Conformational Change and Topological Stability of Proteins

The conformation and topology of a protein changes when stabilizing forces are absent, but the mechanisms by which these changes occur remains elusive. This dissertation aims to broaden the understandings. On the conformational level, the M20 loop conformers of E. coli dihydrofolate reductase are interrogated to identify factors responsible for their stability as well as to determine how one conformer might change into another. Molecular dynamics is used to simulate the open, closed and occluded conformers (observed in X-ray crystal structures) under a series of different single ligand conditions. Analysis shows that all open conformers move to a similar new conformation. Free energy methods examine the stability of the new loop conformer relative to the others. External perturbation molecular dynamics simulations and normal mode analysis methods examine possible M20 loop pathways occurring either when one loop conformer is forced to change into another or when a ligand is pulled out of its binding site. On the topological level, conserved residue-residue interaction networks found among three different protein superfamilies (the all α-helix death domains, the α/β-plaits and the all β-sheet immunoglobulins), each of different secondary structure but sharing the Greek-Key topology, are assessed for any inherent stability they might contain relative to randomly selected interaction networks. This assessment is achieved by simulating one protein from each family at different temperatures, ranging from 300 to 600 K, and observing that adding thermal energy to the system causes the random interaction networks to fall apart more easily than the conserved networks. When considered together, the conformational and topological projects, although very different from each other, both demonstrate the same idea - that regardless of scale, instability causes change and vice versa. This dissertation is divided into five chapters: Introduction, Theoretical Background, M20 Loop Conformers of Dihydrofolate Reductase, Conserved Contact Networks of Greek-Key Proteins and Summary

Effect of chemistry and temperature on planar defects in superalloys

Fault energies are exceptionally important, determining the activation of deformation processes in metals. This is especially true in the case of two phase γ/γ' superalloys. However there is currently no model for how the fault energies in these alloys vary with composition. This is a large issue due to the complex nature of superalloys, which typically feature more than 10 alloying elements. The main aim of this thesis is to design a model to predict fault energies for arbitrary alloying compositions of superalloys. This would provide a tool for alloy designers, allowing the choice of alloy compositions to make the alloys resist deformation, facilitating higher operational temperatures and efficiency. In this research, first-principles calculations are undertaken using the projector augmented wave basis set in conjunction with the generalised-gradient approximation, as implemented in the Vienna ab initio simulation package. This allows the generation of input parameters for axial interaction models. Calculations are made for a large number of compounds and alloys to allow the assessment of how changing the alloy chemistry impacts the intrinsic stacking fault and superlattice intrinsic stacking fault energy. Using interpolation and fitting of these results it is possible to produce a model for arbitrary alloying compositions. Due to the high operational temperatures of superalloys the change in these fault energies with temperature was calculated (as first-principles calculations are traditionally only possible at 0 K). This was done using the quasiharmonic Debye model as implemented in the GIBBS package. The effects of temperature were found, in general, to be significantly less than the effects of alloying, providing validation for the usage of first-principles calculations for high temperature alloys

Multiscale Modeling Of The Hierarchical Structure Of Cellulose Nanocrystals

Cellulose constitutes the most abundant renewable polymeric resource available today. It considered an almost inexhaustible source of raw material, and holds great promise in meeting increasing demands for environmentally friendly and biocompatible products. Key future applications are currently under development for the automotive, aerospace and textile industries. When cellulose fibers are subjected to acid hydrolysis, the fibers yield rod-like, highly crystalline residues called cellulose nanocrystals (CNCs). These particles show remarkable mechanical and chemical properties (e.g. Young Modulus ~200 GPa) within the range of other synthetically-developed reinforcement materials. Critical to the design of these materials are fundamental material properties, many of which are unavailable in the existing literature. A multiscale framework has been developed to predict and describe the thermo-mechanical characteristics of cellulose nanocrystals using state-of-the-art computational tools capable of connecting atomistic based simulations to experiments through continuum based modeling techniques. First-principle density functional theory and molecular dynamic simulations were utilized at the atomistic level. Longstanding issues regarding the elastic and thermal expansion anisotropies for crystalline cellulose have been studied in terms of the single-crystal elasticity tensor and the thermal expansion tensor components. First-principles phonon calculations via van der Waals density functionals as well as reverse non-equilibrium molecular dynamics simulations were used to gain a fundamental understanding of defect-free, crystalline cellulose thermo-mechanical properties. Entropy, enthalpy, constant pressure heat capacity, thermal expansion tensor, thermal conductivity, Young\u27s modulus, and Poisson\u27s ratio, were computed over a wide range of temperatures (0 to 500 K). A comprehensive study of the hydrogen bond structure that characterizes crystalline cellulose has been conducted in an attempt to ascertain the roles that inter- and intra- molecular hydrogen bonds play in determining the mechanical properties of CNCs. Five different force fields/parameter sets were compared with experimental results and first-principles simulations in terms of their ability to predict the following properties: lattice parameters and angles, linear elasticity tensor and linear thermal expansion tensor. Continuum based modeling techniques were used to answer fundamental questions regarding the role of hydrogen bonding in the mechanical response of CNCs. A variety of finite element-based continuum models were specifically developed for cellulose chains and non-bonding interactions (van der Waals, Coulomb and hydrogen bonds). As a result, a complete multiscale framework capable of reproducing the mechanical behavior of cellulose nanocrystals has been developed

Analysis of biological and chemical systems using information theoretic approximations

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 115-123).The identification and quantification of high-dimensional relationships is a major challenge in the analysis of both biological and chemical systems. To address this challenge, a variety of experimental and computational tools have been developed to generate multivariate samples from these systems. Information theory provides a general framework for the analysis of such data, but for many applications, the large sample sizes needed to reliably compute high-dimensional information theoretic statistics are not available. In this thesis we develop, validate, and apply a novel framework for approximating high-dimensional information theoretic statistics using associated terms of arbitrarily low order. For a variety of synthetic, biological, and chemical systems, we find that these low-order approximations provide good estimates of higher-order multivariate relationships, while dramatically reducing the number of samples needed to reach convergence. We apply the framework to the analysis of multiple biological systems, including a phospho-proteomic data set in which we identify a subset of phospho-peptides that is maximally informative of cellular response (migration and proliferation) across multiple conditions (varying EGF or heregulin stimulation, and HER2 expression). This subset is shown to produce statistical models with superior performance to those built with subsets of similar size. We also employ the framework to extract configurational entropies from molecular dynamics simulations of a series of small molecules, demonstrating improved convergence relative to existing methods. As these disparate applications highlight, our framework enables the use of general information theoretic phrasings even in systems where data quantities preclude direct estimation of the high-order statistics. Furthermore, because the framework provides a hierarchy of approximations of increasing order, as data collection and analysis techniques improve, the method extends to generate more accurate results, while maintaining the same underlying theory.by Bracken Matheny King.Ph.D