Molecular Recognition

Total Page:16

File Type:pdf, Size:1020Kb

Molecular Recognition Molecular Recognition Chemical Understanding of Biological Complexity Dr. Henry Dube Supramolecular Chemistry Covalent Noncovalent Atoms Molecules Supramolecules Synthesis Molecular Interactions Molecular Assembly Molecular Recognition HNO3, NO2 H2SO4 2 From Making Substrates to Making Receptors Chemical Substrate Drug Design Natural Receptor Substrate Supramolecular Chemistry Synthetic Receptor 3 Making Substrates: Structure Based Drug Design Disease Structure Ligand Design determination Target X-Ray Crystal structure Biologically active molecules Designed Ligand Assay Synthesis Clinical Studies, Drugs Synthesized Ligand 4 From Making Substrates to Making Receptors Chemical Substrate Drug Design Natural Receptor Substrate Supramolecular Chemistry Synthetic Receptor 5 Early Supramolecular Chemistry 1967 Pedersen 1978 Lehn 1953 Freudenberg, Cramer, Plieninger Complexation of Cations Complexation of Neutral Molecules OH HO O O O OH HO OH O O HO Me CO2H O HO OH O O OH O O O K N O O N O O O O O HO OH OH Me O O O OH OH Me O OH OH OH O O Crown ether Cryptand HO α-Cyclodextrin Lehn, Angew. Chem. 1988, 100, 91. Cram, Angew. Chem. 1988, 100, 1041. Pedersen, Angew. Chem. 1988, 100, 1053. 6 Molecular Recognition: Host–Guest Chemistry Molecular Capsules Wyler, de Mendoza, Rebek, Jr., Angew. Chem. Int. Ed. 1993, 32, 1699. Kang, Rebek, Jr., Nature 1996, 382, 239. 7 Molecular Recognition: Biomimetic Chemistry Synthetic Model for Hemoglobin and Myoglobin N Distal His N H 2.97 Å 3.08 Å R N N Co N N O R N Proximal His N Dube, Kasumaj, Calle, Saito, Jeschke, Diederich, Angew. Chem. Int. Ed. 2008, 47, 2600. Dube, Kasumaj, Calle, Felber, Saito, Jeschke, Diederich, Chem. Eur. J. 2009, 15, 125. 8 Supramolecular Chemistry: Generating Complexity Stoddart and co-workers, Science 2004, 304, 1308 PDB code: 1EVE • Biology • Drug discovery - Drug design • Supramolecular Architectures • Smart Materials • Molecular Machines • ... J.-M. Lehn and co-workers, Mol. Cryst. Liq. Cryst. 2007, 468, 187 Fujita and co-workers, Angew. Chem. Int. Ed. 2011, 44, 10318 9 Introduction Synthetic Models for Cytochrome Analysisc of Binding – Binding Energy cytochrome c was resolved in 2002 by Lange et al. and gives profound insight into the stereochemical organization of the two proteins during electron transfer [190]. A distance of 9.4 Å is observed between the edge-to-edge oriented heme groups of the cytochrome c1 center and cytochrome c. The close spacial arrangement of the two heme groups suggests a direct and rapid electron transfer, the rate of which was calculated to be approximately 8.3 x 106 s-1. -ΔG = Binding Energy N N O H O O R K O O N N Co O N N O R N N 10 Figure 103 X-ray crystal structure of complex III with bound cytochrome c (PDB code: 1KYO, resolution 2.97 Å). a) Binding of cytochrome c to the homodimeric QCR. Bound cytochrome c site is encircled. b) Direct heme-to-heme electron transfer from cytochrome c1 to cytochrome c. The short edge-to-edge distance from one heme to the other is 9.4 Å. At the cytochrome oxidase the ferrous cytochrome c is oxidized to its ferric form. The electron is transferred first to CuA, a binuclear copper center, which has a higher redox potential of 290 mV vs. SHE [188]. From there, the electron is transferred through the protein to the final electron acceptor dioxygen. Cytochrome c is present in all organisms that have mitochondrial respiratory chains: plants, animals, and eukaryotic microorganisms. The protein evolved 1.5 billion years ago, before the divergence of plants and animals. Its function has been conserved until today; cytochrome c of any eukaryotic species reacts in vitro with the cytochrome c oxidase of any other species tested so far. 176 Binding Energy and Binding Constant -ΔG = R.T.lnK -ΔG = Binding Energy K = Binding Constant K = Complexed / Uncomplexed –5,7 kJ/mol ! K = 10 ! 90.1% Complexed (if conc. = 1) 11 Binding Energy and Binding Constant -ΔG = R.T.lnK -ΔG = Binding Energy K = Binding Constant K = Complexed / Uncomplexed –50 kJ/mol ! K = 109 ! 99.9999% Complexed - Nanomolar Binding 12 Intermolecular Interactions Are Dynamic 85 kJ/mol ! Available at 25 °C - G/RT Eyring Equation: k = k T/h*e Δ B 1 min Half Life = ln2/k Dynamic Processes Unless: –85 kJ/mol ! K = 1015 Femtomolar Binding 13 Intermolecular Interactions Are Dynamic Reversible Binding: –34 kJ/mol ! K = 106 ! mikromolar Binding Example: Kinase A Binding to ATP Limit to Irreversible Binding: –68 kJ/mol ! K = 1012 ! picomolar Binding Example: G Protein Binding to GTP/GDP 14 Binding Constant: What‘s In It? - Not so Easy Binding in Water -ΔG = -ΔH + TΔS -ΔG = R.T.lnK 15 Methods to Quantify Binding o Ka, ∆G ion conductivity, UV/Vis absorption, fluorescence, NMR, calorimetry. ∆Ho, T∆So Variable temperature 1H NMR, van’t Hoff analysis Titration microcalorimetry o ∆Cp Titration microcalorimetry o ( heat capacity changes = ∂(∆H )/∂T ) Note that Kd ≈ Ki = 1/Ka (supramolecular chemists use Ka, medicinal chemists Kd and Ki) 16 Methods to Quantify Binding Formation of 1:1 host (H) – guest (G) complex proceeds according to: a – sensitive nucleus on Guest free ligand concentration b – analogous nucleus in complex c – nucleus insensitive to compexation NMR titration plot for a fast equilibrating (on Job plot showing a 1:1 host – guest the NMR timescale) system complex 17 Specifics of Binding Specific Molecular Interactions – Molecular Recognition Substrate Receptor Complex O O O H O H O O H O H O 18 Molecular Recognition – Lock and Key „To use a picture I would like to say that enzyme and glucoside have to fit together like lock and key in order to exert a chemical effect on each other.“ Fischer, Ber. Dtsch. Chem. Ges. 1894, 27, 2985. Tyr169 His181 Glu409 N NH O W OH O OH W W Trp402 H O OH NH NH O HO OH 2 HO S HN O OH W HN W OH Arg W W 243 O O O O O O O HO NH2 Glu167 Glu356 Glu167 Gln316 β-Glucosidase Tyr298 Isorna, Polaina, Latorre-Garcia, Cañada, Gonzalez, Sanz-Aparicio, J. Mol. Biol. 2007, 371, 1204. 19 Molecular Recognition – Specific Interactions Complex Network of Weak Interactions Aricept® in Acetylcholinesterase – Alzheimer‘s Disease H O H Phe330 O cation–π MeO + N Trp84 MeO π–π stacking H π–π stacking H O H H Trp279 O O–H/π H Kryger, Silman, Sussman, J. Physiol. (Paris) 1998, 92, 191–194; Kryger, Silman, Sussman, Structure 1999, 7, 297–307. 20 Weak Interactions: Toolbox to Make Supramolecules 21 Hydrogen Bonds A hydrogen bond is the bonding of a covalently attached H-atom with a second atom Bond energy range: 4 – 120 kJ/mol 22 Hydrogen Bonds 23 Hydrogen Bonds Long Range Interactions: Energy scales with 1/r 24 Hydrogen Bonds Structure and Stability of H-Bonds 25 Hydrogen Bonds DNA Double and Triple Helices 26 Hydrogen Bonds Secondary Electrostatic Interactions – Important if # of H- Bonds are Identical 27 Hydrogen Bonds H H N N H O O H N N N N N N H Et N H N Et N N N N O H N Sugar Cy O H N H H C G T 2-Aminoadenine Prior to the model advanced by Jorgensen, these differences in stability between base pair associations were not understood. 28 Hydrogen Bonds When the model was developed in 1991, a DDD . AAA system was missing. It was later prepared by Zimmerman (J. Am. Chem. Soc. 1992, 114, 4010), confirming the predictions based on consideration of secondary electrostatic effects 29 Electrostatic Interactions Ion Paring (Coulombic Interactions) Coulomb Potential: 1 z+ z–·e2 U = potential energy ____ . ______ Uion-ion = – z · e = ionic charge 4π·εo ε r· r εr, εo = dielectric constant of vacuum (=1) and environment (solvent) r = distance between ions 30 Electrostatic Interactions Ion-pairing often is entropy-driven, due to solvation of the interacting ions Table. Selected thermodynamic ion-pairing parameters (kcal mol-1) at 298 °C in water. ______________________________________________________________________ Ion pair ∆G° ∆H° T∆S° ______________________________________________________________________ 2+ 2– Ca SO4 – 3.2 1.6 4.8 3+ 3– La Fe(CN)6 – 5.1 2.0 7.1 ______________________________________________________________________ • Free enthalpy contribution of non-buried salt bridges: 1.25 ± 0.25 kcal mol-1 H. J. Schneider et al. Chem. Soc. Rev. 1994, 23, 227 31 Electrostatic Interactions Ion – Dipole Interactions (50 – 200 kJ/mol) 1 z · e · µ · cos θ εo = dielectric constant ____ __________________ Uion-dipole = – · z · e = ionic charge 2 4 π εo r r = distance between centers of ion and dipole µ = dipole moment θ = angle between dipole and line r. O H H O O O r K ϑ K O O O 32 Ion Dipole Interactions at Work O R R O O O O O O R R N O O N O O O O O O O O O O Me R O O O 33 Ion Dipole Interactions at Work – Ion Channels Compare 8-fold coordination by valinomycin and 6-fold Coordination by nonactin The selectivity filter of the K+ channel depicted as five sets of four-in-plane O-atoms with K+ ions (green) undergoing cubic coordination to eight protein C=O groups in the 1,3- and 2,4-configurations. Movement by two paths involves octahedral coordination by six O-atoms, two provided by the intervening H2O molecules. 34 Electrostatic Interactions Dipole – Dipole Interactions (5 – 50 kJ/mol) 35 Electrostatic Interactions The potential energy of interaction between two polar molecules is a complicated function of the angle between them. However, when the two dipoles are parallel or aligned towards each other, the energy between stationary polar molecules is f µ1 · µ2 µ = dipole moment _____ ____________ 2 Udipole-dipole = · f = 1 – 3 cos θ 3 (4 π ·εo ) r There is a non-zero average interaction between freely rotating dipoles.
Recommended publications
  • Protein-Ligand Interactions from Molecular Recognition to Drug Design
    P ) . .~ " r~- O Protein-Ligand Interactions From Molecular Recognition to Drug Design Edited by H.-J. Bohm and C. Schneider WILEY- VCH WILEY-VCH GmbH & Co. KGaA Contents Preface XI A Personal Foreword XIII List of Contributors XV List of Abbreviations XVII Prologue 1 David Brown 1 Prediction of Non-bonded Interactions in Drug Design 3 H.-J. Bohm 1.1 Introduction 3 1.2 Major Contributions to Protein-Ligand Interactions 4 1.3 Description of Scoring Functions for Receptor-Ligand Interactions 8 1.3.1 Force Field-based Methods 9 1.3.2 Empirical Scoring Functions 9 1.3.3 Knowledge-based Methods 11 1.4 Some Limitations of Current Scoring Functions 12 1.4.1 Influence of the Training Data 12 1.4.2 Molecular Size 13 1.4.3 Water Structure and Protonation State 13 1.5 Application of Scoring Functions in Virtual Screening and De Novo Design 14 1.5.1 Successful Identification of Novel Leads Through Virtual Screening 14 1.5.2 De novo Ligand Design with LUDI 25 1.6 Outlook 16 1.7 Acknowledgments 17 1.8 References 17 VI Contents 2 Introduction to Molecular Recognition Models 21 H.-J. Schneider 2.1 Introduction and Scope 21 2.2 Additivity of Pairwise Interactions - The Chelate Effect 22 2.3 Geometric Fitting: The Hole-size Concept 26 2.4 Di- and Polytopic Interactions: Change of Binding Mechanism with Different Fit 28 2.5 Deviations from the Lock-and-Key Principle 30 2.5.1 Strain in Host-Guest Complexes 30 2.5.2 Solvent Effects 30 2.5.3 Enthalpy/Entropy Variations 31 2.5.4 Loose Fit in Hydrophobically Driven Complex Formation 32 2.6 Conformational Pre-organization: Flexible vs.
    [Show full text]
  • Molecular Recognition and Advances in Antibody Design and Antigenic Peptide Targeting
    International Journal of Molecular Sciences Editorial Molecular Recognition and Advances in Antibody Design and Antigenic Peptide Targeting Gunnar Houen and Nicole Trier * Department of Neurology, Rigshospitalet Glostrup, Nordre Ringvej 57, 2600 Glostrup, Denmark; [email protected] * Correspondence: [email protected] Received: 11 February 2020; Accepted: 15 February 2020; Published: 19 February 2020 Molecular recognition, the specific interaction between molecules by a combination of physical forces, has been a subject of scientific investigation for decades. The physical forces involve a combination of dipole-dipole interactions (van der Waals forces), hydrogen bonds and ionic interactions, and it is the optimal spatial combination of these interactions, that defines the specificity, i.e., the strength of the interaction, measured as an affinity constant, defined by the association and dissociation rate constants: Ka = kon/koff [1–3]. Specific interactions in living organisms are numerous, ranging from base pairing in DNA and RNA, protein folding, protein interactions and many more, constituting the basis of life [4–6]. Molecular recognition of foreign substances (self/non-self recognition) is the basis of immune defense against pathogens, spanning from less specific (promiscuous) MHC-peptide interactions to highly specific T cell (antigen) receptor (TCR) recognition of MHC-peptide complexes and from less specific IgM-antigen interactions to highly specific IgG-antigen interactions [7–9]. Through the study of the aforementioned specific interactions, scientists have learned to use natural molecules as reagents and have developed new reagents based on the same principles and physical forces. This issue of IJMS, entitled “Advances in Antibody Design and Antigenic Peptide Targeting” aims to give a status of the current “state-of-the-art” in specific molecular recognition.
    [Show full text]
  • Introduction to the Molecular Recognition and Self-Assembly Special Feature
    SPECIAL FEATURE: INTRODUCTORY PERSPECTIVE Introduction to the Molecular Recognition and Self-Assembly Special Feature Julius Rebek, Jr.1 The Skaggs Institute for Chemical Biology and Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037 olecular recognition is a ist. On mixing, these 2 capsules form a and departures for possible future mature branch of chemical hybrid, which is more stable than ei- applications. science, and why shouldn’t ther original capsule. The formation of A switching device is involved in a it be? Decades of studies in the hybrid provides places for guests calyx[6]arene that offers 2 separate Mphysical organic chemistry have defined that could not otherwise be accommo- binding sites in the work of Coquie`re et and evaluated the weak intermolecular dated. As in certain schools of archi- al. (6), ‘‘Multipoint molecular recogni- forces involved when 2 molecules en- tecture, reversible encapsulation is less tion within a calix[6]arene funnel com- counter each other. Every bimolecular about the mechanical boundaries of plex.’’ The conformational mobility of reaction, whether it occurs in the gas the walls than it is about the spaces the system allows 3 imidazole arms to phase, dilute solution, or an enzyme’s they define. chelate zinc ions, while 3 aniline nitro- interior, begins with a recognition event. Encapsulation of multiple groups of gens converge to provide a binding site It is an apt time to explore recognition small molecules is the theme of the arti- for a single proton. The space in the in a larger context, that of multicompo- cle by Yamauchi et al.
    [Show full text]
  • Design of Molecular Mechanics Modeling Techniques for Exploring Molecular Recognition Using Cyclodextrins
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2003 “Design of Molecular Mechanics Modeling Techniques For Exploring Molecular Recognition Using Cyclodextrins. Shannon Bradley Fox University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Chemistry Commons Recommended Citation Fox, Shannon Bradley, "“Design of Molecular Mechanics Modeling Techniques For Exploring Molecular Recognition Using Cyclodextrins.. " PhD diss., University of Tennessee, 2003. https://trace.tennessee.edu/utk_graddiss/2013 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Shannon Bradley Fox entitled "“Design of Molecular Mechanics Modeling Techniques For Exploring Molecular Recognition Using Cyclodextrins.." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Chemistry. M. J. Sepaniak, Major Professor We have read this dissertation and recommend its acceptance: Elizabeth Howell, S. D. Gilman, Z. Xue Accepted
    [Show full text]
  • Receptor Compaction and Gtpase Movements Drive Cotranslational Protein Translocation
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.07.897827; this version posted January 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Receptor compaction and GTPase movements drive cotranslational protein translocation Jae Ho Lee1, SangYoon Chung2, Yu-Hsien Hwang Fu1,a, Ruilin Qian1,b, Xuemeng Sun1,c, Shimon Weiss2,3, Shu-ou Shan1* 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125 2Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA 90095 3Department of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, 52900, Israel aCurrent address: Department of Biochemistry, Stanford University, Stanford, CA, 94305 bCurrent address: Department of Chemistry, University of Science and Technology of China, Hefei, 230026, China cCurrent address: Department of Chemistry, Princeton University, Princeton, NJ, 08544 *corresponding author. Email: [email protected] Keywords: Protein Targeting, Signal Recognition Particle, Single Molecule Spectroscopy, GTPase, Ribosome, Protein Dynamics Short Title: SRP receptor compaction drives protein targeting bioRxiv preprint doi: https://doi.org/10.1101/2020.01.07.897827; this version posted January 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Signal recognition particle (SRP) is a universally conserved targeting machine that couples the synthesis of ~30% of the proteome to their proper membrane localization1,2.
    [Show full text]
  • From Genomics to Scientomics: Expanding the Bioinformation Paradigm
    Information 2011, 2, 651-671; doi:10.3390/info2040651 OPEN ACCESS information ISSN 2078-2489 www.mdpi.com/journal/information Article From Genomics to Scientomics: Expanding the Bioinformation Paradigm Raquel del Moral, Mónica González, Jorge Navarro and Pedro C. Marijuán * Bioinformation and Systems Biology Group Instituto Aragonés de Ciencias de la Salud (I+CS), 50009 Zaragoza, Spain; E-Mails: [email protected] (R.M.); [email protected] (M.G.); [email protected] (J.N.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: (0034) 976 713 526; Fax: (0034) 976 715 554. Received: 1 September 2011; in revised form: 31 October 2011 / Accepted: 1 November 2011 / Published: 9 November 2011 Abstract: Contemporary biological research (particularly in systems biology and the “omic” disciplines) is factually answering some of the poignant questions associated with the information concept and the limitations of information theory. Here, rather than emphasizing and persisting on a focalized discussion about the i-concept, an ampler conception of “informational entities” will be advocated. The way living cells self-produce, interact with their environment, and collectively organize multi-cell systems becomes a paradigmatic case of what such informational entities consist of. Starting with the fundamentals of molecular recognition, and continuing with the basic cellular processes and subsystems, a new interpretation of the global organization of the living cell must be assayed, so that the equivalents of meaning, value, and intelligence will be approached along an emerging “bioinformational” perspective. Further insights on the informational processes of brains, companies, institutions and human societies at large, and even the sciences themselves, could benefit from—and cross-fertilize with—the advancements derived from the informational approach to living systems.
    [Show full text]
  • Molecular-Recognition-Directed Formation of Supramolecular Polymers
    Polymer Journal (2013) 45, 363–383 & 2013 The Society of Polymer Science, Japan (SPSJ) All rights reserved 0032-3896/13 www.nature.com/pj INVITED REVIEW Molecular-recognition-directed formation of supramolecular polymers Takeharu Haino In recent years, significant research effort has focused on creating supramolecular polymers that can be attained by specific host–guest interactions of the repeating units. During the supramolecular polymerization process, molecular recognition events, which are predetermined by the molecular building blocks, are highly selective and directional for defining the size, direction and dimension of the resulting supramolecular polymers. The diversity of the supramolecular building blocks ranges from small aromatic units to macrocycles. Recently, the interplay of supramolecular and polymer chemistry has led to the creation of novel supramolecular materials, which display fascinating functions such as self-healing, stimuli-responsiveness and rubber-like elastomeric properties. Supramolecular cross-linking and supramolecular block copolymerization are the methods that have been used to install fascinating and functional moieties onto polymer backbones. Currently, the development of practical supramolecular polymeric materials is an ongoing challenge for supramolecular chemists. This review will focus on the recent developments in supramolecular polymers composed of discrete repeating units, as well as novel supramolecular materials produced by the interplay of supramolecular and polymer chemistry. Polymer Journal (2013) 45, 363–383; doi:10.1038/pj.2012.144; published online 1 August 2012 Keywords: block copolymerization; cross-linking; molecular recognition; noncovalent interaction; supramolecular chemistry; supramolecular polymer INTRODUCTION Liquid and molecular crystals lose polymeric structures and Synthetic polymers with tailored properties offer a wide range of properties in the isotropic state and in solution.
    [Show full text]
  • Supramolecular Recognition: on the Kinetic Lability of Thermodynamically Stable Host–Guest Association Complexes
    Supramolecular recognition: On the kinetic lability of thermodynamically stable host–guest association complexes Andrew J. Goshe*, Ian M. Steele*, Christopher Ceccarelli†, Arnold L. Rheingold†, and B. Bosnich*§ *Department of Chemistry, University of Chicago, 5735 South Ellis Avenue, Chicago, IL 60637; and †Department of Chemistry and Biochemistry, University of Delaware, Newark, DE 19716 Edited by Jack Halpern, University of Chicago, Chicago, IL, and approved January 16, 2002 (received for review November 2, 2001) A molecular receptor consisting of a spacer bearing two cofacially Table 1. Supramolecular assembly bonds disposed terpyridyl–palladium–ligand (terpy-Pd-L) units rigidly sepa- rated by about 7 Å has been investigated for molecular recognition of planar aromatic molecules. It is found that although the receptor forms stable 1:2 host–guest association complexes with 9-methylan- thracene (9-MA), the guest undergoes very rapid site exchange within the receptor and with external free 9-MA. A crystal structure of the 2:1 adduct shows one 9-MA in the molecular cleft defined by the two terpy-Pd-L units and the other resides on an outside face of one terpy-Pd-L unit. To establish the site residency time of the guests, a number of tethered molecules were prepared. These involve an anthracene molecule tethered to a pyridine ligand bound to the palladium atoms to form intramolecular host–guest adducts. Rotating-frame Overhauser effects were used to infer the site resi- many third row transition metal bonds are stable and nonlabile, dency of the anthracene guests in the receptor. Variable-temperature and such bonds are used as the permanent components of 1H NMR spectroscopy of the intramolecular host–guest complexes supramolecular structures.
    [Show full text]
  • 2 1663 May 2015
    2 1663 May 2015 IN A HOME PREGNANCY TEST, something inside Th e good news is that a solution is known: More the test stick reacts with something in the sample to provide reliable antibodies can be generated in the laboratory based a yes or no answer. Th e stakes are high: if the something on the genetic code that defi nes them. In fact, Bradbury’s inside the test doesn’t properly identify its target, there could team at Los Alamos has already created a successful pipeline be a lot of unhappy people. for making highly specifi c antibodies of this kind in a A pregnancy test is an example of an immunoassay— high-throughput, standardized fashion. And another team a test used in molecular biology labs worldwide. Immuno- at Los Alamos has developed a way to create other types of assays take advantage of immune-system proteins called binding proteins using specialized algorithms to customize antibodies that recognize and bind to specifi c target mole- their shape for any desired target. Together, the researchers’ cules, confi rming their presence in a sample. Th is general hope is that these technologies, alongside increased demand idea of molecular recognition is a powerful concept used by from the scientifi c community, will change the paradigm for living organisms for nearly all cellular processes. It is well using standardized molecular recognition in research once known that antibodies recognize pathogens, but cells also use and for all. recognition proteins for many other functions, such as on-off switches for gene transcription or enzyme production, and Building without a blueprint as receptors for communication between cells.
    [Show full text]
  • Molecular Recognition: Storage and Processing of Molecular Information
    Molecular Recognition: Storage and Processing of Molecular Information Tanya Sienko and Jean-Marie Lehn 컴퓨터공학과 99419-042 안병규 Contents Å Molecular Computing Basics Å Molecular Recognition ¿ Covalent Chemistry ¿ Definition ¿ Basics ¿ Examples ¿ Multiple Recognition ¿ Chemical Systems ¿ Self-Assembly, Self-Recognition, and Self-Organization Å Thermodynamics, Information, and Entropy Authors (1) Å Jean-Marie Lehn ¿ Winner of The Nobel Prize in Chemistry 1987 Authors (2) Å Tanya Sienko ¿ Coauthor of “Molecular Computing” Molecular Computing Basics (1) Å physical base underlying every computing system ¿ semi-conductor material ¿ more complicated organic chemical Å Reverse the demarcation of computing into “hardware” and “software” ¿ cannot make a distinction Å instead of “programmability”, ¿ “evolution”, ¿ “adaptability,” ¿ and “informed materials” Molecular Computing Basics (2) Å “information” lie in ¿ chemical ¿ supramolecular attributes and behavior Å gains ability of implementing ¿ “recognition,” ¿ self-organization, ¿ and other “high-level” behaviors Å molecular recognition ¿ supramolecular interaction algorithms Covalent Chemistry (1) Å assemble molecules from atoms or smaller molecules Å Structure and functionality depend on the anture of the atoms and on their geometrical arrangement Covalent Chemistry (2) Covalent Chemistry (3) Covalent Chemistry (4) Molecular Recognition – Definition (1) Å Is the binding and specific selection of substrates by a given receptor molecule ¿ binding forms a complex, or supramolecule Å Pattern-recognition
    [Show full text]
  • Molecular Motions in Functional Self-Assembled Nanostructures
    Int. J. Mol. Sci. 2013, 14, 2303-2333; doi:10.3390/ijms14022303 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Review Molecular Motions in Functional Self-Assembled Nanostructures Alexandre Dhotel 1,2, Ziguang Chen 2, Laurent Delbreilh 1, Boulos Youssef 1, Jean-Marc Saiter 1 and Li Tan 2,* 1 AMME–LECAP, EA4528, International Laboratory, Institut des Matériaux de Rouen, Université et INSA de Rouen, BP12, 76801 Saint Etienne du Rouvray Cedex, France; E-Mails: [email protected] (A.D.); [email protected] (L.D.); [email protected] (B.Y.); [email protected] (J.-M.S.) 2 AMME–A-TEAM, Department of Mechanical and Materials Engineering, University of Nebraska, Lincoln, NE 68588, USA; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-402-472-4018. Received: 11 December 2012; in revised form: 11 January 2013 / Accepted: 11 January 2013 / Published: 24 January 2013 Abstract: The construction of “smart” materials able to perform specific functions at the molecular scale through the application of various stimuli is highly attractive but still challenging. The most recent applications indicate that the outstanding flexibility of self-assembled architectures can be employed as a powerful tool for the development of innovative molecular devices, functional surfaces and smart nanomaterials. Structural flexibility of these materials is known to be conferred by weak intermolecular forces involved in self-assembly strategies. However, some fundamental mechanisms responsible for conformational lability remain unexplored. Furthermore, the role played by stronger bonds, such as coordination, ionic and covalent bonding, is sometimes neglected while they can be employed readily to produce mechanically robust but also chemically reversible structures.
    [Show full text]
  • From Host-Guest to Protein-Ligand Binding Joel Jose Montalvo Acosta
    Computational approaches to molecular recognition : from host-guest to protein-ligand binding Joel Jose Montalvo Acosta To cite this version: Joel Jose Montalvo Acosta. Computational approaches to molecular recognition : from host-guest to protein-ligand binding. Theoretical and/or physical chemistry. Université de Strasbourg, 2018. English. NNT : 2018STRAF051. tel-02145764 HAL Id: tel-02145764 https://tel.archives-ouvertes.fr/tel-02145764 Submitted on 3 Jun 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Logo UNIVERSITÉ DE STRASBOURG Ecole doctorale ÉCOLE DOCTORALE DES SCIENCES CHIMIQUES Institut de Science et d’Ingénierie Supramoléculaires Institut de Chimie THÈSE présentée par : Joel José Montalvo Acosta soutenue le : 4 september 2018 pour obtenir le grade de : Docteur de l’université de Strasbourg Discipline/ Spécialité : Chimie/ Chimie informatique et théorétique Approches Computationnelles de la Reconnaissance Moléculaire: L'analyse de la Liaison Hôte-Invité et Protéine-Ligand THÈSE dirigée par : M. CECCHINI Marco Maître de Conférences, Université de Strasbourg RAPPORTEURS : M. MICHEL Julien Professeur, Université d’Édimbourg M. SIMONSON Thomas Professeur, École Polytechnique AUTRES MEMBRES DU JURY : M. ROGNAN Didier Professeur, Université de Strasbourg M. RENDINE Stefano Chercheur, Syngenta AG COMPUTATIONALAPPROACHESTOMOLECULAR RECOGNITION:FROMHOST-GUESTTO PROTEIN-LIGANDBINDING.
    [Show full text]