Strukturelle Analyse Der Dynamischen Konformationsänderung Des Prion-Proteins in Proteinmissfaltungskrankheiten

Total Page:16

File Type:pdf, Size:1020Kb

Strukturelle Analyse Der Dynamischen Konformationsänderung Des Prion-Proteins in Proteinmissfaltungskrankheiten Strukturelle Analyse der dynamischen Konformationsänderung des Prion-Proteins in Proteinmissfaltungskrankheiten DISSERTATION zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften (Dr. rer. nat.) Eingereicht im Fachbereich Chemie der Fakultät für Mathematik, Informatik und Naturwissenschaften an der Universität Hamburg vorgelegt von Anne Sommer Hamburg 2015 Die praktischen Arbeiten wurden im Zeitraum von Februar 2010 bis September 2014 in der Nachwuchsgruppe von Dr. Lars Redecke im Laboratorium für Strukturbiologie von Infektion und Entzündung der Universitäten Hamburg und Lübeck sowie am Institut für Biochemie und Molekularbiologie des Fachbereichs Chemie der Universität Hamburg in den Laboratorien der Arbeitsgruppe von Prof. Dr. Christian Betzel durchgeführt. 1. Gutachter: Prof. Dr. Ch. Betzel 2. Gutachter: Prof. Dr. B. Meyer Tag der Disputation: 07.08.2015 I. Inhaltsverzeichnis I I. Inhaltsverzeichnis II. Abkürzungsverzeichnis ...................................................................................................... V III. Zusammenfassung ........................................................................................................... IX IV. Summary ........................................................................................................................... XI 1. Einleitung .............................................................................................................................. 1 1.1. Prionen-Erkrankungen .................................................................................................... 2 1.2. Der Erreger und die „Protein-only“-Theorie ................................................................... 4 1.3. Struktur und Funktion des Prion-Proteins ....................................................................... 5 1.4. Aggregation des Prion-Proteins ...................................................................................... 8 1.5. Therapie von Prionen-Erkrankungen ............................................................................ 11 1.6. Dynamische Lichtstreuung (DLS) ................................................................................ 13 2. Zielsetzung der Doktorarbeit ............................................................................................ 15 3. Ergebnisse ........................................................................................................................... 17 3.1. Klonierung der Prion-Protein-Konstrukte ..................................................................... 18 3.2. Optimierung der Prion-Protein-Herstellung .................................................................. 19 3.2.1. Rekombinante Genexpression in E. coli ................................................................ 19 3.2.2. Rückfaltung, Reinigung und Entfernung der Hexahistidinsequenz ....................... 20 3.3. Charakterisierung der Prion-Proteine ............................................................................ 23 3.3.1. CD-spektroskopische Analyse ............................................................................... 24 3.3.2. Hydrodynamische Radien und Proteinase K Sensitivität ....................................... 24 3.3.3. Bestimmung der Schmelzpunkte ............................................................................ 28 3.3.4. Strukturelle Charakterisierung von hPrP90-230+EFEA mit Hexahistidinsequenz in Lösung .............................................................................................................................. 31 3.4. Oxidativ-induzierte Umwandlung ................................................................................. 33 3.4.1. Validierung des bestehenden UV-Versuchsaufbaus .............................................. 33 3.4.1.1. Validierung des integrierten Online-DLS-Systems ......................................... 34 3.4.1.2. Anpassung der UV-Bestrahlungsparameter .................................................... 39 3.4.1.3. Charakterisierung der entstandenen oligomeren Spezies ................................ 42 3.4.1.4. Einfluss der Laserleistung auf die UV-induzierte Prion-Protein-Umwandlung ...................................................................................................................................... 48 3.4.2. Herstellung der intermediären Zwischenzustände ................................................. 55 3.4.2.1. Produktionsläufe im Kreislaufsystem ............................................................. 58 3.4.2.2. Produktionsläufe im Durchflusssystem ........................................................... 59 3.4.2.3. Isolierung des „dimeren“ Zwischenzustandes ................................................. 61 I. Inhaltsverzeichnis II 3.4.2.4. Charakterisierung des Prion-Spezies-Gemisches ............................................ 63 3.4.3. Metallkatalysierte oxidative Umwandlung ............................................................ 69 3.4.3.1. Analyse der Kupferbindung nach metallkatalysierter Umwandlung .............. 70 3.5. Inhibition der oxidativen Umwandlung von Prion-Proteinen ....................................... 73 3.5.1. Inhibition der metallkatalysierten oxidativen Umwandlung .................................. 73 3.5.1.1. Bestimmung der Peptid-Bindungsaffinitäten mittels Oberflächenplasmonen- resonanz (SPR)-Spektroskopie ..................................................................................... 79 3.5.1.2. Untersuchung des Faltungszustandes der PrP-Peptid-Komplexe nach metall- katalysierter Oxidation ................................................................................................. 81 3.5.2. Untersuchungen zur Inhibition der UV-induzierten Umwandlung ........................ 82 3.6. Kristallisation ................................................................................................................ 83 3.6.1. In-vivo-Kristallisation ............................................................................................. 87 4. Diskussion ........................................................................................................................... 89 4.1. UV-Bestrahlungssystem mit integriertem Online-DLS-System ................................... 90 4.2. Anwendung des UV-Bestrahlungssystems zur Induktion der Umwandlung und Aggregation des Prion-Proteins ........................................................................................... 91 4.3. Das Prion-Protein als Übergangsmetall-bindendes Protein .......................................... 96 4.4. Einfluss des GPI-Ankers auf die Umwandlung des Prion-Proteins .............................. 97 4.5. Inhibition der Prion-Protein-Umwandlung und Aggregation ....................................... 98 4.6. Kristallisationsversuche .............................................................................................. 102 4.7. Ausblick ...................................................................................................................... 104 5. Material und Methoden ................................................................................................... 107 5.1. Material ....................................................................................................................... 107 5.1.1. Geräte ................................................................................................................... 107 5.1.2. Verbrauchsmaterialien ......................................................................................... 109 5.1.3. Chemikalien ......................................................................................................... 110 5.1.4. Kommerzielle Kristallisationslösungen, Kits und Enzyme .................................. 111 5.1.5. DNA und Proteingrößenmarker ........................................................................... 111 5.1.6. Plasmide und Oligonukleotide ............................................................................. 111 5.1.7. Bakterienstämme .................................................................................................. 114 5.1.8. Insektenzellen ....................................................................................................... 115 5.1.9. Medien, Puffer und Lösungen .............................................................................. 115 5.2. Methoden ..................................................................................................................... 119 5.2.1. Molekularbiologische Methoden .......................................................................... 119 I. Inhaltsverzeichnis III 5.2.1.1. Polymerase-Kettenreaktion (PCR) ................................................................ 119 5.2.1.2. Agarose-Gelelektrophorese ........................................................................... 120 5.2.1.3. Reinigung von PCR-Fragmenten .................................................................. 120 5.2.1.4. Restriktionsspaltung ...................................................................................... 120 5.2.1.5. Bestimmung von DNA-Konzentrationen ...................................................... 121 5.2.1.6. Ligation ......................................................................................................... 121 5.2.1.7. Herstellung chemisch kompetenter Zellen .................................................... 121 5.2.1.8. Transformation von E. coli-Zellen ...............................................................
Recommended publications
  • A Network of Molecular Switches Controls the Activation of the Two-Component Response Regulator Ntrc
    ARTICLE Received 8 Aug 2014 | Accepted 26 Apr 2015 | Published 15 Jun 2015 DOI: 10.1038/ncomms8283 A network of molecular switches controls the activation of the two-component response regulator NtrC Dan K. Vanatta1, Diwakar Shukla1,2,3, Morgan Lawrenz1 & Vijay S. Pande1,2 Recent successes in simulating protein structure and folding dynamics have demonstrated the power of molecular dynamics to predict the long timescale behaviour of proteins. Here, we extend and improve these methods to predict molecular switches that characterize conformational change pathways between the active and inactive state of nitrogen regulatory protein C (NtrC). By employing unbiased Markov state model-based molecular dynamics simulations, we construct a dynamic picture of the activation pathways of this key bacterial signalling protein that is consistent with experimental observations and predicts new mutants that could be used for validation of the mechanism. Moreover, these results suggest a novel mechanistic paradigm for conformational switching. 1 Department of Chemistry, Stanford University, Stanford, California 94305, USA. 2 SIMBIOS NIH Center for Biomedical Computation, Stanford University, Stanford, California 94305,USA. 3 Department of Chemical and Bimolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illionios 61801, USA. Correspondence and requests for materials should be addressed to V.S.P. (email: [email protected]). NATURE COMMUNICATIONS | 6:7283 | DOI: 10.1038/ncomms8283 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8283 roteins involved in cellular signalling change their con- Results formation in response to changes in environment (input Simulations reveal molecular switches for NtrC activation. Psignal), such as ligand binding or chemical modification, MSMs for NtrC were built using snapshots along the simulated to control downstream cellular processes (output signal)1.
    [Show full text]
  • Spring 2013 Lecture 23
    CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna LIPIDS III EFFECT OF CHOLESTEROL ON MEMBRANES: - Bulky rigid molecule - Moderates fluidity of membranes – both increases and decreases o Cholesterol in membranes DECREASES fluidity because it is rigid o Prevents crystallization (making solid) of fatty acyl side chains by fitting between them. Disrupts close packing of fatty acyl chains. Therefore, INCREASED fluidity BIOLOGICAL MEMBRANES CONTAIN PROTEINS AS WELL AS LIPIDS: - Proteins are 20-80% of cell membrane - Rest is lipid or carbohydrate; supramolecular assembly of lipid, protein and carbohydrate - Proteins are also distributed asymmetrically - TWO classes of Membrane Proteins: o Integral Membrane Proteins o Peripheral Membrane Proteins 178 CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna - INTEGRAL MEMBRANE PROTEINS o Located WITHIN the lipid bilayer o Usually span the bilayer one or more times – called transmembrane (TM) proteins o Hydrophobic amino acids interact with fatty acid chains in the hydrophobic core of the membrane o Can be removed from the membrane with detergents like SDS – need to disrupt the hydrophobic interactions § Membrane Disruption Animation: o http://www.youtube.com/watch?v=AHT37pvcjc0 o Function: § Transporters – moving molecules into or out of cells or cell membranes § Receptors – transmitting signals from outside of the cell to the inside - β Barrel Integral Membrane Proteins § Barrel-shaped membrane protein that is made up of antiparallel β-strands with hydrophilic (interior) and hydrophobic (facing lipid tails). § So far found only in outer membranes of Gram-negative bacteria, cell wall of Gram-positive bacteria, and outer membranes of mitochondria and chloroplasts. 179 CHM333 LECTURES 23: 3/25/13 SPRING 2013 Professor Christine Hrycyna - α-Helical Membrane Proteins - Can cross the membrane once or many times and have multiple transmembrane segments.
    [Show full text]
  • Caenorhabditis Elegans BRICHOS Domain–Containing Protein C09F5.1 Maintains Thermotolerance and Decreases Cytotoxicity of A42 B
    G C A T T A C G G C A T genes Article Caenorhabditis elegans BRICHOS Domain–Containing Protein C09F5.1 Maintains Thermotolerance and Decreases Cytotoxicity of Aβ42 by Activating the UPR Myungchul Song 1, Kyunghee Song 1,2, Sunghee Kim 1,3, Jinyoung Lee 1,4, Sueyun Hwang 5 and Chingtack Han 1,* 1 Department of Life Science, Sogang University, Seoul 04107, Korea; [email protected] (M.S.); [email protected] (K.S.); [email protected] (S.K.); jinylee@amorepacific.com (J.L.) 2 LG Household & Health Care, Daejeon 34114, Korea 3 Department of Medicine, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea 4 Amorepacific R&D Center, Yongin 17074, Korea 5 Department of Chemical Engineering, Hankyung National University, Anseong 17579, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-705-8454 Received: 11 December 2017; Accepted: 9 March 2018; Published: 13 March 2018 Abstract: Caenorhabditis elegans C09F5.1 is a nematode-specific gene that encodes a type II transmembrane protein containing the BRICHOS domain. The gene was isolated as a heat-sensitive mutant, but the function of the protein remained unclear. We examined the expression pattern and subcellular localization of C09F5.1 as well as its roles in thermotolerance and chaperone function. Expression of C09F5.1 under heat shock conditions was induced in a heat shock factor 1 (HSF-1)–dependent manner. However, under normal growth conditions, most cells types exposed to mechanical stimuli expressed C09F5.1. Knockdown of C09F5.1 expression or deletion of the N-terminal domain decreased thermotolerance.
    [Show full text]
  • Modified Bradford Assay Method of Protein Quantification Utilising Dye Reagents from Four Nigerian Plants
    International Journal of Research Studies in Biosciences (IJRSB) Volume 3, Issue 12, December 2015, PP 79-87 ISSN 2349-0357 (Print) & ISSN 2349-0365 (Online) www.arcjournals.org Modified Bradford Assay Method of Protein Quantification Utilising Dye Reagents from Four Nigerian Plants *S. O. Okeniyi+, *J. Ogbodobri, **A. O. Oyedeji, *P. E. Omale, *M. M. Adeyemi, *S. Garba, ***J. A. Lori *Department.of Chemistry, Nigerian Defence Academy, Afaka, Kaduna State - Nigeria ** Department of Chemical & Physical Sciences, Walter Sisulu University Eastern Cape, South Africa ***Department of Chemistry, Bingham University, Karu, Nassarawa State - Nigeria Abstract: Aqueous and organic solvents extraction process using ethanol, methanol and chloroform were carried out with four different Nigerian plants namely: Pterocarpus osun (uhe), Lawsonia inermis (lalle), Bixa Orellana (annatto) and Hibiscus sabderriffa (zobo) to extract dye reagents from the plants. The ability of the dye reagents to replace Coomassie Brilliant Blue in the Bradford assay method of protein quantification were determined and compared. The solvents extracts gave good colourful results in the extraction of the dye reagents while only aqueous extract of Hisbiscus sabderiffa (zobo) gave similar results to that of solvent extracts. The solvent extracts obtained from Pterocarpus osun (uhe), Lawsonia inermis (lalle) and Bixa Orellana (annatto) plants could not be used to estimate amino acids from protein samples. However, solvent extracts of Hibiscus sabderriffa (zobo) was able to estimate amino acids from protein samples. The change in maximum wavelength (λmax) and the increased absorption with zobo dye reagent; on addition of protein samples showed that solvent extract of Hibiscus sabderiffa (zobo) dye has the potential to quantify and estimate amino acids in protein samples as much as the Coomassie Blue utilised in the Bradford assay method.
    [Show full text]
  • Site-Specific Tryptophan Labels Reveal Local Microsecond
    molecules Article Site-Specific Tryptophan Labels Reveal Local Microsecond–Millisecond Motions of Dihydrofolate Reductase Morgan B. Vaughn 1,*, Chloe Biren 2, Qun Li 2, Ashwin Ragupathi 2 and R. Brian Dyer 2,* 1 Division of Natural Sciences, Oglethorpe University, Atlanta, GA 30319, USA 2 Department of Chemistry, Emory University, Atlanta, GA 30322, USA; [email protected] (C.B.); [email protected] (Q.L.); [email protected] (A.R.) * Correspondence: [email protected] (M.B.V.); [email protected] (R.B.D.); Tel.: +1-404-504-1214 (M.B.V.); +1-404-727-6637 (R.B.D.) Academic Editor: Rafik Karaman Received: 24 July 2020; Accepted: 21 August 2020; Published: 22 August 2020 Abstract: Many enzymes are known to change conformations during their catalytic cycle, but the role of these protein motions is not well understood. Escherichia coli dihydrofolate reductase (DHFR) is a small, flexible enzyme that is often used as a model system for understanding enzyme dynamics. Recently, native tryptophan fluorescence was used as a probe to study micro- to millisecond dynamics of DHFR. Yet, because DHFR has five native tryptophans, the origin of the observed conformational changes could not be assigned to a specific region within the enzyme. Here, we use DHFR mutants, each with a single tryptophan as a probe for temperature jump fluorescence spectroscopy, to further inform our understanding of DHFR dynamics. The equilibrium tryptophan fluorescence of the mutants shows that each tryptophan is in a different environment and that wild-type DHFR fluorescence is not a simple summation of all the individual tryptophan fluorescence signatures due to tryptophan–tryptophan interactions.
    [Show full text]
  • Predicting Protein-Membrane Interfaces of Peripheral Membrane
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.28.450157; this version posted June 29, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Predicting protein-membrane interfaces of pe- ripheral membrane proteins using ensemble machine learning Alexios Chatzigoulas1,2,* and Zoe Cournia1,* 1Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece, 2Depart- ment of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece *To whom correspondence should be addressed. Abstract Motivation: Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane binding domains of peripheral membrane proteins are usually not known. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Results: Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating residues. We utilize available experimental data in the literature for training 21 machine learning classifiers and a voting classifier. Evaluation of the ensemble classifier accuracy pro- duced a macro-averaged F1 score = 0.92 and an MCC = 0.84 for predicting correctly membrane-pen- etrating residues on unknown proteins of an independent test set.
    [Show full text]
  • Atomic Modeling of Proteins Via Small Angle X-Ray Scattering (SAXS)
    Atomic modeling of proteins via Small Angle X-ray Scattering (SAXS) Susan Tsutakawa SIBYLS Beamline. Advanced Light Source Synchrotron, Lawrence Berkeley Natl Lab Six Take Home Messages on What can SAXS do for you? X-ray Scattering by Small Angle X-ray Scattering electrons provides distances measures all electron pair between electrons. distances in a protein in solution Atomic models can be quantitatively compared with SAXS data Atomic models are more SAXS can validate protein powerful than shape structure predictions because they can be tested. SAXS can reveal protein conformations occurring in solution at the atomic level If I could ask for any scientific app, what would it be? Accurate and reliable protein structure prediction For proteins with no known orthologs, structure predictions currently are not reliable. Amino Acid Protein Prediction Structure Prediction Sequence Server SIBYLS SAXS data I propose that Beamline input of SAXS data 12.3.1 can improve protein structure algoriths. As in crystallography, SAXS uses elastic scattering of X-rays, where the X-rays are scattered by an electron without a change in energy. Scattered X- rays constructively or destructively combine with each other. The X-ray scattering provides information on the distance between electrons. In SAXS, the intramolecular distances In Crystallography, these electrons are are constant; the scattering is related to each in crystallographic coherent, and the amplitudes are symmetry. added. SAXS is a distance method, measuring all electron pair distances. Scattering Curve Electron Pair Distance Histogram Fourier dmax Intensity (q) Intensity SAXS sample 30 ul q (Å-1) Protein 1-3 mg/ml Exact Buffer Distance information can validate an atomic model – as exemplified by validation of the May, 1953 model of DNA by fiber diffraction GeneticalImplications of the structure of Deoxyribonucleic Acid WatsonJ.D.
    [Show full text]
  • Conformational Proofreading: the Impact of Conformational
    Conformational Proofreading: The Impact of Conformational Changes on the Specificity of Molecular Recognition Yonatan Savir1 and Tsvi Tlusty1,* 1Department of Physics of Complex Systems, the Weizmann Institute of Science, Rehovot, Israel, 76100 *Corresponding author. E-mail: [email protected] Abstract To perform recognition, molecules must locate and specifically bind their targets within a noisy biochemical environment with many look-alikes. Molecular recognition processes, especially the induced-fit mechanism, are known to involve conformational changes. This arises a basic question: does molecular recognition gain any advantage by such conformational changes? By introducing a simple statistical-mechanics approach, we study the effect of conformation and flexibility on the quality of recognition processes. Our model relates specificity to the conformation of the participant molecules and thus suggests a possible answer: Optimal specificity is achieved when the ligand is slightly off target, that is a conformational mismatch between the ligand and its main target improves the selectivity of the process. This indicates that deformations upon binding serve as a conformational proofreading mechanism, which may be selected for via evolution. Introduction Practically all biological systems rely on the ability of bio-molecules to specifically recognize each other. Examples are antibodies targeting antigens, regulatory proteins binding DNA and enzymes catalyzing their substrates. These and other molecular recognizers must locate and preferentially interact with their specific targets among a vast variety of molecules that are often structurally similar. This task is further complicated by the inherent noise in the biochemical environment, whose magnitude is comparable with that of the non- covalent binding interactions [1-3].
    [Show full text]
  • SDSU Template, Version 11.1
    NONLINEAR LASER WAVE-MIXING DETECTION FOR CAPILLARY ELECTROPHORESIS AND MULTI-CHANNEL ARRAYS FOR BIOMEDICAL AND ENVIRONMENTAL APPLICATIONS _______________ A Thesis Presented to the Faculty of San Diego State University _______________ In Partial Fulfillment of the Requirements for the Degree Master of Science in Chemistry _______________ by Eric J. Maxwell Spring 2015 iii Copyright © 2015 by Eric J. Maxwell All Rights Reserved iv DEDICATION To my wife, Selena, for supporting me through all of the long nights and weekends of work that this program required. I cannot wait to start the next chapter in this journey. v ABSTRACT OF THE THESIS Nonlinear Laser Wave-Mixing Detection for Capillary Electrophoresis and Multi-Channel Arrays for Biomedical and Environmental Applications by Eric J. Maxwell Master of Science in Chemistry San Diego State University, 2015 Degenerate Four-Wave Mixing is demonstrated as a highly sensitive nonlinear spectroscopic detection method for biomedical and environmental targets. This is achieved through refractive index change within an absorbing liquid medium, which produces a laser- like signal beam. This signal has high spatial resolution, and may be collected with high efficiency against a nearly 100% dark background. The cubic dependence on laser power and square dependence on analyte concentration allow for high signal intensity in trace analysis applications. In this work, the Degenerate Four-Wave Mixing technique is coupled with capillary electrophoresis, immunoprecipitation or color-forming reactions to provide specificity. The veterinary drugs malachite green and crystal violet are shown to be detectable at concentrations as low as 6.9 x 10-10 M (2.5 x 10-19 mol) and 8.3 x 10-11 M (3.0 x 10-20 mol) respectively (S/N = 2).
    [Show full text]
  • M.Sc. [Botany] 346 13
    cover page as mentioned below: below: mentioned Youas arepage instructedcover the to updateupdate to the coverinstructed pageare asYou mentioned below: Increase the font size of the Course Name. Name. 1. IncreaseCourse the theof fontsize sizefont ofthe the CourseIncrease 1. Name. use the following as a header in the Cover Page. Page. Cover 2. the usein the followingheader a as as a headerfollowing the inuse the 2. Cover Page. ALAGAPPAUNIVERSITY UNIVERSITYALAGAPPA [Accredited with ’A+’ Grade by NAAC (CGPA:3.64) in the Third Cycle Cycle Third the in (CGPA:3.64) [AccreditedNAAC by withGrade ’A+’’A+’ Gradewith by NAAC[Accredited (CGPA:3.64) in the Third Cycle and Graded as Category–I University by MHRD-UGC] MHRD-UGC] by University and Category–I Graded as as Graded Category–I and University by MHRD-UGC] M.Sc. [Botany] 003 630 – KARAIKUDIKARAIKUDI – 630 003 346 13 EDUCATION DIRECTORATEDISTANCE OF OF DISTANCEDIRECTORATE EDUCATION BIOLOGICAL TECHNIQUES IN BOTANY I - Semester BOTANY IN TECHNIQUES BIOLOGICAL M.Sc. [Botany] 346 13 cover page as mentioned below: below: mentioned Youas arepage instructedcover the to updateupdate to the coverinstructed pageare asYou mentioned below: Increase the font size of the Course Name. Name. 1. IncreaseCourse the theof fontsize sizefont ofthe the CourseIncrease 1. Name. use the following as a header in the Cover Page. Page. Cover 2. the usein the followingheader a as as a headerfollowing the inuse the 2. Cover Page. ALAGAPPAUNIVERSITY UNIVERSITYALAGAPPA [Accredited with ’A+’ Grade by NAAC (CGPA:3.64) in the Third Cycle Cycle Third the in (CGPA:3.64) [AccreditedNAAC by withGrade ’A+’’A+’ Gradewith by NAAC[Accredited (CGPA:3.64) in the Third Cycle and Graded as Category–I University by MHRD-UGC] MHRD-UGC] by University and Category–I Graded as as Graded Category–I and University by MHRD-UGC] M.Sc.
    [Show full text]
  • Small Angle X-Ray Scattering Experiments of Monodisperse Samples Close to the Solubility Limit
    Small angle x-ray scattering experiments of monodisperse samples close to the solubility limit Erik W. Martina, Jesse B. Hopkinsb, Tanja Mittaga1 a Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis TN b The Biophysics Collaborative Access Team (BioCAT), Department of Biological Sciences, Illinois Institute of Technology, Chicago, IL 1Corresponding author: email address: [email protected] Abstract The condensation of biomolecules into biomolecular condensates via liquid-liquid phase separation (LLPS) is a ubiquitous mechanism that drives cellular organization. To enable these functions, biomolecules have evolved to drive LLPS and facilitate partitioning into biomolecular condensates. Determining the molecular features of proteins that encode LLPS will provide critical insights into a plethora of biological processes. Problematically, probing biomolecular dense phases directly is often technologically difficult or impossible. By capitalizing on the symmetry between the conformational behavior of biomolecules in dilute solution and dense phases, it is possible to infer details critical to phase separation by precise measurements of the dilute phase thus circumventing complicated characterization of dense phases. The symmetry between dilute and dense phases is found in the size and shape of the conformational ensemble of a biomolecule – parameters that small-angle x-ray scattering (SAXS) is ideally suited to probe. Recent technological advances have made it possible to accurately characterize samples of intrinsically disordered protein regions at low enough concentration to avoid interference from intermolecular attraction, oligomerization or aggregation, all of which were previously roadblocks to characterizing self-assembling proteins. Herein, we describe the pitfalls inherent to measuring such samples, the details required for circumventing these issues and analysis methods that place the results of SAXS measurements into the theoretical framework of LLPS.
    [Show full text]
  • Solution Small Angle X-Ray Scattering : Fundementals and Applications in Structural Biology
    The First NIH Workshop on Small Angle X-ray Scattering and Application in Biomolecular Studies Open Remarks: Ad Bax (NIDDK, NIH) Introduction: Yun-Xing Wang (NCI-Frederick, NIH) Lectures: Xiaobing Zuo, Ph.D. (NCI-Frederick, NIH) Alexander Grishaev, Ph.D. (NIDDK, NIH) Jinbu Wang, Ph.D. (NCI-Frederick, NIH) Organizer: Yun-Xing Wang (NCI-Frederick, NIH) Place: NCI-Frederick campus Time and Date: 8:30am-5:00pm, Oct. 22, 2009 Suggested reading Books: Glatter, O., Kratky, O. (1982) Small angle X-ray Scattering. Academic Press. Feigin, L., Svergun, D. (1987) Structure Analysis by Small-angle X-ray and Neutron Scattering. Plenum Press. Review Articles: Svergun, D., Koch, M. (2003) Small-angle scattering studies of biological macromolecules in solution. Rep. Prog. Phys. 66, 1735- 1782. Koch, M., et al. (2003) Small-angle scattering : a view on the properties, structures and structural changes of biological macromolecules in solution. Quart. Rev. Biophys. 36, 147-227. Putnam, D., et al. (2007) X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution. Quart. Rev. Biophys. 40, 191-285. Software Primus: 1D SAS data processing Gnom: Fourier transform of the I(q) data to the P(r) profiles, desmearing Crysol, Cryson: fits of the SAXS and SANS data to atomic coordinates EOM: fit of the ensemble of structural models to SAXS data for disordered/flexible proteins Dammin, Gasbor: Ab initio low-resolution structure reconstruction for SAXS/SANS data All can be obtained from http://www.embl-hamburg.de/ExternalInfo/Research/Sax/software.html MarDetector: 2D image processing Igor: 1D scattering data processing and manipulation SolX: scattering profile calculation from atomic coordinates Xplor/CNS: high-resolution structure refinement GASR: http://ccr.cancer.gov/staff/links.asp?profileid=5546 Part One Solution Small Angle X-ray Scattering: Basic Principles and Experimental Aspects Xiaobing Zuo (NCI-Frederick) Alex Grishaev (NIDDK) 1.
    [Show full text]