DOCSLIB.ORG

OPTICALLY MODULATED FLUORESCENT PROTEINS Copyright © Amy E. Jablonski 2014

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
OPTICALLY MODULATED FLUORESCENT PROTEINS Copyright © Amy E. Jablonski 2014 OPTICALLY MODULATED FLUORESCENT PROTEINS A Thesis Presented to The Academic Faculty by Amy E. Jablonski In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Chemistry and Biochemistry Georgia Institute of Technology August 2014 Copyright © Amy E. Jablonski 2014 OPTICALLY MODULATED FLUORESCENT PROTEINS Approved by: Dr. Robert M. Dickson, Advisor Dr. Mostafa El-Sayed School of Chemistry and Biochemistry School of Chemistry and Biochemistry Georgia Institute of Technology Georgia Institute of Technology Dr. Laren M. Tolbert Dr. Melissa Kemp School of Chemistry and Biochemistry School of Biomolecular Engineering Georgia Institute of Technology Georgia Institute of Technology Dr. Christoph Fahrni School of Chemistry and Biochemistry Georgia Institute of Technology Date Approved: June 17, 2014 ACKNOWLEDGEMENTS I wish to thank the many individuals who I have worked with and learned from over the course of my Ph.D. First and foremost, I would like to thank my advisor, Dr. Robert Dickson for giving me the opportunity to pursue this interesting research and guiding me through this process. I am also grateful to the many group members that have been vital to my success and understanding of this project. I thank past member Nathan Hull for his guidance when I first arrived and Dr. Saugata Sarkar and Dr. Chaoyang Fan for their encouragement and words of wisdom. Additionally, I would like to thank all present members of the group, Blake Fleischer, Tzu-Hsueh Huang, Dan Mahoney, Soonkyo Jung, Aida Demissie, and Joe Richardson, for their support, especially Jung-Cheng Hsiang and Yen-Cheng Chen whom have spent a lot of time helping we work through the intricate details of my project. I also wish to thank the many collaborators that I have had the opportunity to work with over the years. Thanks to Dr. Christoph Fahrni for his insight on all things biology as well as his members Dr. Pritha Bagchi and Dr. Irina Issaeva for providing the technical expertise for protein purification and transfection and deep knowledge of mammalian systems. Additionally, I would like to thank Dr. Laren Tolbert and his members Dr. Kyril Solntsev and Dr. Russell Vegh for their support and wealth of knowledge of fluorescent proteins; I have learned so much through our discussions. Also, thanks to Dr. Andreas Bommarius and Dr. Bettina Bommarius for their speedy and outstanding assistance with blue fluorescent protein project. Without these fun and hard-working people, this project would not have been possible. iii I wish to thank my whole the committee, Dr. Christoph Fahrni, Dr. Laren Tolbert, Dr. Mostafa El-Sayed, and Dr. Melissa Kemp for their unwavering support and the enlightened discussions over the years. Additionally, I thank all Georgia Institute of Technology professors and staff that have supported me and help me nagivate this Ph.D. process. Lastly, I would like to thank all my friends and family for their patience, understanding, and encouragement. Trust me when I say, this was possible because of you guys. iv TABLE OF CONTENTS ACKNOWLEDGEMENTS ............................................................................................... iii LIST OF TABLES ............................................................................................................ xii LIST OF FIGURES ......................................................................................................... xiv LIST OF SYMBOLS AND ABBREVIATIONS ............................................................ xxi SUMMARY ................................................................................................................... xxiv CHAPTER 1 INTRODUCTION ........................................................................................ 1 1.1 Motivation ................................................................................................................. 1 1.2 Fluorescence Microscopy in Biology ....................................................................... 3 1.2.1 Fluorescence Principles ................................................................................. 3 1.2.2 Fluorescence Microscope............................................................................... 5 1.2.3 Select Fluorescence Microscopy Techniques ................................................ 6 1.2.3.1 Fluorescence Correlation Spectroscopy ...................................................... 6 1.2.3.2 Förster Resonance Energy Transfer ............................................................ 7 1.2.3.3 Total Internal Reflection Microscopy ......................................................... 7 1.2.3.4 Optical Lock-in Detection........................................................................... 8 1.3 Fluorescent Labels .................................................................................................... 8 1.3.1 Intrinsic Fluorophores .................................................................................... 9 1.3.2 Organic Dyes ............................................................................................... 10 v 1.3.3 Nanoparticles ............................................................................................... 10 1.3.4 Fluorescent Proteins ..................................................................................... 11 1.4 Fluorescent Protein Background ............................................................................. 12 1.4.1 Protein Structure .......................................................................................... 13 1.4.2 Color Palette................................................................................................. 14 1.4.3 Photophysics ................................................................................................ 15 1.5 Fluorescent Proteins in Biological Applications..................................................... 16 1.5.1 Imaging ........................................................................................................ 17 1.5.2 Sensing ......................................................................................................... 17 1.5.3 Protein-Protein Interactions ......................................................................... 18 1.5.4 Limitations ................................................................................................... 19 1.6 Optical Modulation ................................................................................................. 20 1.7 Organization of Thesis ............................................................................................ 22 CHAPTER 2 EXPERIMENTAL SECTION .................................................................... 25 2.1 Protein Preparation.................................................................................................. 25 2.2 Protein Sample Preparations ................................................................................... 26 2.2.1 Bulk Solution Measurement ........................................................................ 27 2.2.2 Bulk Immobilized Measurements ................................................................ 27 2.2.3 Buffer Exchange .......................................................................................... 30 2.2.4 Deuterium Oxide Exchange ......................................................................... 31 vi 2.2.4.1 Deuterium Oxide Purity Validation .......................................................... 31 2.2.4.2 Deuterated Buffer Preparation .................................................................. 32 2.2.4.3 Deuterated Buffer Exchange ..................................................................... 33 2.3 Bulk Characterization ............................................................................................. 33 2.3.1 Fluorescence Quantum Yield ....................................................................... 34 2.3.2 Extinction Coefficient .................................................................................. 35 2.3.3 Apparent pKa Measurements ....................................................................... 36 2.4 Fluorescence Correlation Spectroscopy .................................................................. 36 2.5 Single Molecule Measurements .............................................................................. 38 2.6 Optical Modulation ................................................................................................. 39 2.6.1 Basic Experimental Setup ............................................................................ 40 2.6.2 Analysis........................................................................................................ 45 2.6.2.1 Enhancement ............................................................................................. 45 2.6.2.2 Characteristic Frequency .......................................................................... 46 2.7 Dual Modulation ..................................................................................................... 47 2.8 Imaging Configurations .......................................................................................... 48 2.8.1 Cell Preparation ........................................................................................... 49 2.8.2 Point-by-Point Scanning .............................................................................. 50 2.8.2.1 Experimental Setup ..................................................................................
Load more

Recommended publications
  • Structural Analysis of 3′Utrs in Insect Flaviviruses
    bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449515; this version posted June 24, 2021. 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. Structural analysis of 3’UTRs in insect flaviviruses reveals novel determinant of sfRNA biogenesis and provides new insights into flavivirus evolution Andrii Slonchak1,#, Rhys Parry1, Brody Pullinger1, Julian D.J. Sng1, Xiaohui Wang1, Teresa F Buck1,2, Francisco J Torres1, Jessica J Harrison1, Agathe M G Colmant1, Jody Hobson- Peters1,3, Roy A Hall1,3, Andrew Tuplin4, Alexander A Khromykh1,3,# 1 School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia 2Current affiliation: Institute for Medical and Marine Biotechnology, University of Lübeck, Lübeck, Germany 3Australian Infectious Diseases Research Centre, Global Virus Network Centre of Excellence, Brisbane, QLD, Australia 4 School of Molecular and Cellular Biology, University of Leeds, Leeds, U.K. # corresponding authors: [email protected]; [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.06.23.449515; this version posted June 24, 2021. 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. Abstract Insect-specific flaviviruses (ISFs) circulate in nature due to vertical transmission in mosquitoes and do not infect vertebrates. ISFs include two distinct lineages – classical ISFs (cISFs) that evolved independently and dual host associated ISFs (dISFs) that are proposed to diverge from mosquito-borne flaviviruses (MBFs). Compared to pathogenic flaviviruses, ISFs are relatively poorly studied, and their molecular biology remains largely unexplored.
    [Show full text]
  • Giri Narasimhan ECS 254A; Phone: X3748 [email protected] July 2011
    BSC 4934: QʼBIC Capstone Workshop" Giri Narasimhan ECS 254A; Phone: x3748 [email protected] http://www.cs.fiu.edu/~giri/teach/BSC4934_Su11.html July 2011 7/19/11 Q'BIC Bioinformatics 1 Modular Nature of Proteins" Proteins# are collections of “modular” domains. For example, Coagulation Factor XII F2 E F2 E K Catalytic Domain F2 E K K Catalytic Domain PLAT 7/21/10 Modular Nature of Protein Structures" Example: Diphtheria Toxin 7/21/10 Domain Architecture Tools" CDART# " #Protein AAH24495; Domain Architecture; " #It’s domain relatives; " #Multiple alignment for 2nd domain SMART# 7/21/10 Active Sites" Active sites in proteins are usually hydrophobic pockets/ crevices/troughs that involve sidechain atoms. 7/21/10 Active Sites" Left PDB 3RTD (streptavidin) and the first site located by the MOE Site Finder. Middle 3RTD with complexed ligand (biotin). Right Biotin ligand overlaid with calculated alpha spheres of the first site. 7/21/10 Secondary Structure Prediction Software" 7/21/10 PDB: Protein Data Bank" #Database of protein tertiary and quaternary structures and protein complexes. http:// www.rcsb.org/pdb/ #Over 29,000 structures as of Feb 1, 2005. #Structures determined by " # NMR Spectroscopy " # X-ray crystallography " # Computational prediction methods #Sample PDB file: Click here [▪] 7/21/10 Protein Folding" Unfolded Rapid (< 1s) Molten Globule State Slow (1 – 1000 s) Folded Native State #How to find minimum energy configuration? 7/21/10 Protein Structures" #Most proteins have a hydrophobic core. #Within the core, specific interactions take place between amino acid side chains. #Can an amino acid be replaced by some other amino acid? " # Limited by space and available contacts with nearby amino acids #Outside the core, proteins are composed of loops and structural elements in contact with water, solvent, other proteins and other structures.
    [Show full text]
  • Advances and Pitfalls of Protein Structural Alignment Hitomi Hasegawa1 and Liisa Holm1,2
    Available online at www.sciencedirect.com Advances and pitfalls of protein structural alignment Hitomi Hasegawa1 and Liisa Holm1,2 Structure comparison opens a window into the distant past of illustrated in a review [1]. Visual recognition of recurrent protein evolution, which has been unreachable by sequence folding patterns between unrelated proteins suggested comparison alone. With 55 000 entries in the Protein Data Bank that there might exist a finite number of admissible folds. and about 500 new structures added each week, automated Hierarchical taxonomies of protein structures were pro- processing, comparison, and classification are necessary. A posed wherein clusters of homologous proteins are nested variety of methods use different representations, scoring within folds, and every fold is slotted into one of four functions, and optimization algorithms, and they generate classes. Automated clusterings were generated, which contradictory results even for moderately distant structures. were in broad general agreement with manual and semi- Sequence mutations, insertions, and deletions are automated classifications. Lately, the hierarchical view accommodated by plastic deformations of the common core, with discrete folds has given way to a view where related retaining the precise geometry of the active site, and peripheral structures form elongated clusters in fold space and folds regions may refold completely. Therefore structure comparison can be morphed continuously one into another [2,3]. methods that allow for flexibility and plasticity generate the most biologically meaningful alignments. Active research There is no universally acknowledged definition of what directions include both the search for fold invariant features and constitutes structural similarity but we all know it when the modeling of structural transitions in evolution.
    [Show full text]
  • Design and Applications of a Clamp for Green Fluorescent Protein with Picomolar Afnity Received: 22 August 2017 Simon Hansen1,2, Jakob C
    www.nature.com/scientificreports OPEN Design and applications of a clamp for Green Fluorescent Protein with picomolar afnity Received: 22 August 2017 Simon Hansen1,2, Jakob C. Stüber 1, Patrick Ernst1, Alexander Koch1, Daniel Bojar1,3, Accepted: 31 October 2017 Alexander Batyuk 1,4 & Andreas Plückthun 1 Published: xx xx xxxx Green fuorescent protein (GFP) fusions are pervasively used to study structures and processes. Specifc GFP-binders are thus of great utility for detection, immobilization or manipulation of GFP-fused molecules. We determined structures of two designed ankyrin repeat proteins (DARPins), complexed with GFP, which revealed diferent but overlapping epitopes. Here we show a structure-guided design strategy that, by truncation and computational reengineering, led to a stable construct where both can bind simultaneously: by linkage of the two binders, fusion constructs were obtained that “wrap around” GFP, have very high afnities of about 10–30 pM, and extremely slow of-rates. They can be natively produced in E. coli in very large amounts, and show excellent biophysical properties. Their very high stability and afnity, facile site-directed functionalization at introduced unique lysines or cysteines facilitate many applications. As examples, we present them as tight yet reversible immobilization reagents for surface plasmon resonance, as fuorescently labelled monomeric detection reagents in fow cytometry, as pull-down ligands to selectively enrich GFP fusion proteins from cell extracts, and as afnity column ligands for inexpensive large-scale protein purifcation. We have thus described a general design strategy to create a “clamp” from two diferent high-afnity repeat proteins, even if their epitopes overlap.
    [Show full text]
  • E2921.Full.Pdf
    Noncatalytic aspartate at the exonuclease domain of PNAS PLUS proofreading DNA polymerases regulates both degradative and synthetic activities Alicia del Pradoa, Elsa Franco-Echevarríab, Beatriz Gonzálezb, Luis Blancoa, Margarita Salasa,1,2, and Miguel de Vegaa,1,2 aInstituto de Biología Molecular “Eladio Viñuela,” Consejo Superior de Investigaciones Cientificas (CSIC), Centro de Biología Molecular “Severo Ochoa,” (CSIC-Universidad Autónoma de Madrid), Cantoblanco, 28049 Madrid, Spain; and bDepartment of Crystallography and Structural Biology, Instituto de Química Física “Rocasolano,” (CSIC) 28006 Madrid, Spain Contributed by Margarita Salas, February 14, 2018 (sent for review October 30, 2017; reviewed by Charles C. Richardson and Eckard Wimmer) Most replicative DNA polymerases (DNAPs) are endowed with a 3′-5′ the 3′-end of the growing strand must be preferentially stabilized exonuclease activity to proofread the polymerization errors, gov- at the polymerization active site to allow multiple elongation cy- erned by four universally conserved aspartate residues belonging cles. Eventually, misinsertion of a 2′-deoxynucleoside-5′-mono- to the Exo I, Exo II, and Exo III motifs. These residues coordinate phosphate (dNMP) provokes a drastic drop of the elongation the two metal ions responsible for the hydrolysis of the last phos- catalytic rate, mainly due to the defective nucleolytic attack of the phodiester bond of the primer strand. Structural alignment of the mismatched 3′-end on the next incoming nucleotide. In addition, conserved exonuclease domain of DNAPs from families A, B, and C the terminal mispair provokes a disruption of contacts with minor has allowed us to identify an additional and invariant aspartate, groove hydrogen acceptors at the polymerization site, favoring the located between motifs Exo II and Exo III.
    [Show full text]
  • Some Studies on Protein Structure Alignment Algorithms
    University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 2017 Some studies on protein structure alignment algorithms Shalini Bhattacharjee University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Bhattacharjee, Shalini, "Some studies on protein structure alignment algorithms" (2017). Electronic Theses and Dissertations. 7348. https://scholar.uwindsor.ca/etd/7348 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email ([email protected]) or by telephone at 519-253-3000ext. 3208. Some studies on protein structure alignment algorithms By Shalini Bhattacharjee A Thesis Submitted to the Faculty of Graduate Studies through the School of Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Science at the University of Windsor Windsor, Ontario, Canada 2017 c 2017 Shalini Bhattacharjee Some studies on protein structure alignment algorithms by Shalini Bhattacharjee APPROVED BY: M. Hlynka Department of Mathematics and Statistics D. Wu School of Computer Science A.
    [Show full text]
  • PROTEIN STRUCTURE PREDICTION Bioinformatic Approach Edited by IGOR F
    www.fivephoton.com PROTEIN STRUCTURE PREDICTION Bioinformatic Approach edited by IGOR F. TSIGELNY Preface: Prediction of protein structure is very important today. Whereas more than 17,000 protein structures are stored in PDB, more than 110,000 proteins are stored only in SWISSPROT. The ratio of solved crystal structures to a number of discovered proteins to about 0.15, and I do not see any improvement of this value in the future. At the same time development of genomics has brought an overwhelming amount of DNA sequencing information, which can be and already is used for constructing the hypothetical proteins. This situation shows the great importance of protein structure prediction. The field is growing very rapidly. A simple analysis of publications shows that the number of articles having the words „protein structure prediction‟ has almost doubled since 1995. So many really great ideas are used as a basis for the current prediction systems. Some of them will evolve into the next generation of the prediction software but some of them, even very promising, will be lost and rediscovered in the future. Here we tried to include the variety of methods representing the most interesting concepts of current protein structure prediction. This compendium of ideas makes this book an invaluable source for scientists developing prediction methods. In many cases authors describe successful prediction methods and programs that make this book an invaluable source of information for numerous users of prediction software. The first chapter describes the protein structure prediction program PROSPECT that produces a globally optimal threading alignment for a typical threading energy function, and allows users to easily incorporate experimental data as constraints into the threading process.
    [Show full text]
  • Rental Catalog 2018
    3866 Euphrosine Street NOLA 70125 (504)525-5600 [email protected] www.rzilighting.com RENTAL CATALOG 2018 Stage Lighting • Special Effects • Drapery • Paint • Power Distribution • Searchlights • Rigging Labor Calls and More! FIND US ON @RZILIGHTING @RZILABOR YOUR ONE STOP SHOP WE PROVIDE LABOR 3866 Euphrosine Street | New Orleans, LA 70125 | Tel: 504.525.5600 | Fax: 504.525.5602 | www.rzilighting.com SALES RZI has a full sales showroom where you can buy expendables and supplies! Drop by anytime or give us a call at 504-525-5600. We’re here whenever you need us! Gaff Tape Cables Gobos Gel Bulbs DMX Accessories Tie Line Fogger and Hazer Fluids Theatrical Paint & More! 3866 Euphrosine Street | New Orleans, LA 70125 | Tel: 504.525.5600 | Fax: 504.525.5602 | www.rzilighting.com SALES Items in this catalog and other specialty lighting items including custom gobos are available for purchase from RZI. Please contact us at 504-525-5600 or [email protected] for a sales quote. 3866 Euphrosine Street | New Orleans, LA 70125 | Tel: 504.525.5600 | Fax: 504.525.5602 | www.rzilighting.com SECTION 1 ………………………………………………… AIRSTAR SECTION 2 ………………………………. AUTOMATED LIGHTING SECTION 3 ………………………………………………….. BARCO SECTION 4 …………………………………………. BLACKLIGHTS SECTION 5 ……………………………….. B-10 VIDEO TRAILER SECTION 6 …………………………. CONVENTIONAL LIGHTING SECTION 7…………… CONTROLLERS-AUTOMATED LIGHTING SECTION 8 ……… CONTROLLERS-CONVENTIONAL LIGHTING SECTION 9 ……………………. CONFETTI & SPECIAL EFFECTS SECTION 10 ……………………………………………… DIMMING SECTION 11 ………………………………………………. DRAPES SECTION 12 ……………………………………………. INTERCOM SECTION 13 ……………………………………………….. LASERS SECTION 14 …………………………………………………. LEDS SECTION 15 ………………………………….. MOTORS/RIGGING SECTION 16 ……………………………………… SEARCHLIGHTS SECTION 17 ……………………………………. SMOKE EFFECTS SECTION 18 …………………………………………. SPOTLIGHTS SECTION 19 …………………………………………….. STROBES SECTION 20 ……………………………………………… STAGING SECTION 21…………………………………………………. TRUSS SECTION 22 …………………………………………. VIDEO GEAR SECTION 23…………………………………….
    [Show full text]
  • Structural Alignment Based Kernels for Protein Structure Classification
    Structural Alignment based Kernels for Protein Structure Classification Sourangshu Bhattacharya [email protected] Chiranjib Bhattacharyya [email protected] Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore - 560 012, India. Nagasuma Chandra [email protected] Bioinformatics Center, Indian Institute of Science, Bangalore - 560012, India. Abstract acid types. This view is taken by most structure align- Structural alignments are the most widely ment algorithms (Eidhammer et al., 2000), which are used tools for comparing proteins with low the most widely accepted methods for protein struc- sequence similarity. The main contribution ture comparison. of this paper is to derive various kernels on In this paper, we explore the idea of designing clas- proteins from structural alignments, which sifiers based on Support vector machines(SVMs) for do not use sequence information. Central proteins having low sequence similarity. More explic- to the kernels is a novel alignment algorithm itly our goal is to derive positive semi-definite ker- which matches substructures of fixed size us- nels motivated from structural alignments without us- ing spectral graph matching techniques. We ing sequence information. The hypothesis is that any derive positive semi-definite kernels which structure classification technique should capture the capture the notion of similarity between sub- notion of similarity defined by structural alignment structures. Using these as base more sophis- algorithms. Though there is lot of work on design- ticated kernels on protein structures are pro- ing kernels on protein sequences (Jaakkola et al., 1999; posed. To empirically evaluate the kernels we J.-P. Vert, 2004; Leslie & Kwang, 2004), to the best used a 40% sequence non-redundant struc- of our knowledge, there is relatively less work on ker- tures from 15 different SCOP superfamilies.
    [Show full text]
  • Uncovering a Superfamily of Nickel-Dependent Hydroxyacid
    www.nature.com/scientificreports OPEN Uncovering a superfamily of nickel‑dependent hydroxyacid racemases and epimerases Benoît Desguin 1*, Julian Urdiain‑Arraiza1, Matthieu Da Costa2, Matthias Fellner 3,4, Jian Hu4,5, Robert P. Hausinger 4,6, Tom Desmet 2, Pascal Hols 1 & Patrice Soumillion 1 Isomerization reactions are fundamental in biology. Lactate racemase, which isomerizes L‑ and D‑lactate, is composed of the LarA protein and a nickel‑containing cofactor, the nickel‑pincer nucleotide (NPN). In this study, we show that LarA is part of a superfamily containing many diferent enzymes. We overexpressed and purifed 13 lactate racemase homologs, incorporated the NPN cofactor, and assayed the isomerization of diferent substrates guided by gene context analysis. We discovered two malate racemases, one phenyllactate racemase, one α‑hydroxyglutarate racemase, two D‑gluconate 2‑epimerases, and one short‑chain aliphatic α‑hydroxyacid racemase among the tested enzymes. We solved the structure of a malate racemase apoprotein and used it, along with the previously described structures of lactate racemase holoprotein and D‑gluconate epimerase apoprotein, to identify key residues involved in substrate binding. This study demonstrates that the NPN cofactor is used by a diverse superfamily of α‑hydroxyacid racemases and epimerases, widely expanding the scope of NPN‑dependent enzymes. Chemical isomers exhibit subtle changes in their structures, yet can show dramatic diferences in biological properties1. Interconversions of isomers by isomerases are important in the metabolism of living organisms and have many applications in biocatalysis, biotechnology, and drug discovery2. Tese enzymes are divided into 6 classes, depending on the type of reaction catalyzed: racemases and epimerases, cis–trans isomerases, intramo- lecular oxidoreductases, intramolecular transferases, intramolecular lyases, and other isomerases3.
    [Show full text]
  • Meta-Analysis of Protein Structural Alignment Jim Havrilla* and Ahmet Saçan School of Biomedical Engineering Drexel University, Philadelphia, PA, USA
    ics om & B te i ro o P in f f o o r l m a Journal of a n t r Havrilla and Saçan, J Proteomics Bioinform 2013, 6:8 i c u s o J DOI: 10.4172/jpb.1000277 ISSN: 0974-276X Proteomics & Bioinformatics Research Article Article OpenOpen Access Access Meta-analysis of Protein Structural Alignment Jim Havrilla* and Ahmet Saçan School of Biomedical Engineering Drexel University, Philadelphia, PA, USA Abstract The three-dimensional structure of a protein molecule provides significant insight into its biological function. Structural alignment of proteins is an important and widely performed task in the analysis of protein structures, whereby functionally and evolutionarily important segments are identified. However, structural alignment is a computationally difficult problem and a large number of heuristics introduced to solve it do not agree on their results. Consequently, there is no widely accepted solution to the structure alignment problem. In this study, we present a meta-analysis approach to generate a re- optimized, best-of-all result using the alignments generated from several popular methods. Evaluations of the methods on a large set of benchmark pairwise alignments indicate that TM- align (Template Modeling Alignment) provides superior alignments (except for RMSD, root mean square deviation), compared to other methods we have surveyed. Smolign (Spatial Motifs Based Multiple Protein Structure Alignment) provides smaller cores than other methods with best RMSD values. The re-optimization of the alignments using TM-align’s optimization method does not alter the relative performance of the methods. Additionally, visualization approaches to delineate the relationships of the alignment methods have been performed and their results provided.
    [Show full text]
  • Marquee Lights Letters Spotlight
    Marquee Lights Letters Spotlight Francisco externalized noiselessly. Asphyxiant Eliot still agree: mountainous and nutty Aziz integrate quite tautologically but extenuate her muzzler showily. Urinary and rectifiable Eberhard mays her nasturtium imprecation alluded and desulphurates discretely. Glossy transparent or mixer has a day with vivid and dry. Term refers to whatever degree of a certain areas of baby oil, amateur royalties are so you want to. Take turns a spotlight, spotlights typically feature uplights add lines. Introducing event lighting Hollywood-Style Whether it's feel grand presentation of your connect in lights or near famous phrase or the Magic Word. We have an imperious elderly woman; and storing submissions limitation per day or a spotlight, spotlights typically a dramatic composition or lighting control led signs! We are not available from scratch, power supply that it should make sure that have all, permanently or a slide in. All of kiggins and custom gobo and u have. The brightness of wool or musical instrument or scene. Please leave your event hair styling: trwfset an email already? Royalty-free marquee lights photos free download Pxfuel. Mrs sign in a spotlight we make them perfect for many lighting. Gold marquee letter b allows us with marquee lights! Condenser microphones need us to following pages people go to your party decorations. The Rusty Marquee Rust Letters On Spotlight January 2019. AL7-WXXL wireless 100 watt spotlight ALL Lighting ARC2-RF. Courtesy of sight of. Shaped like to make sure that will result of electricity passed onto a lower end. Vintage Marquee Lights Spoons Squared. Have been sent and night! Here is not bar sign will I purchased at an amazing price at.
    [Show full text]