Gauge Symmetries and Structure of Proteins
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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 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 .......................................................................................................... -
Conformational Transition of H-Shaped Branched Polymers
1 Conformational Transition of H-shaped Branched Polymers Ashok Kumar Dasmahapatra* and Venkata Mahanth Sanka Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati – 781039, Assam, India PACS number(s): 83.80.Rs, 87.10.Rt, 82.20.Wt, 61.25.he * Corresponding author: Phone: +91-361-258-2273; Fax: +91-361-258-2291; Email address: [email protected] 2 ABSTRACT We report dynamic Monte Carlo simulation on conformational transition of H-shaped branched polymers by varying main chain (backbone) and side chain (branch) length. H- shaped polymers in comparison with equivalent linear polymers exhibit a depression of theta temperature accompanying with smaller chain dimensions. We observed that the effect of branches on backbone dimension is more pronounced than the reverse, and is attributed to the conformational heterogeneity prevails within the molecule. With increase in branch length, backbone is slightly stretched out in coil and globule state. However, in the pre-collapsed (cf. crumpled globule) state, backbone size decreases with the increase of branch length. We attribute this non-monotonic behavior as the interplay between excluded volume interaction and intra-chain bead-bead attractive interaction during collapse transition. Structural analysis reveals that the inherent conformational heterogeneity promotes the formation of a collapsed structure with segregated backbone and branch units (resembles to “sandwich” or “Janus” morphology) rather an evenly distributed structure comprising of all the units. The shape of the collapsed globule becomes more spherical with increasing either backbone or branch length. 3 I. INTRODUCTION Branched polymers are commodious in preparing tailor-made materials for various applications such as in the area of catalysis1, nanomaterials2, and biomedicines3. -
Electrically Sensing Hachimoji DNA Nucleotides Through a Hybrid Graphene/H-BN Nanopore Cite This: Nanoscale, 2020, 12, 18289 Fábio A
Nanoscale View Article Online PAPER View Journal | View Issue Electrically sensing Hachimoji DNA nucleotides through a hybrid graphene/h-BN nanopore Cite this: Nanoscale, 2020, 12, 18289 Fábio A. L. de Souza, †a Ganesh Sivaraman, †b,g Maria Fyta, †c Ralph H. Scheicher, *†d Wanderlã L. Scopel †e and Rodrigo G. Amorim *†f The feasibility of synthesizing unnatural DNA/RNA has recently been demonstrated, giving rise to new perspectives and challenges in the emerging field of synthetic biology, DNA data storage, and even the search for extraterrestrial life in the universe. In line with this outstanding potential, solid-state nanopores have been extensively explored as promising candidates to pave the way for the next generation of label- free, fast, and low-cost DNA sequencing. In this work, we explore the sensitivity and selectivity of a gra- phene/h-BN based nanopore architecture towards detection and distinction of synthetic Hachimoji nucleobases. The study is based on a combination of density functional theory and the non-equilibrium Green’s function formalism. Our findings show that the artificial nucleobases are weakly binding to the Creative Commons Attribution 3.0 Unported Licence. device, indicating a short residence time in the nanopore during translocation. Significant changes in the Received 9th June 2020, electron transmission properties of the device are noted depending on which artificial nucleobase resides Accepted 7th August 2020 in the nanopore, leading to a sensitivity in distinction of up to 80%. Our results thus indicate that the pro- DOI: 10.1039/d0nr04363j posed nanopore device setup can qualitatively discriminate synthetic nucleobases, thereby opening up rsc.li/nanoscale the feasibility of sequencing even unnatural DNA/RNA. -
The New Architectonics: an Invitation to Structural Biology
THE ANATOMICAL RECORD (NEW ANAT.) 261:198–216, 2000 FEATURE ARTICLE The New Architectonics: An Invitation To Structural Biology CLARENCE E. SCHUTT* AND UNO LINDBERG The philosophy of art might offer an epistemological basis for talking about the complexity of biological molecules in a meaningful way. The analysis of artistic compositions requires the resolution of intrinsic tensions between disparate sensory categories—color, line and form—not unlike those encountered in looking at the surfaces of protein molecules, where charge, polarity, hydrophobicity, and shape compete for our attentions. Complex living systems exhibit behaviors such as contraction waves moving along muscle fibers, or shivers passing through the growth cones of migrating neurons, that are easy to describe with common words, but difficult to explain in terms of the language of chemistry. The problem follows from a lack of everyday experience with processes that move towards equilibrium by switching between crystalline order and chain-like disorder, a commonplace occurrence in the submicroscopic world of proteins. Since most of what is understood about protein function comes from studies of isolated macromolecules in solution, a serious gap exists between what we know and what we would like to know about organized biological systems. Closing this gap can be achieved by recognizing that protein molecules reside in gradients of Gibbs free energy, where local forces and movements can be large compared with Brownian motion. Architectonics, a term borrowed from the philosophical literature, symbolizes the eventual union of the structure of theories—how our minds construct the world—with the theory of structures—or how stability is maintained in the chaotic world of microsystems. -
De Novo Nucleic Acids: a Review of Synthetic Alternatives to DNA and RNA That Could Act As † Bio-Information Storage Molecules
life Review De Novo Nucleic Acids: A Review of Synthetic Alternatives to DNA and RNA That Could Act as y Bio-Information Storage Molecules Kevin G Devine 1 and Sohan Jheeta 2,* 1 School of Human Sciences, London Metropolitan University, 166-220 Holloway Rd, London N7 8BD, UK; [email protected] 2 Network of Researchers on the Chemical Evolution of Life (NoR CEL), Leeds LS7 3RB, UK * Correspondence: [email protected] This paper is dedicated to Professor Colin B Reese, Daniell Professor of Chemistry, Kings College London, y on the occasion of his 90th Birthday. Received: 17 November 2020; Accepted: 9 December 2020; Published: 11 December 2020 Abstract: Modern terran life uses several essential biopolymers like nucleic acids, proteins and polysaccharides. The nucleic acids, DNA and RNA are arguably life’s most important, acting as the stores and translators of genetic information contained in their base sequences, which ultimately manifest themselves in the amino acid sequences of proteins. But just what is it about their structures; an aromatic heterocyclic base appended to a (five-atom ring) sugar-phosphate backbone that enables them to carry out these functions with such high fidelity? In the past three decades, leading chemists have created in their laboratories synthetic analogues of nucleic acids which differ from their natural counterparts in three key areas as follows: (a) replacement of the phosphate moiety with an uncharged analogue, (b) replacement of the pentose sugars ribose and deoxyribose with alternative acyclic, pentose and hexose derivatives and, finally, (c) replacement of the two heterocyclic base pairs adenine/thymine and guanine/cytosine with non-standard analogues that obey the Watson–Crick pairing rules. -
Chain Structure Characterization
CHAIN STRUCTURE CHARACTERIZATION Gregory Beaucage and Amit S. Kulkarni Department of Chemical and Materials Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0012 I. Introduction to Structure in Synthetic Macromolecules a) Dimensionality and Statistical Descriptions b) Chain Persistence and the Kuhn Unit c) Coil Structure and Chain Scaling Transitions d) Measures of Coil Size Rg and Rh II. Local Structure and its Ramifications a) Tacticity c) Branching d) Crystallization e) Hyperbranched Polymers III. Summary 1 I. Introduction to Structure in Synthetic Macromolecules a) Dimensionality and Statistical Descriptions Synthetic polymers display some physical characteristics that we can identify as native to this class of materials, particularly shear thinning rheology, rubber elasticity, and chain folded crystals. These properties are inherent to long-chain linear and weakly branched molecules and are not drastically different across a wide range of chemical make-ups. We can consider these features to define synthetic macromolecules as a distinct category of materials. The realization of this special category of materials necessitated the definition of a structural model broad enough to encompass nylon to polyethylene yet specific enough that detailed analytically available features could be used to define the major properties of interest, especially those native to this class of materials. This structural model for polymer chains is based on the random walk statistics observed by Robert Brown in studies of pollen grains and explained by Einstein in 1905. It is a trivial exercise to construct a random walk on a cubic lattice using a PC, Figure 1. From such a walk we can observe certain features of the general model for a polymer chain. -
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.