coal plant retrofitted with a post-combustion-amine based CCS system equipped with the option to perform partial capture or amine storage, and a co-located wind farm. A techno-economic assessment of the performance of the hybrid system is carried out both from the perspective of the stakeholders (utility owners, investors, etc.) as well as that of the power system operator.

In order to perform the assessment from the perspective of the facility owners (e.g., electric power utilities, independent power producers), an optimal design and operating strategy of the hybrid system is determined for both the amine storage and partial capture configurations. A linear optimization model is developed to determine the optimal component sizes for the hybrid system and capture rates while meeting constraints on annual average emission targets of CO2, and variability of the combined power output. Results indicate that there are economic benefits of flexible operation relative to conventional CCS, and demonstrate that the hybrid system could operate as an energy storage system: providing an effective pathway for wind power integration as well as a mechanism to mute the variability of intermittent wind power.

In order to assess the performance of the hybrid system from the perspective of the system operator, a modified Unit Commitment/ Economic Dispatch model is built to consider and represent the techno-economic aspects of operation of the hybrid system within a power grid. The hybrid system is found to be effective in helping the power system meet an average CO2 emissions limit equivalent to the CO2 emission rate of a state-of-the-art natural gas plant, and to reduce power system operation costs and number of instances and magnitude of energy and reserve scarcity.

Path Optimization in Free Energy Calculations

Free energy calculations are a computational method for determining thermodynamic quantities, such as free energies of binding, via simulation.

Currently, due to computational and algorithmic limitations, free energy calculations are limited in scope.

In this work, we propose two methods for improving the efficiency of free energy calculations.

First, we expand the state space of alchemical intermediates, and show that this expansion enables us to calculate free energies along lower variance paths.

We use Q-learning, a reinforcement learning technique, to discover and optimize paths at low computational cost.

Second, we reduce the cost of sampling along a given path by using sequential Monte Carlo samplers.

We develop a new free energy estimator, pCrooks (pairwise Crooks), a variant on the Crooks fluctuation theorem (CFT), which enables decomposition of the variance of the free energy estimate for discrete paths, while retaining beneficial characteristics of CFT.

Combining these two advancements, we show that for some test models, optimal expanded-space paths have a nearly 80% reduction in variance relative to the standard path.

Additionally, our free energy estimator converges at a more consistent rate and on average 1.8 times faster when we enable path searching, even when the cost of path discovery and refinement is considered.