Global ETD Search

21	Effective Statistical Energy Function Based Protein Un/Structure Prediction Mishra, Avdesh 05 August 2019 (has links) Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on this, we extend the mapping of protein sequence not only to a fixed stable structure but also to an ensemble of protein conformations, which help us explain the complex interaction within a cell that was otherwise obscured. The objective of this dissertation is to develop effective ab initio methods and tools for protein un/structure prediction by developing effective statistical energy function, conformational search method, and disulfide connectivity patterns predictor. The key outcomes of this dissertation research are: i) a sequence and structure-based energy function for structured proteins that includes energetic terms extracted from hydrophobic-hydrophilic properties, accessible surface area, torsion angles, and ubiquitously computed dihedral angles uPhi and uPsi, ii) an ab initio protein structure predictor that combines optimal energy function derived from sequence and structure-based properties of proteins and an effective conformational search method which includes angular rotation and segment translation strategies, iii) an SVM with RBF kernel-based framework to predict disulfide connectivity pattern, iv) a hydrophobic-hydrophilic property based energy function for unstructured proteins, and v) an ab initio conformational ensemble generator that combines energy function and conformational search method for unstructured proteins which can help understand the biological systems involving IDPs and assist in rational drugs design to cure critical diseases such as cancer or cardiovascular diseases caused by challenging states of IDPs. Energy Function Ab Initio Protein Structure Prediction Disulfide Bonds Prediction Intrinsically Disordered Proteins Flexible Energy Function Conformational Ensemble Generator Algebraic Geometry Amino Acids, Peptides, and Proteins Bioinformatics Biological and Chemical Physics Computational Biology Other Computer Sciences Statistical Models Structural Biology
22	The Development and Application of Mass Spectrometry-based Structural Proteomic Approaches to Study Protein Structure and Interactions Makepeace, Karl A.T. 26 August 2022 (has links) Proteins and their intricate network of interactions are fundamental to many molecular processes that govern life. Mass spectrometry-based structural proteomics represents a powerful set of techniques for characterizing protein structures and interactions. The last decade has witnessed a large-scale adoption in the application of these techniques toward solving a variety of biological questions. Addressing these questions has often been coincident with the further development of these techniques. Insight into the structures of individual proteins and their interactions with other proteins in a proteome-wide context has been made possible by recent developments in the relatively new field of chemical crosslinking combined with mass spectrometry. In these experiments crosslinking reagents are used to capture protein-protein interactions by forming covalent linkages between proximal amino acid residues. The crosslinked proteins are then enzymatically digested into peptides, and the covalently-coupled crosslinked peptides are identified by mass spectrometry. These identified crosslinked peptides thus provide evidence of interacting regions within or between proteins. In this dissertation the development of tools and methods that facilitate this powerful technique are described. The primary arc of this work follows the development and application of mass spectrometry-based approaches for the identification of protein crosslinks ranging from those which exist endogenously to those which are introduced synthetically. Firstly, the development of a novel strategy for comprehensive determination of naturally occurring protein crosslinks in the form of disulfide bonds is described. Secondly, the application of crosslinking reagents to create synthetic crosslinks in proteins coupled with molecular dynamics simulations is explored in order to structurally characterize the intrinsically disordered tau protein. Thirdly, improvements to a crosslinking-mass spectrometry method for defining a protein-protein interactome in a complex sample is developed. Altogether, these described approaches represent a toolset to allow researchers to access information about protein structure and interactions. / Graduate Mass Spectrometry Structural Proteomics Crosslinking Cross-linking Disulfide bonds Proteomics Tau Mitochondria Method development Molecular dynamics Protein chemistry Protein crosslinking Protein cross-linking Shotgun proteomics Bottom-up proteomics Data-dependent acquisition DDA Protein-protein interactome Interactome Interactomics Proteinase K Trypsin Higher-order crosslinking Machine learning Feature engineering Yeast Yeast mitochondria Algorithms Data analysis Surface modification Molecular modelling Non-specific digest Structural mass spectrometry Structural biology Biochemistry LC-MS

Search results

Effective Statistical Energy Function Based Protein Un/Structure Prediction

The Development and Application of Mass Spectrometry-based Structural Proteomic Approaches to Study Protein Structure and Interactions