DOCSLIB.ORG

Calculation of Proteins' Total Side-Chain Torsional Entropy And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Calculation of Proteins' Total Side-Chain Torsional Entropy And doi:10.1016/j.jmb.2009.05.068 J. Mol. Biol. (2009) 391, 484–497 Available online at www.sciencedirect.com Calculation of Proteins’ Total Side-Chain Torsional Entropy and Its Influence on Protein–Ligand Interactions Kateri H. DuBay and Phillip L. Geissler⁎ Department of Chemistry, Despite the high density within a typical protein fold, the ensemble of University of California at sterically permissible side-chain repackings is vast. Here, we examine the Berkeley, Berkeley, CA 94720, extent of this variability that survives energetic biases due to van der Waals USA interactions, hydrogen bonding, salt bridges, and solvation. Monte Carlo simulations of an atomistic model exhibit thermal fluctuations among a Chemical Sciences Division, diverse set of side-chain arrangements, even with the peptide backbone Lawrence Berkeley National Lab, fixed in its crystallographic conformation. We have quantified the torsional Berkeley, CA 94720, USA entropy of this native-state ensemble, relative to that of a noninteracting Physical Biosciences Division, reference system, for 12 small proteins. The reduction in entropy per Lawrence Berkeley National Lab, rotatable bond due to each kind of interaction is remarkably consistent Berkeley, CA 94720, USA across this set of molecules. To assess the biophysical importance of these fluctuations, we have estimated side-chain entropy contributions Received 12 January 2009; to the binding affinity of several peptide ligands with calmodulin. received in revised form Calculations for our fixed-backbone model correlate very well with 20 May 2009; experimentally determined binding entropies over a range spanning more accepted 22 May 2009 than 80 kJ/(mol·308 K). Available online © 2009 Elsevier Ltd. All rights reserved. 28 May 2009 Keywords: side-chain entropy; configurational entropy; side-chain fluctuations; Edited by M. Levitt protein–ligand binding; protein thermodynamics Introduction niques in particular resolve fluctuations at the level of single bond vectors in both the backbones and – Native protein conformations are extremely dense, side-chains of folded proteins.3 5 The Lipari–Szabo with packing fractions comparable to those of order parameters6 they determine, which increase 1 2 2 organic crystals. This observation motivated in from Saxis =0 to Saxis =1 as rotational motion early studies of protein structure and dynamics a becomes restricted, report on the range of picose- jigsaw-puzzle notion, in which amino acid side cond to nanosecond dynamics for backbone amide chains of a folded structure become fixed in a unique and side-chain methyl groups. Computational stu- spatial arrangement by steric interactions with their dies suggest that order parameters lower than 0.8 neighbors. Still today, many computational proce- point to transitions between multiple rotameric dures that explore side-chain packing strive to states in addition to the inevitable vibrations about identify a single native configuration.2 optimal torsional angles.7,8 The three-dimensional structure of a protein, how- Side-chain methyl group order parameters often lie b 2 b 3 ever, can fluctuate considerably. Large-scale mo- in the range 0.2 Saxis 0.8, indicating extensive tions involve partial or full unfolding and backbone exploration of different rotameric states. These results hinge motions, but subtle structural variation on are corroborated by dipolar coupling measurements, smaller scales has been highlighted by several suggesting that side chains substantially populate experimental measurements. NMR relaxation tech- different rotameric states within the ensemble of folded configurations.9,10 Evidence for alternative side-chain conformations has even been found in *Corresponding author. E-mail address: electron density maps from crystallography expe- [email protected]. riments.11 The data accumulating from such studies Abbreviations used: MC, Monte Carlo; MD, molecular paint a consistent picture: Residual side-chain fluc- dynamics; LJ, Lennard–Jones; SB, salt bridge; HB, tuations in the native-state ensemble are distributed hydrogen bond; IS, implicit solvent; SASA, solvent- heterogeneously throughout the protein; side-chain accessible surface area; CaM, calmodulin; PYP, bond vectors fluctuate more significantly than do photoactive yellow protein; WL, Wang–Landau. those along the backbone; and the entropy associated 0022-2836/$ - see front matter © 2009 Elsevier Ltd. All rights reserved. Calculation of Side-Chain Torsional Entropy 485 with such fluctuations is likely to be a significant in several systems, entropy changes figure promi- player in protein thermodynamics.4,12 nently in tuning protein binding affinities.22,23 In the Computational studies focusing on geometric case of stromelysin 1 binding to the N-terminal do- aspects of side-chain packing have reconciled the main of tissue inhibitor of metalloproteinases 1, they evidence for significant torsional fluctuations with even overcome a substantially unfavorable enthalpy constraints due to steric interactions in a dense of binding.24 Implicating the involvement of side environment.13 Much as in a dense liquid, volume chains in these phenomena, entropies inferred from exclusion reduces the diversity of accessible config- NMR order parameters correlate strongly with calo- urations greatly, but by no means completely. rimetrically determined binding entropies for cal- Nearly 1020 distinct side-chain conformations were modulin (CaM) and several peptide ligands.12 We determined to satisfy hard-core constraints in a 125- find even better agreement between binding en- residue protein with native backbone structure.13 To tropy measurements and calculated values based on what degree non-steric interactions further reduce the methods we have developed. This comparison is this variability is not at all clear a priori. Populating discussed in detail in Results and Discussion. even a very small fraction of the geometrically acceptable arrangements would be sufficient to Model allow for significant contributions to free energies of folding and ligand binding. In developing a theoretical approach, we are Mean field theories,14 various interpretations of guided by the notion that side-chain rearrangements – molecular dynamics (MD) simulations,15 18 and within a protein's native state are not strongly me- – several Monte Carlo (MC) approaches13,19 21 have diated by motions of the peptide backbone. Physi- all been used to estimate the residual entropy of side- cally, we expect that once the molecule has folded, it is chain rotations in folded proteins. Each of subject to global constraints of high packing fraction these approaches, however, is limited by underlying that vary little with small-amplitude backbone fluc- approximations or formidable practical challenges. tuations. Empirically, we note that correlations Mean field approaches, by definition, do not account observed between backbone NMR order parameters, 2 2 for a complete range of thermal fluctuations; straight- S , and their associated side-chain parameters, Saxis, forward MD simulations can explore only rearrange- are weak.25 Following Kussell et al.,13 we thus adopt a ments that occur on computationally accessible time model in which the peptide backbone is fixed in its scales. MC methods are similarly hindered by crystallographically determined conformation. As a sampling difficulties intrinsic to such tightly packed result, applications of our methods are limited to systems. As a compromise, entropies are sometimes proteins whose native structures have been deter- calculated separately for single residues or small mined with high resolution. groups of neighboring residues while keeping other The sole degrees of freedom in our calculations are residues fixed.19 Studies that do confront the full dihedral angles χ for rotatable side-chain bonds with combinatorial problem, allowing all side chains to heavy-atom (i.e., non-hydrogen) substituents. Other rotate simultaneously, have neglected potentially variables are known to influence side-chain entropy,15 important contributions from intra-rotameric mo- but torsional entropy alone is thought to provide a tions20 or have considered geometric effects indepen- good approximation.19 Natural amino acids possess dent of non-steric interactions.13,21 no more than a handful of such dihedral degrees of In this article, we present a new approach for freedom. Alanine, for example, has none, while lysine estimating side-chain torsional entropy. Building on and arginine possess the largest number (four). As in a algorithms developed by Kussell et al.,13 our calcula- simple molecule such as propane, local bonding tions are enabled by enhanced MC methods and a energetics bias such angles to lie in one of typically schematic treatment of forces due to sterics, van der three ranges. For classification purposes, we consider Waals interactions, hydrogen bonding, salt bridges these ranges as discrete rotameric states, each with an (SBs), and solvation. Through this combination, we ideal angular value θ. We do, however, permit achieve thorough sampling of thermal fluctuations, deviations from these ideal angles, ϕ=χ−θ.Weand incorporate fully coupled rotations of all residues, others have found them to be essential for accom- and address a comprehensive set of physical interac- modating tightly packed rearrangements.13,26 The tions. Model outlines our approach and the physical intrinsic energetic penalty Edihedrals limiting such perspectives underlying it. Results and Discussion fluctuations
Load more

Recommended publications
  • Side-Chain Liquid Crystal Polymers (SCLCP): Methods and Materials. an Overview
    Materials 2009, 2, 95-128; doi:10.3390/ma2010095 OPEN ACCESS materials ISSN 1996-1944 www.mdpi.com/journal/materials Review Side-chain Liquid Crystal Polymers (SCLCP): Methods and Materials. An Overview Tomasz Ganicz * and Włodzimierz Stańczyk Centre of Molecular and Macromolecular Studies, Polish Academy of Science, Sienkiewicza 112, 90- 363 Łódź, Poland; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel. +48-42-684-71-13; Fax: +48-42-684-71-26 Received: 5 February 2009; in revised form: 3 March 2009 / Accepted: 9 March 2009 / Published: 11 March 2009 Abstract: This review focuses on recent developments in the chemistry of side chain liquid crystal polymers. It concentrates on current trends in synthetic methods and novel, well defined structures, supramolecular arrangements, properties, and applications. The review covers literature published in this century, apart from some areas, such as dendritic and elastomeric systems, which have been recently reviewed. Keywords: Liquid crystals, side chain polymers, polymer modification, polymer synthesis, self-assembly. 1. Introduction Over thirty years ago the work of Finkelmann and Ringsdorf [1,2] gave important momentum to synthesis of side chain liquid crystal polymers (SCLCPs), materials which combine the anisotropy of liquid crystalline mesogens with the mechanical properties of polymers. Although there were some earlier attempts [2], their approach of decoupling the motions of a polymer main chain from a mesogen, thus allowed side chain moieties to build up long range ordering (Figure 1). They were able to synthesize polymers with nematic, smectic and cholesteric phases via free radical polymerization of methacryloyl type monomers [3].
    [Show full text]
  • The Role of Side-Chain Branch Position on Thermal Property of Poly-3-Alkylthiophenes
    Polymer Chemistry The Role of Side-chain Branch Position on Thermal Property of Poly-3-alkylthiophenes Journal: Polymer Chemistry Manuscript ID PY-ART-07-2019-001026.R2 Article Type: Paper Date Submitted by the 02-Oct-2019 Author: Complete List of Authors: Gu, Xiaodan; University of Southern Mississippi, School of Polymer Science and Engineering Cao, Zhiqiang; University of Southern Mississippi, School of Polymer Science and Engineering Galuska, Luke; University of Southern Mississippi, School of Polymer Science and Engineering Qian, Zhiyuan; University of Southern Mississippi, School of Polymer Science and Engineering Zhang, Song; University of Southern Mississippi, School of Polymer Science and Engineering Huang, Lifeng; University of Southern Mississippi, School of Polymers and High Performance Materials Prine, Nathaniel; University of Southern Mississippi, School of Polymer Science and Engineering Li, Tianyu; Oak Ridge National Laboratory; University of Tennessee Knoxville He, Youjun; Oak Ridge National Laboratory Hong, Kunlun; Oak Ridge National Laboratory, Center for Nanophase Materials Science; Oak Ridge National Laboratory, Center for Nanophase Materials Science Page 1 of 13 PleasePolymer do not Chemistryadjust margins ARTICLE The Role of Side-chain Branch Position on Thermal Property of Poly-3-alkylthiophenes Received 00th January 20xx, a a a a a a Accepted 00th January 20xx Zhiqiang Cao, Luke Galuska, Zhiyuan Qian, Song Zhang, Lifeng Huang, Nathaniel Prine, Tianyu Li,b, c Youjun He,b Kunlun Hong,*b, c and Xiaodan Gu*a DOI: 10.1039/x0xx00000x Thermomechanical properties of conjugated polymers (CPs) are greatly influenced by both their microstructures and backbone structures. In the present work, to investigate the effect of side-chain branch position on the backbone’s mobility and molecular packing structure, four poly (3-alkylthiophene-2, 5-diyl) derivatives (P3ATs) with different side chains, either branched and linear, were synthesized by a quasi-living Kumada catalyst transfer polymerization (KCTP) method.
    [Show full text]
  • Improved Prediction of Protein Side-Chain Conformations with SCWRL4 Georgii G
    proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 Georgii G. Krivov,1,2 Maxim V. Shapovalov,1 and Roland L. Dunbrack Jr.1* 1 Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, Pennsylvania 19111 2 Department of Applied Mathematics, Moscow Engineering Physics Institute (MEPhI), Kashirskoe Shosse 31, Moscow 115409, Russian Federation INTRODUCTION ABSTRACT The side-chain conformation prediction problem is an integral Determination of side-chain conformations is an component of protein structure determination, protein structure important step in protein structure prediction and protein design. Many such methods have prediction, and protein design. In single-site mutants and in closely been presented, although only a small number related proteins, the backbone often changes little and structure pre- 1 are in widespread use. SCWRL is one such diction can be accomplished by accurate side-chain prediction. In method, and the SCWRL3 program (2003) has docking of ligands and other proteins, taking into account changes remained popular because of its speed, accuracy, in side-chain conformation is often critical to accurate structure pre- and ease-of-use for the purpose of homology dictions of complexes.2–4 Even in methods that take account of modeling. However, higher accuracy at compara- changes in backbone conformation, one step in the process is recal- ble speed is desirable. This has been achieved in culation of side-chain conformation or ‘‘repacking.’’5 Because many a new program SCWRL4 through: (1) a new backbone conformations may be sampled in model refinements, backbone-dependent rotamer library based on side-chain prediction must also be very fast.
    [Show full text]
  • Molecular Modelling Virtual Chemistry Resource Pymol Experiment Answer Sheet Well Done for Completing Our Pymol Experiment! We Really Hope You Enjoyed It!
    #Outtheboxthinking Faculty of Natural & Mathematical Sciences Department of Chemistry Molecular Modelling Virtual Chemistry Resource PyMol Experiment Answer Sheet Well done for completing our PyMol experiment! We really hope you enjoyed it! Please make sure you have completed our post experiment questionnaire. https://kings.onlinesurveys.ac.uk/post-questionnaire-for-outtheboxthinking-resources Send us photos, comments, or thoughts to either our twitter or Instagram accounts using the hashtag #outtheboxthinking. We also encourage you to make a poster of your data and email it to us. Exploring how proteins are formed Press “Amino Acid” Q. What is the R group in the side chain of this amino acid? CH3- Methyl Group Q. Which amino acid is shown here? Alanine Press the button “ALA_ASP” and an additional amino acid will appear Q. What is the chemical formula of the side chain of the new amino acid? CH2COOH Q. What is the name of the second, new amino acid? Aspartic Acid or Aspartate Press the button “Bonded_Ala_Asp” Q. What colour(s) is the amide bond connecting the two amino acids? Blue and green. It is important to highlight the bond between the nitrogen of one amino acid and the carbonyl of the second amino acid and not any other bond (Figure 1). Figure 1: Arrow points to the amide bond formed between two amino acids. Page 1 of 8 Instagram: kclchemoutreach #outtheboxthinking Q. Which elements have been lost from each amino acid following their bonding? Amino acid 1:: Alanine has lost an oxygen and a hydrogen Amino acid 2: Aspartic Acid has lost a hydrogen If you are unsure why this is correct look at figure 2 and open the PyMol document again to visualise the bonding Figure 2: Reaction scheme for amino acid bonding, resulting in the formation of an amide bond and the loss of water.
    [Show full text]
  • Amino Acid Recognition by Aminoacyl-Trna Synthetases
    www.nature.com/scientificreports OPEN The structural basis of the genetic code: amino acid recognition by aminoacyl‑tRNA synthetases Florian Kaiser1,2,4*, Sarah Krautwurst3,4, Sebastian Salentin1, V. Joachim Haupt1,2, Christoph Leberecht3, Sebastian Bittrich3, Dirk Labudde3 & Michael Schroeder1 Storage and directed transfer of information is the key requirement for the development of life. Yet any information stored on our genes is useless without its correct interpretation. The genetic code defnes the rule set to decode this information. Aminoacyl-tRNA synthetases are at the heart of this process. We extensively characterize how these enzymes distinguish all natural amino acids based on the computational analysis of crystallographic structure data. The results of this meta-analysis show that the correct read-out of genetic information is a delicate interplay between the composition of the binding site, non-covalent interactions, error correction mechanisms, and steric efects. One of the most profound open questions in biology is how the genetic code was established. While proteins are encoded by nucleic acid blueprints, decoding this information in turn requires proteins. Te emergence of this self-referencing system poses a chicken-or-egg dilemma and its origin is still heavily debated 1,2. Aminoacyl-tRNA synthetases (aaRSs) implement the correct assignment of amino acids to their codons and are thus inherently connected to the emergence of genetic coding. Tese enzymes link tRNA molecules with their amino acid cargo and are consequently vital for protein biosynthesis. Beside the correct recognition of tRNA features3, highly specifc non-covalent interactions in the binding sites of aaRSs are required to correctly detect the designated amino acid4–7 and to prevent errors in biosynthesis5,8.
    [Show full text]
  • 2. Proteins Have Hierarchies of Structure
    27 2. Proteins have Hierarchies of Structure Protein structure is usually described at four different levels (Fig. II.2.1). The first level, called the primary structure, describes the linear sequence of the amino acids in the chain. The different primary structures correspond to the different sequences in which the amino acids are covalently linked together. The secondary structure describes two common patterns of structural repetition in proteins: the coiling up into helices of segments of the chain, and the pairing together of strands of the chain into β-sheets. The tertiary structure is the next higher level of organization, the overall arrangement of secondary structural elements. The quaternary structure describes how different polypeptide chains are assembled into complexes. Figure II.2.1. Different levels of protein structure. A protein chain’s primary structure is its amino acid sequence. Secondary structure consists of the regular organization of helices and sheets. The example shown is a schematic representation of an α helix. Tertiary structure is a polypeptide chain’s three- dimensional native conformation, which often involves compact packing of secondary structure elements. The example given in the figure is a schematic drawing of one of the four polypeptide chains (subunits) of hemoglobin, the protein that transports oxygen in the blood. α-helices along the chain are represented as cylinders. N is the amino terminus and C is the carboxyl terminus of the polypeptide chain. Quaternary structure is the arrangement of multiple polypeptide chains (subunits) to form a functional biomolecular structure. The figure shows the quaternary arrangement of four subunits to form the functional hemoglobin molecule.
    [Show full text]
  • 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.
    [Show full text]
  • 6 Synthesis of N-Alkyl Amino Acids Luigi Aurelio and Andrew B
    j245 6 Synthesis of N-Alkyl Amino Acids Luigi Aurelio and Andrew B. Hughes 6.1 Introduction Among the numerous reactions of nonribosomal peptide synthesis, N-methylation of amino acids is one of the common motifs. Consequently, the chemical research community interested in peptide synthesis and peptide modification has generated a sizeable body of literature focused on the synthesis of N-methyl amino acids (NMA). That literature is summarized herein. Alkyl groups substituted on to nitrogen larger than methyl are exceedingly rare among natural products. However, medicinal chemistry programs and peptide drug development projects are not limited to N-methylation. While being a much smaller body of research, there is a range of methods for the N-alkylation of amino acids and those reports are also covered in this chapter. The literature on N-alkyl, primarily N-methyl amino acids comes about due to the useful properties that the N-methyl group confers on peptides. N-Methylation increases lipophilicity, which has the effect of increasing solubility in nonaqueous solvents and improving membrane permeability. On balance this makes peptides more bioavailable and makes them better therapeutic candidates. One potential disadvantage is the methyl group removes the possibility of hydro- gen bonding and so binding events may be discouraged. It is notable though that the N-methyl group does not fundamentally alter the identity of the amino acid. Some medicinal chemists have taken advantage of this fact to deliberately discourage binding of certain peptides that can still participate in the general or partial chemistry of a peptide. A series of recent papers relating to Alzheimers disease by Doig et al.
    [Show full text]
  • Amino Acids, Peptides, Proteins: Introduction
    Chem 109 C Bioorganic Compounds Fall 2019 HFH1104 Armen Zakarian Office: Chemistry Bldn 2217 http://labs.chem.ucsb.edu/~zakariangroup/courses.html CLAS Instructor: Dhillon Bhavan [email protected] Amino acids, Peptides, Proteins: Introduction Chapter 21 O R OH NH2 α-amino acid O O + H N R R R OH 2 R N - H2O H peptide bond = amide bond Proteins: Function 21.1 3 Proteins: Structural Histone Protein Structure: DNA packaging 4 Proteins: Protective botulinum toxin (botox) structure most toxic substance known LD50 = 10 ng/kg Amino acids, Peptides, Proteins: Introduction O O R O R O H R R OH R N OH N N OH H NH2 H NH2 O NH2 O R α-amino acid dipeptide tripeptide ! oligopeptide: 3 - 10 amino acids ! polypeptide, or protein: many amino acids Proteins: Amino Acids, Configuration NH2 HO O phenylalanine O + - NH3 natural amino acids C O +H N H same as - have the L configuration (S) 3 O2C R R Amino acids: Classification • Hydrophobic: “water-fearing”, nonpolar side chains – Alkyl side chain • Hydrophilic: “water-loving” side chains – Polar, neutral side chains – Anionic – Cationic - Table 21.1 lists 20 most common natural occurring amino acids - The structures of amino acids will be provided on the tests nonpolar side chains polar neutral (uncharged) side chains Amino acids: Classification polar acidic (anionic) side chains Amino acids: Classification polar basic (cationic) side chains Amino acids: Classification PROBLEM 1 Explain why when the imidazole ring of histidine is protonated, the double-bonded nitrogen is the nitrogen atom that accepts the proton. same for guanidine group in arginine.
    [Show full text]
  • Effect of Side Chain Substituent Volume on Thermoelectric Properties of IDT-Based Conjugated Polymers
    molecules Article Effect of Side Chain Substituent Volume on Thermoelectric Properties of IDT-Based Conjugated Polymers De-Xun Xie 1,2, Tong-Chao Liu 3, Jing Xiao 1,2, Jing-Kun Fang 4,* , Cheng-Jun Pan 3 and Guang Shao 1,2,* 1 School of Chemistry, Sun Yat-sen University, Guangzhou 510275, China; [email protected] (D.-X.X.); [email protected] (J.X.) 2 Shenzhen Research Institute, Sun Yat-sen University, Shenzhen 518057, China 3 Shenzhen Key Laboratory of Polymer Science and Technology, College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China; [email protected] (T.-C.L.); [email protected] (C.-J.P.) 4 School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China * Correspondence: [email protected] (J.-K.F.); [email protected] (G.S.) Abstract: A p-type thermoelectric conjugated polymer based on indacenodithiophene and benzoth- iadiazole is designed and synthesized by replacing normal aliphatic side chains (P1) with conjugated aromatic benzene substituents (P2). The introduced bulky substituent on P2 is detrimental to form the intensified packing of polymers, therefore, it hinders the efficient transporting of the charge carriers, eventually resulting in a lower conductivity compared to that of the polymers bearing aliphatic side chains (P1). These results reveal that the modification of side chains on conjugated polymers is crucial to rationally designed thermoelectric polymers with high performance. Keywords: organic thermoelectric materials; conjugated polymers; power factor Citation: Xie, D.-X.; Liu, T.-C.; Xiao, J.; Fang, J.-K.; Pan, C.-J.; Shao, G. Effect of Side Chain Substituent Volume on 1.
    [Show full text]
  • Naming Organic Compounds: Alkanes
    Naming Organic Compounds: Alkanes Chemical nomenclature assigns compounds a unique name that allows them to be easily identified and structurally understood. The International Union of Pure and Applied Chemistry (IUPAC) is the organization that assigns names to chemical compounds, and these names generally have three distinct features: a root name indicating either the major carbon chain or the ring of atoms found in the compound; a suffix indicating functional groups that may be present in the compound; and names of substituent groups, other than hydrogen, that may also be present in the compound. This handout will cover how to correctly name alkanes using IUPAC methods. Important Terms When naming alkanes, it is helpful to know the following terms: • Alkanes are organic compounds that only contain single bonds between carbon elements. Alkanes are often referred to as saturated hydrocarbons. Alkane compounds end in –ane. • The longest continuous carbon chain in the compound is the parent chain. Parent chains utilize prefixes to show the amount of carbons in the chain. • A substituent is a side chain group that branches off from the parent chain of the compound. Substituents utilize prefixes to show how many carbons are in the chain • Alkyl groups are substituents that consist of just carbons and hydrogens ( 2 +1). Alkyl groups begin with a prefix, determined by the number of carbons, and end in -yl. Useful Prefixes Number of Carbons Prefix Assigned Number of Carbons Prefix Assigned 1 Meth- 6 Hex- 2 Eth- 7 Hept- 3 Prop- 8 Oct- 4 But- 9 Non- 5 Pent- 10 Dec- Provided by the Academic Center for Excellence 1 Naming Organic Compounds June 2016 Lastly, if the compound is in a ring, use the prefix cyclo-.
    [Show full text]
  • Prediction of Protein Structure by Emphasizing Local Side- Chain/Backbone Interactions in Ensembles of Turn Fragments
    PROTEINS: Structure, Function, and Genetics 53:486–490 (2003) Prediction of Protein Structure by Emphasizing Local Side- Chain/Backbone Interactions in Ensembles of Turn Fragments Qiaojun Fang and David Shortle* Department of Biological Chemistry, The Johns Hopkins University School of Medicine Baltimore, Maryland ABSTRACT The prediction strategy used in energy is known to be minimized over all chemical interac- the CASP5 experiment was premised on the assump- tions involving the protein chain. And usually the most tion that local side-chain/backbone interactions are significant term of the scoring function assesses the ener- the principal determinants of protein structure at getics of long range contacts between side-chains, espe- low resolution. Our implementation of this assump- cially those involving hydrophobic residues. tion made extensive use of a scoring function based As documented in the previous CASP experiments, this on the propensities of the 20 amino acids for 137 standard approach has enjoyed very limited success in the different sub-regions of the Ramachandran plot, prediction of new folds until recently. With the extensive allowing estimation of the quality of fit between a use of large amounts of bioinformatics-type information in sequence segment and a known conformation. New both secondary structure prediction and in fragment selec- folds were predicted in three steps: prediction of tion, real progress in structure prediction has been made .1 secondary structure, threading to isolate fragments The most common explanation for how sequence informa- of protein structures corresponding to one turn plus tion from known homologues can produce such major flanking helices/strands, and recombination of over- improvements in prediction methods is improved sam- lapping fragments.
    [Show full text]