In Silico Drug Discovery on Computational Grids for Finding Novel Drugs Against Neglected Diseases
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In silico drug discovery on computational Grids for finding novel drugs against neglected diseases Dissertation zur Erlangung des Doktorgrades (Dr. rer. nat.) der Mathematisch-Naturwissenschaftlichen Fakultat der Rheinischen Friedrich-Wilhelms-Universitat Bonn vorgelegt von Vinod Kumar Kasam Aus Warangal, Indien Bonn September 2009 Angefertigt mit Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn. 1. Referent: Univ.-Prof. Dr. Martin Hofmann-Apitius 2. Referent: Univ.-Prof. Dr. Christa Mueller Tag der Promotion: 30.04.2010 Diese Dissertation ist auf dem Hochschulschriftenserver der ULB Bonn unter http://hss.ulb.uni-bonn.de verfügbar. Erscheinungsjahr: 2010 For my Family: My Wife and Son Abstract Abstract Malaria is a dreadful disease affecting 300 million people and killing 1-1.5 million people every year. Malaria is caused by a protozoan parasite, belonging to the genus Plasmodium. There are several species of Plasmodium infecting cattle, birds, and humans. The four species P.falciparum, P.vivax, P.malariae and P.ovale are in particular considered important, as these species infect humans. One of the main causes for the comeback of malaria is that the most widely used drug against malaria, chloroquine, has been rendered useless by drug resistance in much of the world. New antimalarial drugs are presently available but the potential emergence of resistance, the difficulty to synthesize these drugs at a large-scale and their cost make it of utmost importance to keep searching for new drugs. Despite continuous efforts of the international community to reduce the impact of malaria on developing countries, no significant progress has been made in the recent years and the discovery of new drugs is more than ever needed. Out of the many proteins involved in the metabolic activities of the Plasmodium parasite, some are promising targets to carry out rational drug discovery. In silico drug design, especially vHTS is a widely and well-accepted technology in lead identification and lead optimization. This approach, therefore builds upon the progress made in computational chemistry to achieve more accurate in silico docking and in information technology to design and operate large-scale Grid infrastructures. One potential limitation of structure-based methods, such as molecular docking and molecular dynamics is that; both are computational intensive tasks. Recent years have witnessed the emergence of Grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations such as docking and molecular dynamics. The current thesis is a part of WISDOM project, which stands for Wide In silico Docking on Malaria. This thesis describes the rational drug discovery activity at large-scale, especially molecular docking and molecular dynamics on computational Grids in finding hits against four different targets (PfPlasmepsin, PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. The first attempt at using Grids for large-scale virtual screening (combination of molecular docking and molecular dynamics) focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. The combination of docking and molecular dynamics simulations, followed by rescoring using sophisticated scoring functions resulted in the identification of 26 novel sub- Abstract micromolar inhibitors. The inhibitors are further clustered into five different scaffolds. While two scaffolds, diphenyl urea, and thiourea analogues are already known as plasmepsin inhibitors, albeit the compounds identified here are different from the existing ones, with the new class of potential inhibitors, the guanidino group of compounds, we have established a new class of chemical entities with inhibitory activity against Plasmodium falciparum plasmepsins. Following the success achieved on plasmepsin, a second drug finding effort was performed, focussed on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase. Modeling results are very promising and based on these in silico results, in vitro tests are in progress. Thus, with the work presented here, we not only demonstrate the relevance of computational grids in drug discovery, but also identify several promising small molecules (success achieved on P. falciparum plasmepsins). With the use of the EGEE infrastructure for the virtual screening campaign against the malaria-causing parasite P. falciparum, we have demonstrated that resource sharing on an e-Science infrastructure such as EGEE provides a new model for doing collaborative research to fight diseases of the poor. Through WISDOM project, we propose a Grid-enabled virtual screening approach, to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world. Acknowledgements Acknowledgements I am grateful to numerous local and global persons who have contributed towards my thesis. Firstly, I thank Prof. Dr. Martin Hofmann-Apitius for giving me an opportunity to do my PhD thesis at Fraunhofer-SCAI, Germany. His encouragement always motivated me to focus beyond my work. As my supervisor, he has constantly motivated me to remain focused on achieving my goal. I am thankful to Prof. Dr. Christa Mueller for her readiness to be co- supervisor on the thesis. I am very grateful to Dr. Vincent Breton, LPC, IN2P3-CNRS, Clermont-Ferrand France for his guidance, support and providing me a chance to work in his lab, without which this thesis would have not been possible. I want to thank Prof. Giulio Rasteli, University of Modena, Italy for his guidance and training on the molecular dynamics approach. I thank Prof. Doman Kim, University of South Korea, for kindly performing the in vitro tests. At the outset, I would like to express my special thanks and regards to Jean Salzemann, Marc Zimmermann, Astrid Maass, Antje Wolf and Mohammed Shahid for their help and scientific discussions. My special thanks to Ana Da Costa and Nicolas Jacq. I sincerely feel that working together with them was beneficial for my successful completion of the thesis. I thank all my colleagues at Fraunhofer-SCAI and LPC, IN2P3-CNRS for their immense support and co-operation during my thesis work. My very special thanks to all the people involved in WISDOM collaboration. List of Abbreviations List of Abbreviations Plm Plasmespin MD Molecular Dynamics MOE Molecular Operating Environment vHTS Virtual High Throughput Screening HTS High Throughput Screening DHFR Dihydrofolate Reductase RMSD Root Mean Square Deviation EGEE European Grid Enabling E-science GST Glutathione-S-Trasferase MM-PBSA Molecular Mechanics Poisson Boltzmann Surface Area MM-GBSA Molecular Mechanics Generalized Born Surface Area NCE New Chemical Entity ADME Absorption, Distribution, Metabolism, Elimination Contents Contents 1 Chapter1. Introduction .................................................................................................1 1.1 Malaria .....................................................................................................................3 1.1.1 Complex life cycle of malaria ............................................................................4 1.1.2 Current drugs ....................................................................................................7 1.1.3 Motivation ....................................................................................................... 11 1.2 Thesis outline ......................................................................................................... 15 2 Chapter 2. State of the art on rational drug design ................................................... 17 2.1 Drug discovery ....................................................................................................... 17 2.2 Virtual screening .................................................................................................... 22 2.3 Molecular docking .................................................................................................. 27 2.3.1 Search methods and docking algorithms .......................................................... 28 2.3.2 Scoring functions ............................................................................................ 31 2.4 Molecular dynamics ............................................................................................... 35 2.5 Combination of docking and molecular dynamics methods ..................................... 40 2.6 Summary ................................................................................................................ 41 3 Chapter 3. Deployment of molecular docking and molecular dynamics on EGEE Grid infrastructure ............................................................................................................. 43 3.1 Introduction ............................................................................................................ 43 3.1.1 Concept of e-Science ....................................................................................... 43 3.1.2 Computational Grid ......................................................................................... 44 3.1.3 Classification of Grids ..................................................................................... 47 3.1.4 Service oriented architecture and web services ...............................................