Entamoeba Histolytica
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Introduction of Human Telomerase Reverse Transcriptase to Normal Human Fibroblasts Enhances DNA Repair Capacity
Vol. 10, 2551–2560, April 1, 2004 Clinical Cancer Research 2551 Introduction of Human Telomerase Reverse Transcriptase to Normal Human Fibroblasts Enhances DNA Repair Capacity Ki-Hyuk Shin,1 Mo K. Kang,1 Erica Dicterow,1 INTRODUCTION Ayako Kameta,1 Marcel A. Baluda,1 and Telomerase, which consists of the catalytic protein subunit, No-Hee Park1,2 human telomerase reverse transcriptase (hTERT), the RNA component of telomerase (hTR), and several associated pro- 1School of Dentistry and 2Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California teins, has been primarily associated with maintaining the integ- rity of cellular DNA telomeres in normal cells (1, 2). Telomer- ase activity is correlated with the expression of hTERT, but not ABSTRACT with that of hTR (3, 4). Purpose: From numerous reports on proteins involved The involvement of DNA repair proteins in telomere main- in DNA repair and telomere maintenance that physically tenance has been well documented (5–8). In eukaryotic cells, associate with human telomerase reverse transcriptase nonhomologous end-joining requires a DNA ligase and the (hTERT), we inferred that hTERT/telomerase might play a DNA-activated protein kinase, which is recruited to the DNA role in DNA repair. We investigated this possibility in nor- ends by the DNA-binding protein Ku. Ku binds to hTERT mal human oral fibroblasts (NHOF) with and without ec- without the need for telomeric DNA or hTR (9), binds the topic expression of hTERT/telomerase. telomere repeat-binding proteins TRF1 (10) and TRF2 (11), and Experimental Design: To study the effect of hTERT/ is thought to regulate the access of telomerase to telomere DNA telomerase on DNA repair, we examined the mutation fre- ends (12, 13). -
Computation and Visualization of Protein Topology Graphs Including Ligand Information
Computation and Visualization of Protein Topology Graphs Including Ligand Information Tim Schäfer1, Patrick May2, and Ina Koch1 1 Institute of Computer Science, Department of Molecular Bioinformatics, Johann Wolfgang Goethe-University Frankfurt (Main), Robert-Mayer-Straße 11–15, 60325 Frankfurt (Main), Germany, [email protected] 2 Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L–4362 Esch-sur-Alzette, Luxembourg Abstract Motivation: Ligand information is of great interest to understand protein function. Protein structure topology can be modeled as a graph with secondary structure elements as vertices and spatial contacts between them as edges. Meaningful representations of such graphs in 2D are required for the visual inspection, comparison and analysis of protein folds, but their automatic visualization is still challenging. We present an approach which solves this task, supports different graph types and can optionally include ligand contacts. Results: Our method extends the field of protein structure description and visualization by including ligand information. It generates a mathematically unique representation and high- quality 2D plots of the secondary structure of a protein based on a protein-ligand graph. This graph is computed from 3D atom coordinates in PDB files and the corresponding SSE assignments of the DSSP algorithm. The related software supports different notations and allows a rapid visualization of protein structures. It can also export graphs in various standard file formats so they can be used with other software. Our approach visualizes ligands in relationship to protein structure topology and thus represents a useful tool for exploring protein structures. Availability: The software is released under an open source license and available at http://www.bioinformatik.uni-frankfurt.de/ in the Software section under Visualization of Protein Ligand Graphs. -
Predictive Energy Landscapes for Folding Α-Helical Transmembrane Proteins
Predictive energy landscapes for folding α-helical transmembrane proteins Bobby L. Kim, Nicholas P. Schafer, and Peter G. Wolynes1 Departments of Chemistry and Physics and Astronomy and the Center for Theoretical Biological Physics, Rice University, Houston, TX 77005 Contributed by Peter G. Wolynes, June 11, 2014 (sent for review May 19, 2014; reviewed by Zaida A. Luthey-Schulten, Shoji Takada, and Margaret Cheung) We explore the hypothesis that the folding landscapes of mem- folding. Starting with Khorana’swork(7),numerousα-helical brane proteins are funneled once the proteins’ topology within the transmembrane proteins have been refolded from a chemically membrane is established. We extend a protein folding model, denatured state in vitro (8). This indicates that at least some the associative memory, water-mediated, structure, and energy transmembrane domains may not require the translocon to fold model (AWSEM) by adding an implicit membrane potential and properly. In addition, recent experiments on a few α-helical reoptimizing the force field to account for the differing nature of transmembrane proteins have succeeded in characterizing the the interactions that stabilize proteins within lipid membranes, structure of transition state ensembles, in a manner like that used yielding a model that we call AWSEM-membrane. Once the pro- for globular proteins. These studies suggest that native contacts tein topology is set in the membrane, hydrophobic attractions are important in the folding nucleus but may not represent the play a lesser role in finding the native structure, whereas po- whole story (9, 10). Whether membrane proteins possess energy lar–polar attractions are more important than for globular pro- landscapes as funneled as globular proteins remains an open teins. -
"Protein Quaternary Structure: Subunit&Ndash;Subunit
Protein Quaternary Secondary article Structure: Subunit–Subunit Article Contents . Introduction Interactions . Quaternary Structure Assembly . Folding and Function Susan Jones, University College, London, England . Protein–Protein Recognition Sites . Concluding Remarks Janet M Thornton, University College, London, England The quaternary structure of proteins is the highest level of structural organization observed in these macromolecules. The multimeric proteins that result from quaternary structure formation involve the association of protein subunits through hydrophobic and electrostatic interactions. Protein quaternary structure has important implications for protein folding and function. Introduction from, other components. Using these definitions, the Proteins are organized into a structural hierarchy. The haemoglobin tetramer (comprised of two a and two b polypeptide chain at the primary structural level comprises polypeptide chains) is defined as an oligomer consisting of a linear, noncovalently linked amino acid residue se- two protomers, each consisting of two monomers, i.e. one a quence. Secondary structure is the level at which the linear and one b polypeptide chain. The definition of a subunit sequences aggregate to form structural motifs such as allows the term to be used for either the a-orb-monomer, helices and sheets. The tertiary structure is formed by or for the ab-protomer. The term multimer is also widely packing of the secondary structural elements into one or used in the literature and is defined here as a protein with a more compact globular domains. In many cases proteins finite number of subunits that need not be identical. are composed of only a single polypeptide chain that has The quaternary nature of some proteins was first tertiary structure as its highest level of organization, e.g. -
Protein Folding and the Organization of the Protein Topology Universe
Opinion TRENDS in Biochemical Sciences Vol.30 No.1 January 2005 Protein folding and the organization of the protein topology universe Kresten Lindorff-Larsen1, Peter Røgen2, Emanuele Paci3, Michele Vendruscolo1 and Christopher M. Dobson1 1University of Cambridge, Department of Chemistry, Lensfield Road, Cambridge, UK, CB2 1EW 2Department of Mathematics, Technical University of Denmark, Building 303, DK-2800 Kongens Lyngby, Denmark 3University of Zu¨ rich, Department of Biochemistry, Winterthurerstrasse 190, 8057 Zu¨ rich, Switzerland The mechanism by which proteins fold to their native ensembles has shown that establishing the correct overall states has been the focus of intense research in recent topology of the polypeptide chain is a crucial aspect of years. The rate-limiting event in the folding reaction is protein folding. This observation is in accord with a series the formation of a conformation in a set known as the of studies that have shown that the folding rate of a transition-state ensemble. The structural features pre- protein, to a first approximation, can be related to the sent within such ensembles have now been analysed for entropic cost of forming the native-like topology [16–22]. a series of proteins using data from a combination of The structural changes occurring during protein fold- biochemical and biophysical experiments together with ing have also been analysed in detail for a series of computer-simulation methods. These studies show that proteins and we discuss some of these studies here. The the topology of the transition state is determined by a results enable the topological view of folding to be set of interactions involving a small number of key reconciled with the well-established concept of nucleation residues and, in addition, that the topology of the [23] by showing that – despite the many different ways in transition state is closer to that of the native state than which a given topology could, in principle, be generated – to that of any other fold in the protein universe. -
Using the Spycatcher-Spytag and Related Systems for Labeling and Localizing Bacterial Proteins
International Journal of Molecular Sciences Review Catching a SPY: Using the SpyCatcher-SpyTag and Related Systems for Labeling and Localizing Bacterial Proteins Daniel Hatlem , Thomas Trunk, Dirk Linke and Jack C. Leo * Bacterial Cell Surface Group, Section for Evolution and Genetics, Department of Biosciences, University of Oslo, 0316 Oslo, Norway; [email protected] (D.H.); [email protected] (T.T.); [email protected] (D.L.) * Correspondence: [email protected]; Tel.: +47-228-59027 Received: 2 April 2019; Accepted: 26 April 2019; Published: 30 April 2019 Abstract: The SpyCatcher-SpyTag system was developed seven years ago as a method for protein ligation. It is based on a modified domain from a Streptococcus pyogenes surface protein (SpyCatcher), which recognizes a cognate 13-amino-acid peptide (SpyTag). Upon recognition, the two form a covalent isopeptide bond between the side chains of a lysine in SpyCatcher and an aspartate in SpyTag. This technology has been used, among other applications, to create covalently stabilized multi-protein complexes, for modular vaccine production, and to label proteins (e.g., for microscopy). The SpyTag system is versatile as the tag is a short, unfolded peptide that can be genetically fused to exposed positions in target proteins; similarly, SpyCatcher can be fused to reporter proteins such as GFP, and to epitope or purification tags. Additionally, an orthogonal system called SnoopTag-SnoopCatcher has been developed from an S. pneumoniae pilin that can be combined with SpyCatcher-SpyTag to produce protein fusions with multiple components. Furthermore, tripartite applications have been produced from both systems allowing the fusion of two peptides by a separate, catalytically active protein unit, SpyLigase or SnoopLigase. -
UC San Diego UC San Diego Electronic Theses and Dissertations
UC San Diego UC San Diego Electronic Theses and Dissertations Title Structural analysis of a forkhead-associated domain from the type III secretion system protein YscD Permalink https://escholarship.org/uc/item/6410g6n9 Author Gamez, Alicia Margarita Publication Date 2011 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Structural analysis of a forkhead-associated domain from the type III secretion system protein YscD A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Chemistry by Alicia Margarita Gamez Committee in charge: Professor Partho Ghosh, Chair Professor Michael Burkart Professor Daniel Donoghue Professor Victor Nizet Professor Susan Taylor 2011 The Dissertation of Alicia Margarita Gamez is approved, and it is acceptable in quality and form for publication on microfilm and electronically: Chair University of California, San Diego 2011 iii DEDICATION This thesis is dedicated to my family. iv TABLE OF CONTENTS Signature Page .......................................................................................................... iii Dedication ................................................................................................................. iv Table of Contents ....................................................................................................... v List of Figures .......................................................................................................... -
Metabolic Genes.Xlsx
Table S4 Survey of key functional genes with biogeochemical or energetic importance in the genomes of Tardiphaga isolates. The gene list was complied from the FunGen pipeline (http://fungene.cme.msu.edu/) and the authors' own collection. Category Gene Enzyme vice154 vice278 vice304 vice352 C metabolism scd2 esterase / lipase ●●●● C metabolism xylA xylose isomerase ○○○○ One carbon metabolism cooS carbon monoxide dehydrogenase ○○○○ One carbon metabolism pmoA particulate methane monooxygenase A‐subunit ○○○○ One carbon metabolism pxmA1 particulate methane monooxygenase beta subunit ○○○○ One carbon metabolism prk phosphoribulokinase ●●●● One carbon metabolism cbbL ribulose‐bisphosphate carboxylase large subunit ●●●● One carbon metabolism cbbM ribulose‐bisphosphate carboxylase small subunit ●●●● One carbon metabolism smmo soluble methane monooxygenase ●●●● N metabolism amiE aliphatic amidase ●●●● N metabolism amoA ammonia monooxygenase subunit A ○○○○ N metabolism ansA asparaginase ○○○○ N metabolism aspA aspartate ammonia‐lyase ○○○○ N metabolism glsA glutaminase ●●●● N metabolism hutH histidine ammonia‐lyase ○○○○ N metabolism norB nitric oxide reductase ○○○○ N metabolism p450nor nitric oxide reductase (NAD(P), nitrous oxide‐forming) ○○○○ N metabolism nir nitrite reductase ●●●● N metabolism nifH nitrogenase iron protein ○○○○ N metabolism anfD nitrogenase iron‐iron protein, alpha chain ○○○○ N metabolism nifD nitrogenase molybdenum‐iron protein subunit alpha ○○○○ N metabolism vnfD nitrogenase vanadium‐iron protein alpha chain ○○○○ N metabolism nosZ -
Every Detail Matters. That Is, How the Interaction Between Gα Proteins and Membrane Affects Their Function
membranes Review Every Detail Matters. That Is, How the Interaction between Gα Proteins and Membrane Affects Their Function Agnieszka Polit * , Paweł Mystek and Ewa Błasiak Department of Physical Biochemistry, Faculty of Biochemistry Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Kraków, Poland; [email protected] (P.M.); [email protected] (E.B.) * Correspondence: [email protected]; Tel.: +48-12-6646156 Abstract: In highly organized multicellular organisms such as humans, the functions of an individ- ual cell are dependent on signal transduction through G protein-coupled receptors (GPCRs) and subsequently heterotrimeric G proteins. As most of the elements belonging to the signal transduction system are bound to lipid membranes, researchers are showing increasing interest in studying the accompanying protein–lipid interactions, which have been demonstrated to not only provide the environment but also regulate proper and efficient signal transduction. The mode of interaction between the cell membrane and G proteins is well known. Despite this, the recognition mechanisms at the molecular level and how the individual G protein-membrane attachment signals are interre- lated in the process of the complex control of membrane targeting of G proteins remain unelucidated. This review focuses on the mechanisms by which mammalian Gα subunits of G proteins interact with lipids and the factors responsible for the specificity of membrane association. We summarize recent data on how these signaling proteins are precisely targeted to a specific site in the membrane region by introducing well-defined modifications as well as through the presence of polybasic regions Citation: Polit, A.; Mystek, P.; within these proteins and interactions with other components of the heterocomplex. -
Telomerase Catalysis: a Phylogenetically Conserved Reverse Transcriptase
Proc. Natl. Acad. Sci. USA Vol. 95, pp. 8415–8416, July 1998 Commentary Telomerase catalysis: A phylogenetically conserved reverse transcriptase Victoria Lundblad Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030 Replication of telomeres, the ends of eukaryotic chromo- replication and maintenance. As a consequence, the first two somes, is the responsibility of the enzyme telomerase. Since its telomerase-associated proteins, p80 and p95, were identified discovery 13 years ago, research on this unusual DNA poly- after purification of the Tetrahymena telomerase complex (15). merase has revealed a series of surprises. The first of these was On the basis of limited sequence similarities with other poly- the realization that information within the enzyme itself merases, p95 was proposed to contain the catalytic active site determines the sequence of its product: a portion of a telom- of this enzyme (15). However, p95 showed no homology to the erase RNA subunit is the template that dictates the nucleotides emerging family of TERT proteins. This presented a potential added onto the telomere (1, 2). The interest in telomere puzzle, invoking the possibility of an alternative class of replication has increased further during the past several years telomerase enzymes that utilized a different catalytic mecha- because of observations indicating that maintenance of telo- nism. mere length by telomerase could provide the molecular basis This possibility has now been laid to rest by two reports in for determining the lifespan of cells in culture (3). this issue, from the Cech and Collins laboratories, showing that The most recent insight has been the discovery that telom- the Tetrahymena telomerase relies on a reverse transcriptase erase is a reverse transcriptase (4, 5), with catalysis provided subunit for catalysis (7, 13). -
APEX2 Proximity Proteomics Resolves Flagellum Subdomains and Identifies Flagellum Tip-Specific
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.09.984815; this version posted March 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 APEX2 proximity proteomics resolves flagellum subdomains and identifies flagellum tip-specific 2 proteins in Trypanosoma brucei 3 4 Daniel E. Vélez-Ramírez,a,b Michelle M. Shimogawa,a Sunayan Ray,a* Andrew Lopez,a Shima 5 Rayatpisheh,c* Gerasimos Langousis,a* Marcus Gallagher-Jones,d Samuel Dean,e James A. Wohlschlegel,c 6 Kent L. Hill,a,f,g# 7 8 aDepartment of Microbiology, Immunology, and Molecular Genetics, University of California Los 9 Angeles, Los Angeles, California, USA 10 bPosgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de 11 México, México 12 cDepartment of Biological Chemistry, University of California Los Angeles, Los Angeles, California, USA 13 dDepartment of Chemistry and Biochemistry, UCLA-DOE Institute for Genomics and Proteomics, Los 14 Angeles, California, USA 15 eWarwick Medical School, University of Warwick, Coventry, England, United Kingdom 16 fMolecular Biology Institute, University of California Los Angeles, Los Angeles, California, USA 17 gCalifornia NanoSystems Institute, University of California Los Angeles, Los Angeles, California, USA 18 19 Running title: APEX2 proteomics in Trypanosoma brucei 20 21 #Address correspondence to Kent L. Hill, [email protected]. Page 1 of 33 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.09.984815; this version posted March 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
Machine Learning Methods for Protein Structure Prediction Jianlin Cheng, Allison N
IEEE REVIEWS IN BIOMEDICAL ENGINEERING, VOL. 1, 2008 41 Machine Learning Methods for Protein Structure Prediction Jianlin Cheng, Allison N. Tegge, Member, IEEE, and Pierre Baldi, Senior Member, IEEE Methodological Review Abstract—Machine learning methods are widely used in bioin- formatics and computational and systems biology. Here, we review the development of machine learning methods for protein structure prediction, one of the most fundamental problems in structural biology and bioinformatics. Protein structure predic- tion is such a complex problem that it is often decomposed and attacked at four different levels: 1-D prediction of structural features along the primary sequence of amino acids; 2-D predic- tion of spatial relationships between amino acids; 3-D prediction Fig. 1. Protein sequence-structure-function relationship. A protein is a linear of the tertiary structure of a protein; and 4-D prediction of the polypeptide chain composed of 20 different kinds of amino acids represented quaternary structure of a multiprotein complex. A diverse set of by a sequence of letters (left). It folds into a tertiary (3-D) structure (middle) both supervised and unsupervised machine learning methods has composed of three kinds of local secondary structure elements (helix – red; beta- strand – yellow; loop – green). The protein with its native 3-D structure can carry been applied over the years to tackle these problems and has sig- out several biological functions in the cell (right). nificantly contributed to advancing the state-of-the-art of protein structure prediction. In this paper, we review the development and application of hidden Markov models, neural networks, support vector machines, Bayesian methods, and clustering methods in structure elements are further packed to form a tertiary structure 1-D, 2-D, 3-D, and 4-D protein structure predictions.