Surface of Bacteriorhodopsin Revealed by High-Resolution Electron
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Biomolecules
CHAPTER 3 Biomolecules 3.1 Carbohydrates In the previous chapter you have learnt about the cell and 3.2 Fatty Acids and its organelles. Each organelle has distinct structure and Lipids therefore performs different function. For example, cell membrane is made up of lipids and proteins. Cell wall is 3.3 Amino Acids made up of carbohydrates. Chromosomes are made up of 3.4 Protein Structure protein and nucleic acid, i.e., DNA and ribosomes are made 3.5 Nucleic Acids up of protein and nucleic acids, i.e., RNA. These ingredients of cellular organelles are also called macromolecules or biomolecules. There are four major types of biomolecules— carbohydrates, proteins, lipids and nucleic acids. Apart from being structural entities of the cell, these biomolecules play important functions in cellular processes. In this chapter you will study the structure and functions of these biomolecules. 3.1 CARBOHYDRATES Carbohydrates are one of the most abundant classes of biomolecules in nature and found widely distributed in all life forms. Chemically, they are aldehyde and ketone derivatives of the polyhydric alcohols. Major role of carbohydrates in living organisms is to function as a primary source of energy. These molecules also serve as energy stores, 2021-22 Chapter 3 Carbohydrade Final 30.018.2018.indd 50 11/14/2019 10:11:16 AM 51 BIOMOLECULES metabolic intermediates, and one of the major components of bacterial and plant cell wall. Also, these are part of DNA and RNA, which you will study later in this chapter. The cell walls of bacteria and plants are made up of polymers of carbohydrates. -
Density Estimation for Protein Conformation Angles Using a Bivariate Von Mises Distribution and Bayesian Nonparametrics
Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics Kristin P. LENNOX, David B. DAHL, Marina VANNUCCI, and Jerry W. TSAI Interest in predicting protein backbone conformational angles has prompted the development of modeling and inference procedures for bivariate angular distributions. We present a Bayesian approach to density estimation for bivariate angular data that uses a Dirichlet process mixture model and a bivariate von Mises distribution. We derive the necessary full conditional distributions to fit the model, as well as the details for sampling from the posterior predictive distribution. We show how our density estimation method makes it possible to improve current approaches for protein structure prediction by comparing the performance of the so-called ‘‘whole’’ and ‘‘half’’ position dis- tributions. Current methods in the field are based on whole position distributions, as density estimation for the half positions requires techniques, such as ours, that can provide good estimates for small datasets. With our method we are able to demonstrate that half position data provides a better approximation for the distribution of conformational angles at a given sequence position, therefore providing increased efficiency and accuracy in structure prediction. KEY WORDS: Angular data; Density estimation; Dirichlet process mixture model; Torsion angles; von Mises distribution. 1. INTRODUCTION and Ohlson 2002; Lovell et al. 2003; Rother, Sapiro, and Pande 2008), but they behave poorly for these subdivided datasets. Computational structural genomics has emerged as a pow- This is unfortunate, because these subsets provide structure erful tool for better understanding protein structure and func- prediction that is more accurate, as it utilizes more specific tion using the wealth of data from ongoing genome projects. -
Structure Validation Article
14 STRUCTURAL QUALITY ASSURANCE Roman A. Laskowski The experimentally determined three-dimensional (3D) structures of proteins and nucleic acids represent the knowledge base from which so much understanding of biological processes has been derived over the last three decades of the twentieth cen- tury. Individual structures have provided explanations of specific biochemical functions and mechanisms, while comparisons of structures have given insights into general principles governing these complex molecules, the interactions they make, and their biological roles. The 3D structures form the foundation of structural bioinformatics; all structural analyses depend on them and would be impossible without them. Therefore, it is crucial to bear in mind two important truths about these structures, both of which result from the fact that they have been determined experimentally. The first is that the result of any experiment is merely a model that aims to give as good an explanation for the experimental data as possible. The term structure is commonly used, but you should realize that this should be correctly read as model. As such the model may be an accurate and meaningful representation of the molecule, or it may be a poor one. The quality of the data and the care with which the experiment has been performed will determine which it is. Independently performed experiments can arrive at very similar models of the same molecule; this suggests that both are accurate representations, that they are good models. The second important truth is that any experiment, however carefully performed, will have errors associated with it. These errors come in two distinct varieties: sys- tematic and random. -
Chapter 13 Protein Structure Learning Objectives
Chapter 13 Protein structure Learning objectives Upon completing this material you should be able to: ■ understand the principles of protein primary, secondary, tertiary, and quaternary structure; ■use the NCBI tool CN3D to view a protein structure; ■use the NCBI tool VAST to align two structures; ■explain the role of PDB including its purpose, contents, and tools; ■explain the role of structure annotation databases such as SCOP and CATH; and ■describe approaches to modeling the three-dimensional structure of proteins. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Overview: protein structure The three-dimensional structure of a protein determines its capacity to function. Christian Anfinsen and others denatured ribonuclease, observed rapid refolding, and demonstrated that the primary amino acid sequence determines its three-dimensional structure. We can study protein structure to understand problems such as the consequence of disease-causing mutations; the properties of ligand-binding sites; and the functions of homologs. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Protein primary and secondary structure Results from three secondary structure programs are shown, with their consensus. h: alpha helix; c: random coil; e: extended strand Protein tertiary and quaternary structure Quarternary structure: the four subunits of hemoglobin are shown (with an α 2β2 composition and one beta globin chain high- lighted) as well as four noncovalently attached heme groups. The peptide bond; phi and psi angles The peptide bond; phi and psi angles in DeepView Protein secondary structure Protein secondary structure is determined by the amino acid side chains. -
Electron Crystallography of Ultrathin 3D Protein Crystals: Atomic Model with Charges
Electron crystallography of ultrathin 3D protein crystals: Atomic model with charges Koji Yonekura (米倉 功治)a,b, Kazuyuki Kato (加藤 一幸)c, Mitsuo Ogasawara (小笠原 光雄)b, Masahiro Tomita (富田 正弘)b,d, and Chikashi Toyoshima (豊島 近)b,1 aBiostructural Mechanism Laboratory, RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo, Hyogo 679-5148, Japan; bInstitute of Molecular and Cellular Biosciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan; cHitachi High-Tech Fielding Corporation, 4-28-8 Yotsuya, Shinjuku-ku, Tokyo, 160-0004, Japan; and dHitachi High-Technologies Corporation, 1-24-14 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8717, Japan Contributed by Chikashi Toyoshima, January 23, 2015 (sent for review August 28, 2014) Membrane proteins and macromolecular complexes often yield F and G). These features of Coulomb potential maps result from crystals too small or too thin for even the modern synchrotron the fact that atomic scattering factors for electrons vary consid- X-ray beam. Electron crystallography could provide a powerful erably over a range of spatial frequency depending on the means for structure determination with such undersized crystals, charged state (Fig. 1A) and can become close to zero or even − as protein atoms diffract electrons four to five orders of magni- negative (e.g., for O , Fig. 1A). An advantageous consequence is tude more strongly than they do X-rays. Furthermore, as electron that it is possible to determine experimentally the charged states crystallography yields Coulomb potential maps rather than elec- of protein residues and metals. As proteins use metals of different tron density maps, it could provide a unique method to visualize ionic states for many purposes, notably for catalysis and electron the charged states of amino acid residues and metals. -
Prediction of Amino Acid Side Chain Conformation Using a Deep Neural
Prediction of amino acid side chain conformation using a deep neural network Ke Liu 1, Xiangyan Sun3, Jun Ma3, Zhenyu Zhou3, Qilin Dong4, Shengwen Peng3, Junqiu Wu3, Suocheng Tan3, Günter Blobel2, and Jie Fan1, Corresponding author details: Jie Fan, Accutar Biotechnology, 760 Parkside Ave, Room 213 Brooklyn, NY 11226, USA. [email protected] 1. Accutar Biotechnology, 760 parkside Ave, Room 213, Brooklyn, NY 11226, USA. 2. Laboratory of Cell Biology, Howard Hughes Medical Institute, The Rockefeller University, New York, New York 10065, USA 3. Accutar Biotechnology (Shanghai) Room 307, No. 6 Building, 898 Halei Rd, Shanghai, China 4. Fudan University 220 Handan Rd, Shanghai, China Summary: A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation. Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods. More strikingly, the prediction method presented here is robust enough to identify individual conformational outliers from high resolution structures in a protein data bank without providing its structural factors. We envisage that our amino acid side chain predictor could be used as a quality check step for future protein structure model validation and many other potential applications such as side chain assignment in Cryo-electron microscopy, crystallography model auto-building, protein folding and small molecule ligand docking. -
Recent Advances in Electron Crystallography
pISSN 2287-5123·eISSN 2287-4445 https://doi.org/10.9729/AM.2017.47.3.160 Review Article Recent Advances in Electron Crystallography Jeong Min Chung†, Sangmin Lee†, Hyun Suk Jung* Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chuncheon 24341, Korea Electron crystallography has been used as the one of powerful tool for studying the structure of biological macromolecules at high resolution which is sufficient to provide †These authors contributed equally details of intramolecular and intermolecular interactions at near-atomic level. Previously to this work. it commonly uses two-dimensional crystals that are periodic arrangement of biological molecules, however recent studies reported a novel technical approach to electron *Correspondence to: crystallography of three-dimensional crystals, called micro electron-diffraction (MicroED) Jung HS, which involves placing the irregular and small sized protein crystals in a transmission Tel: +82-33-250-8513 electron microscope to determine the atomic structure. In here, we review the advances in Fax: +82-33-259-9363 electron crystallography techniques with several recent studies. Furthermore, we discuss E-mail: [email protected] the future direction of this structural approach. Received August 7, 2017 Revised September 6, 2017 Key Words: Electron crystallography, Protein structure, Transmission electron microscopy, Accepted September 8, 2017 Micro-electron diffraction, Structural biology INTRODUCTION crystals found during the screening process (Bill et al., 2011). Since early 1940s, electron diffraction has been used to solve The ultimate goal of structural biology is to understand the crystallographic problems (Bendersky & Gayle, 2001). the protein function and its physiological mechanisms by The basic principle of electron crystallography is similar determining the three-dimensional (3D) structure. -
Electron Crystallography of Aquaporins
Portland State University PDXScholar Chemistry Faculty Publications and Presentations Chemistry 7-2008 Electron Crystallography of Aquaporins Simeon Andrews University of Washington Tacoma Steve Reichow [email protected] Tamir Gonen Howard Hughes Medical Institute Follow this and additional works at: https://pdxscholar.library.pdx.edu/chem_fac Part of the Biochemistry, Biophysics, and Structural Biology Commons, and the Chemistry Commons Let us know how access to this document benefits ou.y Citation Details Andrews, S., Reichow, S. L., & Gonen, T. (2008). Electron crystallography of aquaporins. IUBMB life, 60(7), 430-436. This Post-Print is brought to you for free and open access. It has been accepted for inclusion in Chemistry Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. NIH Public Access Author Manuscript IUBMB Life. Author manuscript; available in PMC 2009 June 4. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: IUBMB Life. 2008 July ; 60(7): 430±436. doi:10.1002/iub.53. Electron Crystallography of Aquaporins Simeon Andrews, Steve L. Reichow, and Tamir Gonen Department of Biochemistry, University of Washington, Seattle, WA, USA Summary Aquaporins are a family of ubiquitous membrane proteins that form a pore for the permeation of water. Both electron and X-ray crystallography played major roles in determining the atomic structures of a number of aquaporins. This review focuses on electron crystallography, and its contribution to the field of aquaporin biology. We briefly discuss electron crystallography and the two-dimensional crystallization process. -
Xenorhodopsins, an Enigmatic New Class of Microbial Rhodopsins Horizontally Transferred Between Archaea and Bacteria
UC San Diego UC San Diego Previously Published Works Title Xenorhodopsins, an enigmatic new class of microbial rhodopsins horizontally transferred between Archaea and Bacteria Permalink https://escholarship.org/uc/item/8rg5f54b Journal Biology Direct, 6(1) ISSN 1745-6150 Authors Ugalde, Juan A Podell, Sheila Narasingarao, Priya et al. Publication Date 2011-10-10 DOI http://dx.doi.org/10.1186/1745-6150-6-52 Supplemental Material https://escholarship.org/uc/item/8rg5f54b#supplemental Peer reviewed eScholarship.org Powered by the California Digital Library University of California Ugalde et al. Biology Direct 2011, 6:52 http://www.biology-direct.com/content/6/1/52 DISCOVERYNOTES Open Access Xenorhodopsins, an enigmatic new class of microbial rhodopsins horizontally transferred between archaea and bacteria Juan A Ugalde1, Sheila Podell1, Priya Narasingarao1 and Eric E Allen1,2* Abstract Based on unique, coherent properties of phylogenetic analysis, key amino acid substitutions and structural modeling, we have identified a new class of unusual microbial rhodopsins related to the Anabaena sensory rhodopsin (ASR) protein, including multiple homologs not previously recognized. We propose the name xenorhodopsin for this class, reflecting a taxonomically diverse membership spanning five different Bacterial phyla as well as the Euryarchaeotal class Nanohaloarchaea. The patchy phylogenetic distribution of xenorhodopsin homologs is consistent with historical dissemination through horizontal gene transfer. Shared characteristics of xenorhodopsin-containing microbes include the absence of flagellar motility and isolation from high light habitats. Reviewers: This article was reviewed by Dr. Michael Galperin and Dr. Rob Knight. Findings disseminated photoreceptor and photosensory activities Microbial rhodopsins are a widespread family of photo- across large evolutionary distances [1]. -
Electron Crystallography: Imaging and Single-Crystal Diffraction from Powders
feature articles Acta Crystallographica Section A Foundations of Electron crystallography: imaging and single-crystal Crystallography diffraction from powders ISSN 0108-7673 Xiaodong Zoua,b* and Sven Hovmo¨llera Received 28 September 2007 Accepted 16 November 2007 aStructural Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden, and bBerzelii Centre EXSELENT on Porous Materials, Stockholm University, SE-106 91 Stockholm, Sweden. Correspondence e-mail: [email protected] The study of crystals at atomic level by electrons – electron crystallography – is an important complement to X-ray crystallography. There are two main advantages of structure determinations by electron crystallography compared to X-ray diffraction: (i) crystals millions of times smaller than those needed for X-ray diffraction can be studied and (ii) the phases of the crystallographic structure factors, which are lost in X-ray diffraction, are present in transmission- electron-microscopy (TEM) images. In this paper, some recent developments of electron crystallography and its applications, mainly on inorganic crystals, are shown. Crystal structures can be solved to atomic resolution in two dimensions as well as in three dimensions from both TEM images and electron diffraction. Different techniques developed for electron crystallography, including three- dimensional reconstruction, the electron precession technique and ultrafast electron crystallography, are reviewed. Examples of electron-crystallography # 2008 International Union of Crystallography applications are given. There is in principle no limitation to the complexity of Printed in Singapore – all rights reserved the structures that can be solved by electron crystallography. 1. Introduction Henderson, 1975) using Fourier-transform-based image processing to get both the crystallographic structure-factor Electron diffraction (ED) of crystals was discovered in 1927, amplitudes and phases from the TEM images and retrieve the only 15 years after the discovery of X-ray diffraction. -
NIH Public Access Author Manuscript Proteins
NIH Public Access Author Manuscript Proteins. Author manuscript; available in PMC 2009 May 11. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Proteins. 2008 June ; 71(4): 1637±1646. doi:10.1002/prot.21845. Using Quantum Mechanics to Improve Estimates of Amino Acid Side Chain Rotamer Energies P. Douglas Renfrew, Glenn Butterfoss, and Brian Kuhlman Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, Email: [email protected] Abstract Amino acid side chains adopt a discrete set of favorable conformations typically referred to as rotamers. The relative energies of rotamers partially determine which side chain conformations are more often observed in protein structures and accurate estimates of these energies are important for predicting protein structure and designing new proteins. Protein modelers typically calculate side chain rotamer energies by using molecular mechanics (MM) potentials or by converting rotamer probabilities from the protein database (PDB) into relative free energies. One limitation of the knowledge-based energies is that rotamer preferences observed in the PDB can reflect internal side chain energies as well as longer-range interactions with the rest of the protein. Here, we test an alternative approach for calculating rotamer energies. We use three different quantum mechanics (QM) methods (second order Moller-Plesset (MP2), density functional theory (DFT) energy calculation using the B3LYP functional, and Hartree-Fock) to calculate the energy of amino acid rotamers in a dipeptide model system, and then use these pre-calculated values in side chain placement simulations. Energies were calculated for over 35,000 different conformations of leucine, isoleucine and valine dipeptides with backbone torsion angles from the helical and strand regions of the Ramachandran plot. -
Pulsed EPR Determination of Water Accessibility to Spin-Labeled Amino Acid Residues in Lhciib
1124 Biophysical Journal Volume 96 February 2009 1124–1141 Pulsed EPR Determination of Water Accessibility to Spin-Labeled Amino Acid Residues in LHCIIb A. Volkov,† C. Dockter,‡ T. Bund,‡ H. Paulsen,‡ and G. Jeschke§* †Max-Planck Institute for Polymer Research, Mainz, Germany; ‡Institute of General Botany, Johannes Gutenberg University, Mainz, Germany; and §Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, Zu¨rich, Switzerland ABSTRACT Membrane proteins reside in a structured environment in which some of their residues are accessible to water, some are in contact with alkyl chains of lipid molecules, and some are buried in the protein. Water accessibility of residues may change during folding or function-related structural dynamics. Several techniques based on the combination of pulsed elec- tron paramagnetic resonance (EPR) with site-directed spin labeling can be used to quantify such water accessibility. Accessibility parameters for different residues in major plant light-harvesting complex IIb are determined by electron spin echo envelope modulation spectroscopy in the presence of deuterated water, deuterium contrast in transversal relaxation rates, analysis of longitudinal relaxation rates, and line shape analysis of electron-spin-echo-detected EPR spectra as well as by the conventional techniques of measuring the maximum hyperfine splitting and progressive saturation in continuous-wave EPR. Systematic comparison of these parameters allows for a more detailed characterization of the environment of the spin-labeled residues. These techniques are applicable independently of protein size and require ~10–20 nmol of singly spin-labeled protein per sample. For a residue close to the N-terminus, in a domain unresolved in the existing x-ray structures of light-harvesting complex IIb, all methods indicate high water accessibility.