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Qsar Methods Development, Virtual and Experimental Screening for Cannabinoid Ligand Discovery

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Qsar Methods Development, Virtual and Experimental Screening for Cannabinoid Ligand Discovery QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY by Kyaw Zeyar Myint BS, Biology, BS, Computer Science, Hampden-Sydney College, 2007 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2012 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Kyaw Zeyar Myint It was defended on August 20th, 2012 and approved by Dr. Ivet Bahar, Professor, Department of Computational and Systems Biology Dr. Billy W. Day, Professor, Department of Pharmaceutical Sciences Dr. Christopher Langmead, Associate Professor, Department of Computer Science, CMU Dissertation Advisor: Dr. Xiang-Qun Xie, Professor, Department of Pharmaceutical Sciences ii Copyright © by Kyaw Zeyar Myint 2012 iii QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY Kyaw Zeyar Myint, PhD University of Pittsburgh, 2012 G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side- effects. Therefore, this work was focused on discovering novel CB2 ligands by developing novel quantitative structure-activity relationship (QSAR) methods and performing virtual and experimental screenings. Three novel QSAR methods were developed to predict biological activities and binding affinities of ligands. In the first method, a traditional fragment-based approach was improved by introducing a fragment similarity concept that enhanced the prediction accuracy remarkably. In the second method, pharmacophoric and morphological descriptors were incorporated to derive a novel QSAR regression model with good prediction accuracy. In the third method, a novel fingerprint-based artificial neural network QSAR model iv was developed to overcome the similar scaffold requirement of many fragment-based and other 3D-QSAR methods. These methods provide a foundation for virtual screening and hit ranking of chemical ligands from large chemical space. In addition, several novel CB2 selective ligands within nM binding affinities were discovered. These ligands were proven to be inverse agonists as validated by functional assays and could be useful probes to study CB2 signaling as well as potential drug candidates for autoimmune disesases. v TABLE OF CONTENTS PREFACE ................................................................................................................................... XV 1.0 INTRODUCTION ........................................................................................................ 1 1.1 RECENT ADVANCES IN QSAR METHODS ................................................ 1 1.1.1 Introduction ................................................................................................... 1 1.1.2 Fragment-based 2D-QSAR methods ........................................................... 5 1.1.3 3D-QSAR ..................................................................................................... 11 1.1.4 Comparison of 2D or fragment-based QSAR versus 3D or nD-QSAR methods 22 1.2 VIRTUAL SCREENING APPROACHES...................................................... 25 1.2.1 Structure-based method ............................................................................. 25 1.2.2 Ligand-based method ................................................................................. 26 1.3 CANNABINOID RECEPTORS AND THEIR LIGANDS ............................ 32 1.3.1 Background and significance ..................................................................... 32 1.3.2 Computational design of CB1 and CB2 receptors ................................... 33 1.3.3 Computational design and screening of cannabinoid ligands ................ 37 1.4 OUTLINE OF THE DISSERTATION ............................................................ 41 2.0 FRAGMENT-SIMILARITY-BASED QSAR (FS-QSAR) ALGORITHM FOR LIGAND BIOLOGICAL ACTIVITY PREDICTIONS ......................................................... 44 vi 2.1 INTRODUCTION ............................................................................................. 44 2.2 METHODS ......................................................................................................... 46 2.2.1 Data sets ....................................................................................................... 46 2.2.2 Computational method ............................................................................... 46 2.3 CALCULATIONS ............................................................................................. 50 2.3.1 Partial charge calculation and fragment generation ............................... 50 2.3.2 Parameter tuning using leave-one-out cross-validation (LOOCV) ........ 51 2.3.3 Generation of training and testing data sets............................................. 52 2.4 RESULTS AND DISCUSSION ........................................................................ 52 2.4.1 FS-QSAR modeling on COX2 inhibitor analogs...................................... 52 2.4.2 FS-QSAR modeling on bis-sulfone analogs .............................................. 53 2.4.3 BCUT-similarity score analysis ................................................................. 53 2.4.4 Comparisons with different approaches ................................................... 55 2.5 CONCLUSION .................................................................................................. 58 3.0 NEW QSAR PREDICTION MODELS DERIVED FROM GPCR CB2- ANTAGONISTIC TRIARYL BIS-SULFONE ANALOGS BY A COMBINED MOLECULAR MORPHOLOGICAL AND PHARMACOPHORIC APPROACH ........... 73 3.1 INTRODUCTION ............................................................................................. 73 3.2 METHODS ......................................................................................................... 75 3.2.1 Pharmacophore-based molecular similarity calculation ......................... 75 3.2.2 Morphology-based molecular similarity calculation ............................... 76 3.2.3 QSAR model generation ............................................................................. 77 3.3 RESULTS AND DISCUSSION ........................................................................ 79 vii 3.3.1 Generation of the pharmacophore model and score of the pharmacophoric match .............................................................................................. 79 3.3.2 Generation of hypermolecular alignment and scoring of shape-based molecular similarity ................................................................................................... 82 3.3.3 Development of the PharmShape algorithm based on the QSAR prediction model ......................................................................................................... 84 3.4 CONCLUSION .................................................................................................. 89 4.0 FINGERPRINT-BASED ARTIFICIAL NEURAL NETWORKS QSAR (FANN- QSAR) FOR LIGAND BIOLOGICAL ACTIVITY PREDICTIONS ................................. 105 4.1 INTRODUCTION ........................................................................................... 105 4.2 METHODS ....................................................................................................... 106 4.2.1 Data sets ..................................................................................................... 106 4.2.2 Fingerprint generation ............................................................................. 110 4.2.3 Fingerprint-based Artificial Neural Network QSAR ............................ 110 4.2.4 Radioligand competition binding assay .................................................. 112 4.3 RESULTS AND DISCUSSION ...................................................................... 113 4.3.1 Comparisons with other 3D and 2D-QSAR methods ............................ 114 4.3.2 Prediction of Cannabinoid receptor binding activity using FANN-QSAR method……………………………………………………...……………………….117 4.3.3 Generalization ability of FANN-QSAR method on newly reported cannabinoid ligands ................................................................................................. 120 4.3.4 An application of FANN-QSAR: virtual screening of the NCI compound database ……………………………………………………………………………..123 viii 4.4 CONCLUSION ................................................................................................ 127 5.0 MOLECULAR MODELING, DISCOVERY AND QSAR STUDY OF NOVEL CANNABINOID LIGANDS ..................................................................................... 129 5.1 INTRODUCTION ..........................................................................................
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