© 2015 Michelle Hause Johnson

Total Page:16

File Type:pdf, Size:1020Kb

© 2015 Michelle Hause Johnson © 2015 Michelle Hause Johnson ANTHOCYANINS AND PROANTHOCYANIDINS FROM BLUEBERRY AND BLACKBERRY FERMENTED BEVERAGES TO REDUCE INFLAMMATION AND TYPE-2 DIABETES: A COMPREHENSIVE IN VITRO AND IN VIVO EVALUATION BY MICHELLE HAUSE JOHNSON DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Nutritional Sciences in the Graduate College of the University of Illinois at Urbana-Champaign, 2015 Urbana, Illinois Doctoral Committee: Professor John Erdman, Jr., Chair Professor Elvira de Mejia, Director of Research Professor Gregory Freund Professor Jack Juvik Professor Mary Ann Lila, North Carolina State University ABSTRACT Type-2 diabetes is a serious metabolic disease that currently affects 9.3% of the U.S. population and is aggravated by diets low in fruit and vegetable intake leading to postprandial oxidative stress and inflammation [1, 2]. Berries are one of the richest dietary sources of polyphenolic compounds associated with decreased markers of chronic inflammation and decreased risk for type-2 diabetes [3,4], specifically anthocyanins [5] (ANC) and proanthocyanidins (PAC), polymerized forms of ANC and other phenolics [6]. Fermentation is a feasible way to increase phenolic content of berry juice products, and fermented products may be more bioactive than their unfermented counterparts, thus increasing the potential for health benefits [7]. Our long-term goal, as outlined in Chapter 1, was to fill the gap of knowledge in understanding the mechanisms by which dietary bioactives found in fermented berry beverages contribute to the management of type-2 diabetes using in vitro enzymatic kinetics, analytical assays, computational modeling, in vitro cell culture, and in vivo animal feeding trials. A review of the literature focusing on the role of berries to reduce diabetes is presented in Chapter 2. Our research focused on the evaluation and potential mechanisms of action of alcohol- free blueberry (Vaccinium corymbosum) and blackberry (Rubus spp.) fermented beverages and their bioactive ANC and PAC components to reduce inflammation and type-2 diabetes in vitro and in vivo through the aims introduced in Chapter 3. First we prepared blueberry and blackberry wines that were comparable to those commercially available (Chapter 4), and found that blueberries and their wine, especially when fermented at cold temperature, contain phenolic compounds that act as antioxidants and potential inhibitory factors of starch-cleaving enzymes that could be used to limit glucose absorption (Chapter 5). We then generated ANC- and PAC- enriched fractions and found that blueberry and blackberry ANC and PAC inhibited expression of proteins in the NF-κB-mediated pathway in macrophages, indicating potential to reduce chronic inflammation associated with hyperglycemia (Chapter 6). ANC were found to bind strongly within active pockets of dipeptidyl-peptidase IV (DPP-IV), an incretin-cleaving target of diabetes therapy, to effectively reduce its activity (IC50 = 4.0 μM) (Chapter 7). ANC were also able to reduce the inflammatory cross-talk between macrophages and adipocytes (Chapter 8). Further, ANC increased insulin secretion from pancreatic β-cells by 233 and 100 µIU insulin/mL directly and after epithelial transport, respectively, due to reduced DPP-IV expression and up- ii regulated insulin-receptor associated genes and proteins (Chapter 9). Lastly, in Chapter 10 we established the role of phenolic compounds in 30:70% blueberry-blackberry alcohol-free fermented beverage to reduce body weight (BW) gain (10.3 vs. 16.7 g), fat mass (18.0 vs. 31.3% BW), and fasting blood glucose (184 vs. 222 mg/dL), in diet-induced obese C57BL/6j mice consuming ANC beverage vs. water, (p < 0.05). There were some adverse effects noted at the highest dose, warranting future studies into the optimal dose of phenolic compounds extracted and concentrated from the alcohol-free fermented blueberry-blackberry beverage. These results, as summarized in Chapter 11, suggest that ANC in blueberry and blackberry fermented beverages are beneficial sources of antioxidants, decrease glucose absorption, and inhibit inflammation and DPP-IV, and therefore could be used in the management of type-2 diabetes. 1. National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2014. Atlanta, GA: U.S. DHHS, Centers for Disease Control and Prevention, 2014. 2. Jennings A, Welch AA, Spector T, Macgregor A, Cassidy A. (2014) Intakes of anthocyanins and flavones are associated with biomarkers of insulin resistance and inflammation in women. J Nutr 144(2):202-8. 3. Ley SH, Hamdy O, Mohan V, Hu FB. (2014) Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet 383(9933):1999-2007. 4. van Dam RM, Naidoo N, Landberg R. (2013) Dietary flavonoids and the development of type 2 diabetes and cardiovascular diseases: review of recent findings. Curr Opin Lipidol 24(1):25-33. 5. Wu X, Beecher GR, Holden JM, Haytowitz DB, Gebhardt SE, Prior RL. (2006) Concentrations of anthocyanins in common foods in the United States and estimates of normal consumption. J Agric Food Chem. 54, 4069-4075. 6. Wedick NM, Pan A, Cassidy A, Rimm EB, Sampson L, Rosner B, Willett W, Hu FB, Sun Q, van Dam RM. (2012) Dietary flavonoid intakes and risk of type 2 diabetes in US men and women. Am J Clin Nutr 95: 925-33. 7. Su MS, Chien PJ. (2007) Antioxidant activity, anthocyanins, and phenolics of rabbiteye blueberry (Vaccinium ashei) fluid products as affected by fermentation. Food Chem 104:182- 187. iii ACKNOWLEDGMENTS I would like to thank my doctoral advisor Dr. Elvira de Mejia for her support and motivation to complete the work described in this dissertation, for providing a laboratory experience that spans from food chemistry to disease mechanism, and for being an example of confidence and execution. I would also like to thank the members of my committee, Drs. Erdman, Freund, Juvik, and Lila for their time and contributions to my project, including Dr. William Artz for being on my committee for the first two years of my graduate studies. I would especially like to thank Dr. Lila for welcoming me into her lab at the Plants for Human Health Research Institute in Kannapolis, North Carolina to learn more about blueberries and to complete the phenolic extraction procedures. My research experience was supported by the lab team of all the current and former de Mejia lab members and visiting scholars, of whom I’d like to thank especially Vermont Dia and Jodee Johnson for training and support. I would also like to thank my collaborating visiting scholars Diego Garcia, for work with the adipocytes, and Junfeng Fan for work with the computational modeling. This work would not have been possible without the assistance of my undergraduate mentees in Food Science and Human Nutrition over the years (including Anita Lucius, Tessa Meyer, Mellisa Yu, Ally Meyer, Julia Amador, Valerie Munoz, Starr’Retiece Gibson, Luis M Real Hernandez, Lauren Hall, and Cheng Lu, among all the undergraduates working in our laboratory). Thanks to the Division of Nutritional Sciences for supporting my travel to scientific conferences and many opportunities for research funding and collaborating. I also want to thank Dr. Hartke, Dr. Johnson, Arena Jackson, and Ashley Brown in the DNS office for their excellent administrative assistance and support during my graduate studies. It has been a joy to be surrounded by all the graduate students in DNS for making my grad school experience a friendly and collaborative environment in which to learn and to conduct research. Finally, I want to thank my family for their unconditional support throughout my life and during grad school, particularly my mom and my sister for editing several of my papers and applications. None of my successes would have been possible without their love and encouragement. iv TABLE OF CONTENTS CHAPTER 1: OVERALL HYPOTHESES AND OBJECTIVES ............................................... 1 GENERAL INTRODUCTION ............................................................................... 2 CHAPTER 2: LITERATURE REVIEW: DIABETES AND BERRIES ...................................... 7 CHAPTER 3: OBJECTIVES ...................................................................................................... 46 CHAPTER 4: COMPARISON OF CHEMICAL COMPOSITION AND ANTIOXIDANT CAPACITY OF COMMERCIALLY AVAILABLE BLUEBERRY AND BLACKBERRY WINES IN ILLINOIS ........................................................................................................... 48 CHAPTER 5: CULTIVAR EVALUATION AND EFFECT OF FERMENTATION ON ANTIOXIDANT CAPACITY AND IN VITRO INHIBITION OF α-AMYLASE AND α- GLUCOSIDASE BY HIGHBUSH BLUEBERRY (Vaccinium corombosum) .................... 72 CHAPTER 6: ANTHOCYANINS AND PROANTHOCYANIDINS FROM BLUEBERRY- BLACKBERRY FERMENTED BEVERAGES INHIBIT MARKERS OF INFLAMMATION IN MACROPHAGES AND CARBOHYDRATE-UTILIZING ENZYMES IN VITRO ........................................................................................................... 97 CHAPTER 7: BERRY AND CITRUS PHENOLIC COMPOUNDS INHIBIT DIPEPTIDYL PEPTIDASE IV: IMPLICATIONS IN DIABETES MANAGEMENT ............................. 135 CHAPTER 8: ANTHOCYANINS FROM FERMENTED BERRY BEVERAGES INHIBIT INFLAMMATION-RELATED ADIPOSITY RESPONSE IN VITRO .............................. 164 CHAPTER 9: ANTHOCYANINS FROM FERMENTED BERRY BEVERAGES INCREASED INSULIN SECRETION FROM PANCREATIC Β-CELLS IN VITRO BEYOND THEIR
Recommended publications
  • Open Babel Documentation Release 2.3.1
    Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options .................................................
    [Show full text]
  • JRC QSAR Model Database
    JRC QSAR Model Database EURL ECVAM DataBase service on ALternative Methods to animal experimentation To promote the development and uptake of alternative and advanced methods in toxicology and biomedical sciences SDF - STRUCTURE DATA FORMAT: How to create from SMILES The European Commission’s science and knowledge service Joint Research Centre Directorate F Health, Consumers & Reference Materials Chemicals Safety & Alternative Methods Unit The European Commission’s science and knowledge service Joint Research Centre EUR 28708 EN This publication is a Tutorial by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide user support. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. Contact information Email: [email protected] JRC Science Hub https://ec.europa.eu/jrc JRC107492 EUR 28708 EN PDF ISBN 978-92-79-71294-4 ISSN 1831-9424 doi:10.2760/952280 Print ISBN 978-92-79-71295-1 ISSN 1018-5593 doi:10.2760/668595 Luxembourg: Publications Office of the European Union, 2017 Ispra: European Commission, 2017 © European Union, 2017 The reuse of the document is authorised, provided the source is acknowledged and the original meaning or message of the texts are not distorted. The European Commission shall not be held liable for any consequences stemming from the reuse. How to cite this document: Triebe
    [Show full text]
  • MACSIMUS Manual
    1 MACSIMUS manual benzocaine (ethylaminobenzoate) parameter_set = charmm21 HA | HA-CT-HA | HA-CT-HA | OSn.2 | Cp.7=On.5 | C6R--C6R-HA Most often used links: | | HA-C6R C6R-HA 2.2 Blend synopsis and options | | HA-C6R--C6R-NPn.5^-Hp.25 9.2 Cook synopsis and options | 9.2.5 Cook input data Hp.25 MACromolecule SIMUlation Software © Jiˇr´ıKolafa 1993{2020 MACSIMUS may be distributed under the terms of the GNU General Public Licence Credits: ray: Mark VandeWettering \reasonably intelligent raytracer" CHARMM force field (files: charmm*.par, charmm*/*.rsd) GROMOS force field (files: gromos*.par, gromos*/*.rsd) amoeba implementation by Z. Wagner moil support by J. Schofield several bug fixes by T. Trnka bug discovered by N. Parfenov Contents I Program `blend' version 2.4b 14 1 Introduction 16 1.1 Force fields...................................... 16 1.2 `blend' overview.................................... 16 1.3 Versions........................................ 17 2 Running blend 18 2.1 Environment...................................... 18 2.2 Synopsis........................................ 19 2.2.1 Global options................................. 19 2.2.2 par-options and parameter files....................... 20 2.2.3 mol-options and molecular files....................... 22 2.2.4 Extra-options ................................. 29 2.3 File extensions.................................... 34 2.4 Run-time control................................... 37 2.4.1 get data format for input........................... 37 2.4.2 Scrolling.................................... 38 2.4.3 Error handling................................ 39 2.4.4 Interrupts................................... 40 2.5 Showing molecules graphically............................ 40 2.5.1 X11 Graphics................................. 40 2.5.2 Playback output............................... 44 2.6 Energy minimization................................. 44 2.7 Missing coordinates.................................. 45 3 Force field and the parameter file 46 3.1 Structure of the parameter file...........................
    [Show full text]
  • Ontology-Based Classification of Molecules
    Ontology-Based Classification of Molecules: a Logic Programming Approach Despoina Magka Department of Computer Science, University of Oxford Wolfson Building, Parks Road, OX1 3QD, UK [email protected] Abstract. We describe a prototype that performs structure-based classification of molecular structures. The software we present implements a sound and com- plete reasoning procedure of a formalism that extends logic programming and builds upon the DLV deductive databases system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. In terms of performance, a no- ticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language which is suit- able for the Life Sciences domain and exhibits an encouraging balance between expressive power and practical feasibility. Keywords: Knowledge representation and reasoning, Logic programming and answer set programming, Cheminformatics. 1 Introduction The volume of bioinformatics data produced by research laboratories worldwide is in- creasing at an astonishing rate turning the need to adequately catalogue, represent and index the vast amounts of Life Sciences data sources into a pressing challenge. Semantic technologies have achieved significant progress towards the federation of biochemical information [33, 3, 4] via the definition and use of domain vocabularies with formal semantics, also known as ontologies. OWL [15], a family of logic-based knowledge representation (KR) formalisms standardised by W3C, has played a pivotal role in the advent of Semantic technologies due to its significant ability to reason over ontolo- gies by means of logical inference.
    [Show full text]
  • (CDK): an Open-Source Java Library for Chemo- and Bioinformatics
    J. Chem. Inf. Comput. Sci. 2003, 43, 493-500 493 The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo- and Bioinformatics Christoph Steinbeck,*,† Yongquan Han,† Stefan Kuhn,† Oliver Horlacher,‡ Edgar Luttmann,§ and Egon Willighagen# Max-Planck-Institute of Chemical Ecology, Jena, Germany, TheraSTrat AG, Allschwil, Switzerland, Institute of Organic Chemistry, University of Paderborn, Germany, and Nijmegen, The Netherlands Received August 17, 2002 The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo- and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given. 1. INTRODUCTION of software development, most widely recognized through the great success of the free Unix-like operating system Whoever pursues the endeavor of creating a larger GNU/Linux, a collaborative work of many individuals and software package in chemoinformatics or computational organizations, including the Free Software Foundation lead chemistry from scratch will soon be confronted with the by Richard Stallman and the Finish computer science student Syssiphus task of implementing the standard repertoire of Linus Torvalds who started the project. According to several chemoinformatical algorithms and components invented essays on this subject, open-source software, for which, by during the last 20 or 30 years.
    [Show full text]
  • Chemical File Format Conversion Tools : a N Overview
    International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 2, February - 2014 Chemical File Format Conversion Tools : A n Overview Kavitha C. R Dr. T Mahalekshmi Research Scholar, Bharathiyar University Principal Dept of Computer Applications Sree Narayana Institute of Technology SNGIST Kollam, India Cochin, India Abstract— There are a lot of chemical data stored in large different chemical file formats. Three types of file format databases, repositories and other resources. These data are used conversion tools are discussed in section III. And the by different researchers in different applications in various areas conclusion is given in section IV followed by the references. of chemistry. Since these data are stored in several standard chemical file formats, there is a need for the inter-conversion of II. CHEMICAL FILE FORMATS chemical structures between different formats because all the formats are not supported by various software and tools used by A chemical is a collection of atoms bonded together the researchers. Therefore it becomes essential to convert one file in space. The structure of a chemical makes it unique and format to another. This paper reviews some of the chemical file gives it its physical and biological characteristics. This formats and also presents a few inter-conversion tools such as structure is represented in a variety of chemical file formats. Open Babel [1], Mol converter [2] and CncTranslate [3]. These formats are used to represent chemical structure records and its associated data fields. Some of the file Keywords— File format Conversion, Open Babel, mol formats are CML (Chemical Markup Language), SDF converter, CncTranslate, inter- conversion tools.
    [Show full text]
  • Data Mining for Rational Drug Design
    FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Data Mining for Rational Drug Design Catarina Isabel Peixoto Candeias Mestrado em Engenharia Biomédica Supervisor: Rui Camacho July 7, 2017 c Catarina Isabel Peixoto Candeias, 2017 Data Mining for Rational Drug Design Catarina Isabel Peixoto Candeias Mestrado em Engenharia Biomédica Faculdade de Engenharia da Universidade do Porto July 7, 2017 Resumo Atualmente existe uma crescente incidência de doenças no mundo e por isso, existe uma maior quantidade de medicamentos disponíveis para promover uma maior eficiência nos sistemas de saúde. Assim, a importância dos novos medicamentos é indiscutível para a vida humana e, conse- quentemente traduz-se numa maior competência a nível profissional, trazendo muitos benefícios para a sociedade em geral. Por acréscimo, o seu uso consciente conduz também a uma decrescente necessidade de outros cuidados de saúde mais prolongados e de custos mais elevados. Os fármacos desencadeiam um efeito terapêutico que proporciona uma melhoria de qualidade de vida, no entanto, para sintetizar um fármaco novo, a indústria farmacêutica tem que percorrer um longo, complexo e oneroso processo. Um dos problemas de saúde que tem vindo a crescer exponencialmente corresponde às doenças neurodegenerativas. Este crescimento veio então aumentar a necessidade de descobrir e desen- volver novos fármacos que possam combater este problema. O processo de conceção de fármacos para este tipo de doenças, em que o fármaco tem que alcançar o Sistema Nervoso Central (SNC), é ainda mais demorado devido à complexidade do cérebro, à tendência dos fármacos para provo- carem efeitos adversos graves e principalmente devido à existência da Barreira Hemato-Encefálica (BHE).
    [Show full text]
  • Downloaded Aug
    O’Boyle Journal of Cheminformatics 2012, 4:22 http://www.jcheminf.com/content/4/1/22 RESEARCH ARTICLE Open Access Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI Noel M O’Boyle Abstract Background: There are two line notations of chemical structures that have established themselves in the field: the SMILES string and the InChI string. The InChI aims to provide a unique, or canonical, identifier for chemical structures, while SMILES strings are widely used for storage and interchange of chemical structures, but no standard exists to generate a canonical SMILES string. Results: I describe how to use the InChI canonicalisation to derive a canonical SMILES string in a straightforward way, either incorporating the InChI normalisations (Inchified SMILES) or not (Universal SMILES). This is the first description of a method to generate canonical SMILES that takes stereochemistry into account. When tested on the 1.1 m compounds in the ChEMBL database, and a 1 m compound subset of the PubChem Substance database, no canonicalisation failures were found with Inchified SMILES. Using Universal SMILES, 99.79% of the ChEMBL database was canonicalised successfully and 99.77% of the PubChem subset. Conclusions: The InChI canonicalisation algorithm can successfully be used as the basis for a common standard for canonical SMILES. While challenges remain – such as the development of a standard aromatic model for SMILES – the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits.
    [Show full text]
  • Herman Skolnik Award Symposium 2016
    Herman Skolnik Award Symposium 2016 Honoring Stephen Bryant and Evan Bolton A report by Wendy Warr ([email protected]) for the ACS CINF Chemical Information Bulletin Introduction Stephen Bryant and Evan Bolton were selected to receive the 2016 Herman Skolnik Award for their work on developing, maintaining, and expanding the Web-based National Center for Biotechnology Information (NCBI) PubChem database, and related software capabilities and analytical tools, to enhance the scientific discovery process. NCBI is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). A summary of Steve and Evan’s achievements has been published in the Chemical Information Bulletin. They were invited to present an award symposium at the Fall 2016 ACS National Meeting in Philadelphia, PA. They invited twelve speakers: L to R: Valery Tkachenko, Roger Sayle, Leah McEwen, Steve Heller, Wolf-Dietrich Ihlenfeldt (partially obscured), Yulia Borodina, Peter Linstrom, Steve Bryant, Marc Nicklaus (at front), Evan Bolton (at back), Steve Boyer, Daniel Zaharevitz, Christoph Steinbeck. Not pictured: Michel Dumontier (inset) Developing databases and standards in chemistry Steve Heller was the first speaker, with an amusing scene-setting talk. He admitted that his secret in getting to where he is now was “luck, luck, luck”. He disliked chemistry lab work; he was at the right place at the right time with the right people; he worked with supportive people; and he planned for who would take over the work next. If the problem were just technology, someone would have solved it already. The real problem is always cultural and political, not technical.
    [Show full text]
  • Open Babel Documentation
    Open Babel Documentation Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) Mar 26, 2021 Contents 1 Introduction 3 1.1 Goals of the Open Babel project.....................................3 1.2 Frequently Asked Questions.......................................4 1.3 Thanks..................................................7 2 Install Open Babel 11 2.1 Install a binary package......................................... 11 2.2 Compiling Open Babel.......................................... 11 3 obabel - Convert, Filter and Manipulate Chemical Data 19 3.1 Synopsis................................................. 19 3.2 Options.................................................. 19 3.3 Examples................................................. 22 3.4 Format Options.............................................. 24 3.5 Append property values to the title.................................... 24 3.6 Generating conformers for structures.................................. 24 3.7 Filtering molecules from a multimolecule file.............................. 25 3.8 Substructure and similarity searching.................................. 28 3.9 Sorting molecules............................................ 28 3.10 Remove duplicate molecules....................................... 28 3.11 Aliases for chemical groups....................................... 29 3.12 Forcefield energy and minimization................................... 30 3.13 Aligning molecules or substructures..................................
    [Show full text]
  • Chemistry Resources and Tools for Compound Selection Cheminformatics
    Chemistry resources and tools for compound selection Cheminformatics Noel M. O’Boyle NextMove Software and Open Babel developer “Noel O’Blog” Dec 2013 EMBL-EBI/Wellcome Trust Course: Resources for Computational Drug Discovery Cheminformatics • Hard to define in words: – David Wild: “The field that studies all aspects of the representation and use of chemical and related biological information on computers” – Design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information • Hard to agree on spelling: – Sometimes chemoinformatics • More easily thought of as encompassing a range of concepts and techniques – Molecular similarity – Quantitative-structure activity relationships (QSAR) – Substructure search – (Automated) Molecular depiction – Encoding/decoding of molecular structures – 3D structure generation from a 2D or 0D structure – Conformer generation – Algorithms: ring perception, aromaticity, isomers References • An introduction to cheminformatics, A. R. Leach, V. J. Gillet • Cheminformatics, Johann Gasteiger and Thomas Engel (Eds) • Molecular modelling – Principles and Applications, A. R. Leach • I571 Chemical Information Technology, David Wild, University of Indiana – http://i571.wikispaces.com – Introducing cheminformatics, D. Wild Molecular representation Mike Hann (GSK): “Ceci n'est pas une molecule serves to remind us that all of the graphics images presented here are not molecules, not even pictures of molecules, but pictures of icons which we believe represent some aspects of the molecule's properties.” http://mgl.scripps.edu/people/goodsell/mgs_art/hann.html Computer representations of molecules • How can a molecular structure be stored on a computer? – Common names: aspirin – IUPAC name: 2-acetoxybenzoic acid – Formula: C9H8O4 – As an image (PNG, GIF, etc.) – CAS number: 50-78-2 – File format: ChemDraw file, MOL file, etc.
    [Show full text]
  • Free Energy Methods Involving Quantum Physics, Path Integrals, and Virtual Screenings
    Free Energy Methods Involving Quantum Physics, Path Integrals, and Virtual Screenings Development, Implementation and Application in Drug Discovery Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) eingereicht beim Fachbereich Mathematik und Informatik der Freien Universität Berlin vorgelegt von Christoph Gorgulla Berlin 2018 This thesis was created within the graduate programs of the Berlin Mathematical School (BMS) and the International Max Planck Research School for Compu- tational Biology and Scientific Computing (IMPRS-CBSC) of the Max Planck Institute for Molecular Genetics (MPIMG), as well as within the Department of Mathematics and Computer Science and the Department of Physics of the Freie Universität Berlin. Erstgutachter (Betreuer) Prof. Dr. Christof Schütte Freie Universität Berlin Fachbereich Mathematik und Informatik Arnimallee 6 14195 Berlin, Germany Zweitgutachter Priv. Doz. Dr. Konstantin Fackeldey Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 10623 Berlin, Germany Tag der Disputation: 3. Mai 2018 Copyright c 2018 by Christoph Gorgulla To my grandmother, and all the kind, wise, and caring people in the world. Abstract Computational science has the potential to solve most of the problems which pharma- ceutical research is facing these days. In this field the most pivotal property is arguably the free energy of binding. Yet methods to predict this quantity with sufficient accuracy, reliability and efficiency remain elusive, and are thus not yet able to replace experimental determinations, which remains one of the unattained holy grails of computer-aided drug design (CADD). The situation is similar for methods which are used to identify promising new drug candidates with high binding affinity, which resembles a closely related endeavor in this field.
    [Show full text]