A Fresh Look at the Ramachandran Plot and the Occurrence of Standard Structures in Proteins
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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. -
Structural Insight Into Marburg Virus Nucleoprotein-RNA Complex Formation
bioRxiv preprint doi: https://doi.org/10.1101/2021.07.13.452116; this version posted July 13, 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. 1 Structural insight into Marburg virus nucleoprotein-RNA complex formation 2 3 Yoko Fujita-Fujiharu1,2,3, Yukihiko Sugita1,2,4, Yuki Takamatsu1,#, Kazuya Houri1,2,3, Manabu 4 Igarashi5, Yukiko Muramoto1,2,3, Masahiro Nakano1,2,3, Yugo Tsunoda1, Stephan Becker6,7, 5 Takeshi Noda1,2,3* 6 7 1 Laboratory of Ultrastructural Virology, Institute for Frontier Life and Medical Sciences, 8 Kyoto University, 53 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan 9 2 Laboratory of Ultrastructural Virology, Graduate School of Biostudies, Kyoto University, 53 10 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan 11 3 CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332- 12 0012, Japan 13 4 Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan. 14 5 Division of Epidemiology, International Institute for Zoonosis Control, Hokkaido University, 15 Sapporo 001-0020, Japan 16 6 Institute of Virology, University of Marburg, 35043 Marburg, Germany. 17 7 German Center for Infection Research (DZIF), Marburg-Gießen-Langen Site, University of 18 Marburg, 35043 Marburg, Germany. 19 20 # present address: Department of Virology I, National Institute of Infectious Diseases, Gakuen 21 4-7-1, Musashimurayama-city, Tokyo 208-0011, Japan 22 *correspondence to: [email protected] 23 24 bioRxiv preprint doi: https://doi.org/10.1101/2021.07.13.452116; this version posted July 13, 2021. -
Α/Β Coiled Coils 2 3 Marcus D
1 α/β Coiled Coils 2 3 Marcus D. Hartmann, Claudia T. Mendler†, Jens Bassler, Ioanna Karamichali, Oswin 4 Ridderbusch‡, Andrei N. Lupas* and Birte Hernandez Alvarez* 5 6 Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 7 Tübingen, Germany 8 † present address: Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, 9 Technische Universität München, Munich, Germany 10 ‡ present address: Vossius & Partner, Siebertstraße 3, 81675 Munich, Germany 11 12 13 14 * correspondence to A. N. Lupas or B. Hernandez Alvarez: 15 Department of Protein Evolution 16 Max-Planck-Institute for Developmental Biology 17 Spemannstr. 35 18 D-72076 Tübingen 19 Germany 20 Tel. –49 7071 601 356 21 Fax –49 7071 601 349 22 [email protected], [email protected] 23 1 24 Abstract 25 Coiled coils are the best-understood protein fold, as their backbone structure can uniquely be 26 described by parametric equations. This level of understanding has allowed their manipulation 27 in unprecedented detail. They do not seem a likely source of surprises, yet we describe here 28 the unexpected formation of a new type of fiber by the simple insertion of two or six residues 29 into the underlying heptad repeat of a parallel, trimeric coiled coil. These insertions strain the 30 supercoil to the breaking point, causing the local formation of short β-strands, which move the 31 path of the chain by 120° around the trimer axis. The result is an α/β coiled coil, which retains 32 only one backbone hydrogen bond per repeat unit from the parent coiled coil. -
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. -
Stapled Peptides—A Useful Improvement for Peptide-Based Drugs
molecules Review Stapled Peptides—A Useful Improvement for Peptide-Based Drugs Mattia Moiola, Misal G. Memeo and Paolo Quadrelli * Department of Chemistry, University of Pavia, Viale Taramelli 12, 27100 Pavia, Italy; [email protected] (M.M.); [email protected] (M.G.M.) * Correspondence: [email protected]; Tel.: +39-0382-987315 Received: 30 July 2019; Accepted: 1 October 2019; Published: 10 October 2019 Abstract: Peptide-based drugs, despite being relegated as niche pharmaceuticals for years, are now capturing more and more attention from the scientific community. The main problem for these kinds of pharmacological compounds was the low degree of cellular uptake, which relegates the application of peptide-drugs to extracellular targets. In recent years, many new techniques have been developed in order to bypass the intrinsic problem of this kind of pharmaceuticals. One of these features is the use of stapled peptides. Stapled peptides consist of peptide chains that bring an external brace that force the peptide structure into an a-helical one. The cross-link is obtained by the linkage of the side chains of opportune-modified amino acids posed at the right distance inside the peptide chain. In this account, we report the main stapling methodologies currently employed or under development and the synthetic pathways involved in the amino acid modifications. Moreover, we report the results of two comparative studies upon different kinds of stapled-peptides, evaluating the properties given from each typology of staple to the target peptide and discussing the best choices for the use of this feature in peptide-drug synthesis. Keywords: stapled peptide; structurally constrained peptide; cellular uptake; helicity; peptide drugs 1. -
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. -
Theoretical Protein Structure Prediction of Glucagon-Like Peptide 2 Receptor Using Homology Modelling
J. Chosun Natural Sci. Vol. 10, No. 3 (2017) pp. 119 − 124 https://doi.org/10.13160/ricns.2017.10.3.119 Theoretical Protein Structure Prediction of Glucagon-like Peptide 2 Receptor Using Homology Modelling Santhosh Kumar Nagarajan1,2 and Thirumurthy Madhavan2† Abstract Glucagon-like peptide 2 receptor, a GPCR, binds with the glucagon-like peptide, GLP-2 and regulates various metabolic functions in the gastrointestinal tract. It plays an important role in the nutrient homeostasis related to nutrient assimilation by regulating mucosal epithelium. GLP-2 receptor affects the cellular response to external injury, by controlling the intestinal crypt cell proliferation. As they are therapeutically attractive towards diseases related with the gastrointestinal tract, it becomes essential to analyse their structural features to study the pathophysiology of the diseases. As the three dimensional structure of the protein is not available, in this study, we have performed the homology modelling of the receptor based on single- and multiple template modeling. The models were subjected to model validation and a reliable model based on the validation statistics was identified. The predicted model could be useful in studying the structural features of GLP-2 receptor and their role in various diseases related to them. Keywords: GLP2 Receptor, GPCR, GLP2, Homology Modelling. 1. Introduction nutrient homeostasis proximal to nutrient assimilation by controlling the stasis of mucosal epithelium. The sig- Glucagon and glucagon like peptides (GLPs) are nalling via GLP-2 receptor regulates the intestinal crypt secreted in the pancreas, gut, CNS and PNS and control cell proliferation, directly affecting the cellular response various metabolic functions[1]. -
The Generic Geometry of Helices and Their Close-Packed Structures
Theor Chem Acc (2010) 125:207–215 DOI 10.1007/s00214-009-0639-4 REGULAR ARTICLE The generic geometry of helices and their close-packed structures Kasper Olsen Æ Jakob Bohr Received: 23 May 2009 / Accepted: 9 September 2009 / Published online: 25 September 2009 Ó The Author(s) 2009. This article is published with open access at Springerlink.com Abstract The formation of helices is an ubiquitous 1 Introduction phenomenon for molecular structures whether they are biological, organic, or inorganic, in nature. Helical struc- Helical structures are common in chain molecules such as tures have geometrical constraints analogous to close- proteins, RNA, and DNA, e.g., a-helices and the A, B and Z packing of three-dimensional crystal structures. For helical forms of DNA. In this paper, we consider the packing of packing the geometrical constraints involve parameters idealized helices formed by a continuous tube with the such as the radius of the helical cylinder, the helical pitch purpose to calculate the constraints on such helices which angle, and the helical tube radius. In this communication, arise from close-packing and space filling considerations; the geometrical constraints for single helix, double helix, we consider single helical tubes, as well as sets of two and for double helices with minor and major grooves are identical helical tubes. It is found that the efficiency of the calculated. The results are compared with values from the use of space depends on the helical pitch angle, and the literature for helical polypeptide backbone structures, the optimum helical pitch angle is determined for single and a-, p-, 310-, and c-helices. -
Breaking Non-Native Hydrophobic Clusters Is the Rate-Limiting Step In
Shibasish Chowdhury Wei Zhang Breaking Non-Native Chun Wu Guoming Xiong Hydrophobic Clusters is the Yong Duan Rate-Limiting Step in the Department of Chemistry and Biochemistry, Folding of an Alanine-Based Center of Biomedical Research Excellence, Peptide University of Delaware, Newark, DE 19716 Received 13 March 2002; accepted 29 April 2002 Abstract: The formation mechanism of an alanine-based peptide has been studied by all-atom molecular dynamics simulations with a recently developed all-atom point-charge force field and the Generalize Born continuum solvent model at an effective salt concentration of 0.2M. Thirty-two simulations were conducted. Each simulation was performed for 100 ns. A surprisingly complex folding process was observed. The development of the helical content can be divided into three phases with time constants of 0.06–0.08, 1.4–2.3, and 12–13 ns, respectively. Helices initiate extreme rapidly in the first phase similar to that estimated from explicit solvent simulations. Hydrophobic collapse also takes place in this phase. A folding intermediate state develops in the second phase and is unfolded to allow the peptide to reach the transition state in the third phase. The folding intermediate states are characterized by the two-turn short helices and the transition states are helix–turn–helix motifs—both of which are stabilized by hydrophobic clusters. The equilibrium helical content, calculated by both the main-chain ⌽–⌿ torsion angles and the main-chain hydrogen bonds, is 64–66%, which is in remarkable agreement with experiments. After corrected for the solvent viscosity effect, an extrapolated folding time of 16–20 ns is obtained that is in qualitative agreement with experiments.