Rozprawy doktorskie na temat „Theoreical and Computational Chemistry”

QC 20130531

Enzymes play a crucial role in modern biotechnology, industry, food processing and medical applications. Since their first discovered industrial use, man has attempted to discover new enzymes from Nature to catalyze different chemical reactions. In modern times, with the advent of computational methods, protein structure solutions, protein sequencing and DNA synthesis methods, we now have the tools to enable new approaches to rational enzyme engineering. With an enzyme structure in hand, a researcher may run an in silico experiment to sample different amino acids in the active site in order to identify new combinations which likely stabilize a transition-state-enzyme model. A suggested mutation can then be encoded into the desired enzyme gene, ordered, synthesized and tested. Although this truly astonishing feat of engineering and modern biotechnology allows the redesign of existing enzymes to acquire a new substrate specificity, it still requires a large amount of time, capital and technical capabilities.

Concurrently, while making strides in computational protein design, the cost of sequencing DNA plummeted after the turn of the century. With the reduced cost of sequencing, the number of sequences in public databases of naturally occurring proteins has grown exponentially. This new, large source of information can be utilized to enable rational enzyme design, as long as it can be coupled with accurate modeling of the protein sequences.

This work first describes a novel approach to reengineering enzymes (Genome Enzyme Orthologue Mining; GEO) that utilizes the vast amount of protein sequences in modern databases along with extensive computation modeling and achieves comparable results to the state-of-the-art rational enzyme design methods. Then, inspired by the success of this new method and aware of it's reliance on the accuracy of the protein models, we created a computational benchmark to both measure the accuracy of our models as well as improve it by encoding additional information about the structure, derived from mechanistic studies (Catalytic Geometry constraints; CG). Lastly, we use the improved accuracy method to automatically model hundreds of putative enzymes sequences and dock substrates into them to extract important features that are then used to inform experiments and design. This is used to reengineer a ribonucleotide reductase to catalyze a aldehyde deformylating oxygenase reaction.

These chapters advance the field of rational enzyme engineering, by providing a novel technique that may enable efficient routes to rationally design enzymes for reactions of interest. These chapters also advance the field of homology modeling, in the specific domain in which the researcher is modeling an enzyme with a known chemical reaction. Lastly, these chapters and techniques lead to an example which utilizes highly accurate computational models to create features which can help guide the rational design of enzyme catalysts.

This dissertation contains investigations based on Molecular Dynamics (MD) of a variety of systems, from small atomic clusters to polymers in solution and in their condensed phases. The overall research is divided in three parts. First, I tested a new thermostat in the literature on the thermal equilibration of a small cluster of Lennard-Jones (LJ) atoms. The proposed thermostat is a Hamiltonian thermostat based on a logarithmic oscillator with the outstanding property that the mean value of its kinetic energy is constant independent of the mass and energy. I inspected several weak-coupling interaction models between the LJ cluster and the logarithmic oscillator in 3D. In all cases I show that this coupling gives rise to a kinetic motion of the cluster center of mass without transferring kinetic energy to the interatomic vibrations. This is a failure of the published thermostat because the temperature of the cluster is mainly due to vibrations in small atomic clusters This logarithmic oscillator cannot be used to thermostat any atomic or molecular system, small or large.

The second part of the dissertation is the investigation of the inherent structure of the polymer polyethylene glycol (PEG) solvated in three different solvents: water, water with 4% ethanol, and ethyl acetate. PEG with molecular weight of 2000 Da (PEG₂₀₀₀) is a polymer with many applications from industrial manufacturing to medicine that in bulk is a paste. However, its structure in very dilute solutions deserved a thorough study, important for the onset of aggregation with other polymer chains. I introduced a modification to the GROMOS 54A7 force field parameters for modeling PEG₂₀₀₀ and ethyl acetate. Both force fields are new and have now been incorporated into the database of known residues in the molecular dynamics package Gromacs. This research required numerous high performance computing MD simulations in the ARGO cluster of GMU for systems with about 100,000 solvent molecules. My findings show that PEG₂₀₀₀ in water acquires a ball-like structure without encapsulating solvent molecules. In addition, no hydrogen bonds were formed. In water with 4% ethanol, PEG₂₀₀₀ acquires also a ball-like structure but the polymer ends fluctuate folding outward and onward, although the general shape is still a compact ball-like structure.

In contrast, PEG₂₀₀₀ in ethyl acetate is quite elongated, as a very flexible spaghetti that forms kinks that unfold to give rise to folds and kinks in other positions along the polymer length. The behavior resembles an ideal polymer in a &thetas; solvent. A Principal Component Analysis (PCA) of the minima composing the inherent structure evidences the presence of two distinct groups of ball-like structures of PEG₂₀₀₀ in water and water with 4% ethanol. These groups give a definite signature to the solvated structure of PEG₂₀₀₀ in these two solvents. In contrast, PCA reveals several groups of avoided states for PEG₂₀₀₀ in ethyl acetate that disqualify the possibility of being an ideal polymer in a &thetas; solvent.

The third part of the dissertation is a work in progress, where I investigate the condensed phase of PEG₂₀₀₀ and study the interface between the condensed phase and the three different solvents under study. With a strategy of combining NPT MD simulations at different temperatures and pressures, PEG ₂₀₀₀ condensed phase displays the experimental density within a 1% discrepancy at 300 K and 1 atm. This is a very encouraging result on this ongoing project.

Block copolymers are composed of chemically distinct polymer chains that can be covalently linked in a variety of sequences and architectures. They are ubiquitous as ingredients of consumer products and also have applications in advanced plastics, drug delivery, advanced membranes, and next generation nano-lithographic patterning. The wide spectrum of possible block copolymer applications is a consequence of block copolymer self-assembly into periodic, meso-scale morphologies as a function of varying block composition and architecture in both melt and solution states, and the broad spectrum of physical properties that such mesophases afford.

Materials exploration and discovery has traditionally been pursued through an iterative process between experimental and theoretical/computational collaborations. This process is often implemented in a trial-and-error fashion, and from the computational perspective of generating phase diagrams, usually requires some existing knowledge about the competitive phases for a given system. Self-Consistent Field Theory (SCFT) simulations have proven to be both qualitatively and quantitatively accurate in the determination, or forward mapping, of block copolymer phases of a given system. However, it is possible to miss candidates. This is because SCFT simulations are highly dependent on their initial configurations, and the ability to map phase diagrams requires a priori knowledge of what the competing candidate morphologies are. The unguided search for the stable phase of a block copolymer of a given composition and architecture is a problem of global optimization. SCFT by itself is a local optimization method, so we can combine it with population-based heuristic algorithms geared at global optimization to facilitate forward mapping. In this dissertation, we discuss the development of two such methods: Genetic Algorithm + SCFT (GA-SCFT) and Particle Swarm Optimization + SCFT (PSO-SCFT). Both methods allow a population of configurations to explore the space associated with the numerous states accessible to a block copolymer of a given composition and architecture.

GA-SCFT is a real-space method in which a population of SCFT field configurations “evolves” over time. This is achieved by initializing the population randomly, allowing the configurations to relax to local basins of attraction using SCFT simulations, then selecting fit members (lower free energy structures) to recombine their fields and undergo mutations to generate a new “generation” of structures that iterate through this process. We present results from benchmark testing of this GA-SCFT technique on the canonical AB diblock copolymer melt, for which the theoretical phase diagram has long been established. The GA-SCFT algorithm successfully predicts many of the conventional mesophases from random initial conditions in large, 3-dimensional simulation cells, including hexagonally-packed cylinders, BCC-packed spheres, and lamellae, over a broad composition range and weak to moderate segregation strength. However, the GA-SCFT method is currently not effective at discovery of network phases, such as the Double-Gyroid (GYR) structure.

PSO-SCFT is a reciprocal space approach in which Fourier components of SCFT fields near the principal shell are manipulated. Effectively, PSO-SCFT facilitates the search through a space of reciprocal-space SCFT seeds which yield a variety of morphologies. Using intensive free energy as a fitness metric by which to compare these morphologies, the PSO-SCFT methodology allows us to agnostically identify low-lying competitive and stable morphologies. We present results for applying PSO-SCFT to conformationally symmetric diblock copolymers and a miktoarm star polymer, AB₄, which offers a rich variety of competing sphere structures. Unlike the GA-SCFT method we previously presented, PSO-SCFT successfully predicts the double gyroid morphology in the AB-diblock. Furthermore, PSO-SCFT successfully recovers the A ₁₅ morphology at a composition where it is expected to be stable in the miktoarm system, as well as several competitive metastable candidates, and a new sphere morphology belonging to the hexagonal space group 191, which has not been seen before in polymer systems. Thus, we believe the PSO-SCFT method provides a promising platform for screening for competitive structures in a given block copolymer system.

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification.

As human understandings of physics, chemistry and biology converge and the development of computers proceeds, computational chemistry or computational biophysics has become a substantial field of research. It serves to explore the fundamentals of life and also has extended applications in the field of medicine. Among the many aspects of computational chemistry, this Ph. D. work focuses on the numerical methods for studying diffusion and electrostatics of biomolecules at the nanoscale. Diffusion and electrostatics are two independent subjects in terms of their physics, but closely related in applications. In living cells, the mechanism of diffusion powers a ligand to move towards its binding target. And electrostatic forces between the ligand and the target or the ligand and the environment guide the direction of the diffusion, the correct binding orientation and, together with other molecular forces, ensure the stability of the bound complex. More abstractly, diffusion describes the stochastic manner biomolecules move on their energy landscape and electrostatic forces are a major contributor to the shape of the energy landscape. This Ph. D. work aims to acquire a good understanding of both biomolecular diffusion and electrostatics and how the two are used together in numerical calculations. Three projects are presented. The first project is a proof of concept of the bead-model approach to calculate the diffusion tensor. The second project is the benchmark for a new electrostatics method, the size-modified Poisson-Boltzmann equation. The third project is an application that combines diffusion and electrostatics to calculate the substrate channeling efficiency between the human thymidylate synthase and dihydrofolate reductase.