In silico drug discovery on computational grids for finding novel drugs on neglected diseases [Elektronische Ressource] / Vinod Kumar Kasam

Vinod Kumar Kasam

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• Publié par: rheinische_friedrich-wilhelms-universitat_bonn
• Publié le: 01 janvier 2010
• Nombre de lectures: 14
• Langue: English
• Poids de l'ouvrage: 4 Mo

Extrait

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 Mole