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Computer-Aided Design of Novel Antithrombotic Agents

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Computer-Aided Design of Novel Antithrombotic Agents UNIVERSITÉ DE STRASBOURG en cotutelle avec INSTITUT BOGATSKY DE CHIMIE PHYSIQUE, ODESSA, UKRAINE ÉCOLE DOCTORALE DES SCIENCES CHIMIQUE UMR 7140 Unistra/CNRS THÈSE présentée par Tetiana KHRISTOVA soutenue le 15 novembre 2013 pour obtenir le grade de Docteur de l’Université de Strasbourg Discipline: Chimie Spécialité: Chemoinformatique Computer-aided design of novel antithrombotic agents Directeurs de thèse: M.VARNEK Alexandre Professeur, Université de Strasbourg M. KUZMIN Victor Professeur, Institut Bogatsky de Chimie Physique, Odessa, Ukraine Rapporteurs: M. VILLOUTREIX Bruno Docteur, Université Paris-Diderot M. TETKO Igor Docteur, Institut of Structural Biology, Helmholtz Zentrum München, Neuherberg, Allemagne Examinateurs: M. STOTE Roland Docteur, Université de Strasbourg M. LANGER Thierry Professeur, Prestwick Chemical, Strasbourg Ms Tetiana Khristova was a member of the European Doctoral College of the University of Strasbourg during the preparation of her PhD, from 2010 to 2013, class name Jane Goodall. She has benefited from specific financial supports offered by the College and, along with her mainstream research, has followed a special course on topics of general European interests presented by international experts. This PhD research project has been led with the collaboration of two universities: the A.V.Bogatsky Physico-Chemical Institute, Ukraine and the University of Strasbourg, France. 2 Acknowledgment I would like to thanks French Embassy in Ukraine for the PhD fellowship. As well I want to express my gratitude to my supervisors Professor Alexandre Varnek and Professor Victor Kuz‟min for the opportunity to participate in this exciting French-Ukrainian project. Without their guidance and persistent help this PhD thesis would not have been possible. Then I would like to express my gratitude to Dr. Bruno Villoutreix, Dr. Igor Tetko, Dr. Roland Stote and Pr. Thierry Langer for their time and effort in reviewing this work. Furthermore I would like to thank Dr. Pavel Polishchuk without whose help and moral support this work would have never been finished successfully. I would like to thanks my colleagues at the laboratory Dr. Dragos Horvath, Dr. Gilles Marcou, Dr. Olga Klimchuk, Dr. Vladimir Chupakhin. I am grateful for their constant support and help. I cannot find words to express my gratitude to Dr. Ioana Oprisiu who have supported me throughout entire process. She was always there for me when I needed her. I also want to thank some of my fellow PhD students: Laurent Hoffer, Christophe Muller, Fiorella Ruggiu, Héléna Gaspar. They each helped to make my time in the PhD program more fun and interesting. I am deeply and forever indebted to my parents and friends for their love and support. Thank you for believing in me. 3 Abbreviation ACS : Acute coronary syndrome AD : Applicability domain ADP : Adenosine diphosphate AUC : Area under ROC curve BA : Balanced Accuracy CoMFA : Comparative Molecular Field Analysis FPT : ISIDA fuzzy pH-dependent pharmacophore triplets ISIDA : In Silico Design and Data Analysis MIDAS Metal Ion–Dependent Adhesion Site MOE : Molecular Operating Environment PASS : Prediction of Activity Spectra for Substances PLANTS : Protein-Ligand ANT System QSAR : Quantitative structure–activity relationship RF : Random forest method RMSD : Root-mean-square deviation RMSE : Root mean-squared error ROC : Receiver Operating Characteristic ROCS : Rapid Overlay of Chemical Structures SiRMS : Simplex representation of molecular structure SMF : Substructure molecular fragments TF : Tissue factor TP : Thromboxane A2 receptor TXA2 : Thromboxane A2 vWF : von Willebrand factor XED : eXtended Electron Distribution force field 4 Contents Introduction ................................................................................................................................. 8 Chapter 1. Bibliographic overview ........................................................................................... 10 1. Mechanism of thrombosis ..................................................................................................... 11 2. Known agents for antithrombotic therapy ............................................................................ 18 Chapter 2. Used methodologies and their applications ............................................................. 23 3. Virtual screening funnel ........................................................................................................ 24 3.1. QSAR approach ................................................................................................................. 25 3.1.1. Molecular descriptors ..................................................................................................... 25 3.1.1.1. Simplex representation of molecular structure ............................................................ 25 3.1.1.2. ISIDA substructure molecular fragments .................................................................... 26 3.1.1.3. ISIDA fuzzy pH-dependent pharmacophore triplets ................................................... 27 3.1.2. Machine-learning methods ............................................................................................. 28 3.1.2.1. Random Forest ............................................................................................................. 28 3.1.2.2. Applicability domain ................................................................................................... 29 3.1.2.3. Criteria for estimation of the performance of QSAR models ...................................... 29 3.1.2.3.1. Regression models .................................................................................................... 29 3.1.2.3.2. Classification models ................................................................................................ 30 3.1.2.3.3. The ROC curve (Receiving Operating Characteristics) ........................................... 31 3.1.2.4. Validation of QSAR models ........................................................................................ 31 3.1.3. ADME/Tox assessments of novel compounds ............................................................... 32 3.1.4. PASS ............................................................................................................................... 32 3.2. Molecular shape-similarity ................................................................................................ 33 3.2.1. ROCS. Rapid Overlay of Chemical Structures ............................................................... 33 3.3. Molecular field similarity .................................................................................................. 34 3.3.1. Cresset FieldAlign approach ........................................................................................... 35 3.4. Pharmacophore modeling .................................................................................................. 37 3.4.1. LigandScout .................................................................................................................... 38 3.5. Omega ................................................................................................................................ 40 3.6. Docking .............................................................................................................................. 41 3.6.1. MOE, FlexX, PLANTS .................................................................................................. 42 Chapter 3. RGD-peptidomimetics – antagonists of integrin αIIbβ3receptor .............................. 46 4. Previously reported models .................................................................................................. 47 5 4.1. Pharmacophore models ...................................................................................................... 47 4.2. QSAR modeling ................................................................................................................. 50 4.3. Molecular docking ............................................................................................................. 54 4.4. Conclusions ........................................................................................................................ 59 5. Computer-aided design of new RGD-peptidomimetics ........................................................ 60 5.1. Dataset description ............................................................................................................. 60 5.2. QSAR modeling ................................................................................................................. 63 5.3. Pharmacophore models ...................................................................................................... 67 5.3.1. Structure-based models ................................................................................................... 68 5.3.2. Ligand-based models ...................................................................................................... 69 5.4. Docking .............................................................................................................................. 72 5.4.1. Choice of the docking software and binding site preparation......................................... 72 5.4.2. Docking experiments on selected subsets of RGD peptidomimetics. ............................ 76 5.5. Molecular shape-based comparison ..................................................................................
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