Non-catalytic protein-carbohydrate interactions are an essential element of various biological events. This dissertation presents the work on understanding carbohydrate recognition mechanisms and their physical significance in two groups of non-catalytic proteins, also called lectins, which play key roles in major applications such as cellulosic biofuel production and drug delivery pathways. A computational approach using molecular modeling, molecular dynamic simulations and free energy calculations was used to study molecular-level protein-carbohydrate and protein-protein interactions. Various microorganisms like bacteria and fungi secret multi-modular enzymes to deconstruct cellulosic biomass into fermentable sugars. The carbohydrate binding modules (CBM) are non-catalytic domains of such enzymes that assist the catalytic domains to recognize the target substrate and keep it in proximity. Understanding the protein-carbohydrate recognition mechanisms by which CBMs selectively bind substrate is critical to development of enhanced biomass conversion technology. We focus on CBMs that target both oligomeric and non-crystalline cellulose while exhibiting various similarities and differences in binding specificity and structural properties; such CBMs are classified as Type B CBMs. We show that all six cellulose-specific Type B CBMs studied in this dissertation can recognize the cello-oligomeric ligands in bi-directional fashion, meaning there was no preference towards reducing or non-reducing end of ligand for the cleft/groove like binding sites. Out of the two sandwich and twisted forms of binding site architectures, twisted platform turned out to facilitate tighter binding also exhibiting longer binding sites. The exterior loops of such binding sites were specifically identified by modeling the CBMs with non-crystalline cellulose showing that high- and low-affinity binding site may arise based on orientation of CBM while interacting with non-crystalline substrate. These findings provide various insights that can be used for further understanding of tandem CBMs and for various CBM based biotechnological applications. The later part of this dissertation reports the identification of a physiological ligand for a mammalian glycoprotein YKL-40 that has been only known as a biomarker in various inflammatory diseases and cancers. It has been shown to bind to oligomers of chitin, but there is no known function of YKL-40, as chitin production in the human body has never been reported. Possible alternative ligands include proteoglycans, polysaccharides, and fibers such as collagen, all of which make up the mesh comprising the extracellular matrix. It is likely that YKL-40 is interacting with these alternative polysaccharides or proteins within the body, extending its function to cell biological roles such as mediating cellular receptors and cell adhesion and migration. We considered the feasibility of polysaccharides, including cello-oligosaccharides, hyaluronan, heparan sulfate, heparin, and chondroitin sulfate, and collagen-like peptides as physiological ligands for YKL-40. Our simulation results suggest that chitohexaose and hyaluronan preferentially bind to YKL-40 over collagen, and hyaluronan is likely the preferred physiological ligand, as the negatively charged hyaluronan shows enhanced affinity for YKL-40 over neutral chitohexaose. Collagen binds in two locations at the YKL-40 surface, potentially related to a role in fibrillar formation. Finally, heparin non- specifically binds at the YKL-40 surface, as predicted from structural studies. Overall, YKL-40 likely binds many natural ligands in vivo, but its concurrence with physical maladies may be related to the associated increases in hyaluronan.

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that have received great interest since they were first synthesized in the late 1990s. Practical applications of MOFs are continuously being discovered as a better understanding of the properties of materials adsorbed within the nanopores of MOFs emerges. One such potential application is as a component of an explosive-sensing system. Another potential application is for hydrogen storage. This work is focused on tailoring MOFs to adsorb/desorb the explosive, RDX. Classical grand canonical Monte Carlo (GCMC) and molecular dynamic (MD) simulations have been performed to calculate adsorption isotherms and self-diffusivities of RDX in several IRMOFs. Because gathering experimental data on explosive compounds is dangerous, data is limited. Simulation can in part fill the gap of missing information. Through these simulations, many of the key issues associated with MOFs preconcentrating RDX have been resolved. The issues include both theoretical issues associated with the computational generation of properties and practical issues associated with the use of MOFs in explosive-sensing system. Theoretically, we evaluate the method for generating partial charges for MOFs and the impact of this choice on the adsorption isotherm and diffusivity. Practically, we show that the tailoring of an MOF with a polar group like an amine can lead to an adsorbent that (i) concentrates RDX from the bulk by as much as a factor of 3000, (ii) is highly selective for RDX, and (iii) retains sufficient RDX mobility allowing for rapid, real time sensing. Many of the impediments to the effective explosive detection can be framed as shortcomings in the understanding of molecule surface interactions. A fundamental, molecular-level understanding of the interaction between explosives and functionalized MOFs would provide the necessary guidance that allows the next generation of sensors to be developed. This is one of the main driving forces behind this dissertation. Another important achievement in this work is the demonstration of a new direction for tailoring MOFs. A new class of tailored MOFs containing porphyrins has been proposed. These tailored MOFs show greater capability for hydrogen storage, which also demonstrated the great functionalization of MOFs and great potential to serve as preconcentrators. The use of a novel multiscale modeling technique to develop equations of state for inhomogeneous fluids is included as a supplement to this dissertation.

Many scientists and engineers are turning to lab-on-a-chip systems for cheaper and high throughput analysis of chemical reactions and biomolecular interactions. In this work, we developed several lab-on-a-chip modules based on novel manipulations of individual microbeads inside microchannels. The first manipulation method employs arrays of soft ferromagnetic patterns fabricated inside a microfluidic channel and subjected to an external rotating magnetic field. We demonstrated that the system can be used to assemble individual beads (1-3µm) from a flow of suspended beads into a regular array on the chip, hence improving the integrated electrochemical detection of biomolecules bound to the bead surface. In addition, the microbeads can follow the external magnet rotating at very high speeds and simultaneously orbit around individual soft magnets on the chip. We employed this manipulation mode for efficient sample mixing in continuous microflow. Furthermore, we discovered a simple but effective way of transporting the microbeads on-chip in the rotating field. Selective transport of microbeads with different size was also realized, providing a platform for effective sample separation on a chip. The second manipulation method integrates magnetic and dielectrophoretic manipulations of the same microbeads. The device combines tapered conducting wires and fingered electrodes to generate desirable magnetic and electric fields, respectively. By externally programming the magnetic attraction and dielectrophoretic repulsion forces, out-of-plane oscillation of the microbeads across the channel height was realized. Furthermore, we demonstrated the tweezing of microbeads in liquid with high spatial resolutions by fine-tuning the net force from magnetic attraction and dielectrophoretic repulsion of the beads. The high-resolution control of the out-of-plane motion of the microbeads has led to the invention of massively parallel biomolecular tweezers.

Molecular dynamics (MD) is a powerful computer simulation technique providing atomistic resolution across a broad range of time scales. In the past four decades, researchers have harnessed the exponential growth in computer power and applied it to the simulation of diverse molecular systems. Although MD simulations are playing an increasingly important role in biomedical research, sampling limitations imposed by both hardware and software constraints establish a \textit{de facto} upper bound on the size and length of MD trajectories. While simulations are currently approaching the hundred-thousand-atom, millisecond-timescale mark using large-scale computing centres optimized for general-purpose data processing, many interesting research topics are still beyond the reach of practical computational biophysics efforts. The purpose of this work is to design a high-speed MD machine which outperforms standard simulators running on commodity hardware or on large computing clusters. In pursuance of this goal, an MD-specific computer architecture is developed which tightly couples the fast processing power of Field-Programmable Gate Array (FPGA) computer chips with a network of high-performance CPUs. The development of this architecture is a multi-phase approach. Core MD algorithms are first analyzed and deconstructed to identify the computational bottlenecks governing the simulation rate. High-speed, parallel algorithms are subsequently developed to perform the most time-critical components in MD simulations on specialized hardware much faster than is possible with general-purpose processors. Finally, the functionality of the hardware accelerators is expanded into a fully-featured MD simulator through the integration of novel parallel algorithms running on a network of CPUs. The developed architecture enabled the construction of various prototype machines running on a variety of hardware platforms which are explored throughout this thesis. Furthermore, simulation models are developed to predict the rate of acceleration using different architectural configurations and molecular systems. With initial acceleration efforts focused primarily on expensive van der Waals and Coulombic force calculations, an architecture was developed whereby a single machine achieves the performance equivalent of an 88-core InfiniBand-connected network of CPUs. Finally, a methodology to successively identify and accelerate the remaining time-critical aspects of MD simulations is developed. This design leads to an architecture with a projected performance equivalent of nearly 150 CPU-cores, enabling supercomputing performance in a single computer chassis, plugged into a standard wall socket.

Molecular dynamics (MD) is a powerful computer simulation technique providing atomistic resolution across a broad range of time scales. In the past four decades, researchers have harnessed the exponential growth in computer power and applied it to the simulation of diverse molecular systems. Although MD simulations are playing an increasingly important role in biomedical research, sampling limitations imposed by both hardware and software constraints establish a \textit{de facto} upper bound on the size and length of MD trajectories. While simulations are currently approaching the hundred-thousand-atom, millisecond-timescale mark using large-scale computing centres optimized for general-purpose data processing, many interesting research topics are still beyond the reach of practical computational biophysics efforts. The purpose of this work is to design a high-speed MD machine which outperforms standard simulators running on commodity hardware or on large computing clusters. In pursuance of this goal, an MD-specific computer architecture is developed which tightly couples the fast processing power of Field-Programmable Gate Array (FPGA) computer chips with a network of high-performance CPUs. The development of this architecture is a multi-phase approach. Core MD algorithms are first analyzed and deconstructed to identify the computational bottlenecks governing the simulation rate. High-speed, parallel algorithms are subsequently developed to perform the most time-critical components in MD simulations on specialized hardware much faster than is possible with general-purpose processors. Finally, the functionality of the hardware accelerators is expanded into a fully-featured MD simulator through the integration of novel parallel algorithms running on a network of CPUs. The developed architecture enabled the construction of various prototype machines running on a variety of hardware platforms which are explored throughout this thesis. Furthermore, simulation models are developed to predict the rate of acceleration using different architectural configurations and molecular systems. With initial acceleration efforts focused primarily on expensive van der Waals and Coulombic force calculations, an architecture was developed whereby a single machine achieves the performance equivalent of an 88-core InfiniBand-connected network of CPUs. Finally, a methodology to successively identify and accelerate the remaining time-critical aspects of MD simulations is developed. This design leads to an architecture with a projected performance equivalent of nearly 150 CPU-cores, enabling supercomputing performance in a single computer chassis, plugged into a standard wall socket.

Oral mucositis is a painful and debilitating chronic inflammatory condition that can result from chemo and/or radiotherapy. While current treatment strategies which provide temporary relief exist, there is still an unmet clinical need for a robust long active barrier strategy which can simultaneously provide protection and release drug to enhance the wound healing response. It is proposed that an affinity based layer-by-layer self-assembled barrier administered as a series of mouth rinses can allow for wound specific drug delivery, providing an effective regenerative therapy. In this work, biotinylated poly(acrylic acid) is used to develop LBL assemblies based upon biotin-streptavidin affinity interactions. To explore the ability of developed LBL assemblies to resist the harsh intraoral environment, in vitro chemical and ex vivo mechanical tests are performed. The stability results demonstrate significant LBL barrier stability with wear resistance. From principal component regression analysis, factors such as polymer MW and number of layers in assemblies contributed significantly to chemical barrier stability. Also it is observed that the extent of biotin conjugation plays a significant role in LBL development and in mechanical stability. Thus, the proposed affinity based multilayered assemblies with their excellent barrier properties offer a modular treatment approach in oral mucosal injuries.

X-ray crystallography has been around for 100 years and remains the preferred technique for solving molecular structures today. However, its reliance on the production of sufficiently large crystals is limiting, considering that crystallization cannot be achieved for a vast range of biomolecules. A promising way of circumventing this problem is the method of serial femtosecond imaging of single-molecules or nanocrystals utilizing an X-ray free-electron laser. In such an approach, X-ray pulses brief enough to outrun radiation damage and intense enough to provide usable diffraction signals are employed. This way accurate snapshots can be collected one at a time, despite the sample molecule exploding immediately following the pulse due to extreme ionization. But as opposed to in conventional crystallography, the spatial orientation of the molecule at the time of X-ray exposure is generally unknown. Consequentially, assembling the snapshots to form a three-dimensional representation of the structure of interest is cumbersome, and normally tackled using algorithms to analyze the diffraction patterns. Here we explore the idea that the explosion data can provide useful insights regarding the orientation of ubiquitin, a eukaryotic regulatory protein. Through two series of molecular dynamics simulations totaling 588 unique explosions, we found that a majority of the carbon atoms prevalent in ubiquitin are directionally limited in their respective escape paths. As such we conclude it to be theoretically possible to orient a sample with known structure based on its explosion pattern. Working with an unknown sample, we suggest these discoveries could be applicable in tandem with X-ray diffraction data to optimize image assembly.

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Thesis (Ph. D.) -- University of Texas Southwestern Medical Center at Dallas, 2005. / Vita. Bibliography: 209-224.