WISDOM-II: Screening against multiple targets implicated in malaria using computational grid infrastructures

BackgroundDespite 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.MotivationRecent years have witnessed the emergence of grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations like docking. In 2005, a first attempt at using grids for large-scale virtual screening focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. Following this success, a second deployment took place in the fall of 2006 focussing on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase.MethodsIn 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.ResultsOn the computational side, a sustained infrastructure has been developed: docking at large scale, using different strategies in result analysis, storing of the results on the fly into MySQL databases and application of molecular dynamics refinement are MM-PBSA and MM-GBSA rescoring. The modeling results obtained are very promising. Based on the modeling results, In vitro results are underway for all the targets against which screening is performed.ConclusionThe current paper describes the rational drug discovery activity at large scale, especially molecular docking using FlexX software on computational grids in finding hits against three different targets (PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. Grid-enabled virtual screening approach is proposed to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world.

[1]  Josie Arnold,et al.  The virtual laboratory , 1996 .

[2]  Yongyuth Yuthavong,et al.  Crystal structure of dihydrofolate reductase from Plasmodium vivax: pyrimethamine displacement linked with mutation-induced resistance. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  E. Liebau,et al.  The glutathione S-transferase from Plasmodium falciparum. , 2002, Molecular and biochemical parasitology.

[4]  N. White,et al.  Association of Genetic Mutations inPlasmodium vivax dhfr with Resistance to Sulfadoxine-Pyrimethamine: Geographical and Clinical Correlates , 2001, Antimicrobial Agents and Chemotherapy.

[5]  Ogobara K. Doumbo,et al.  The pathogenic basis of malaria , 2002, Nature.

[6]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[7]  D. Platel,et al.  Plasmodium berghei: implication of intracellular glutathione and its related enzyme in chloroquine resistance in vivo. , 1995, Experimental parasitology.

[8]  Structure, function and evolution of glutathione transferases: implications for classification of non-mammalian members of an ancient enzyme superfamily. , 2001 .

[9]  G. Rastelli,et al.  Structure of Plasmodium vivax dihydrofolate reductase determined by homology modeling and molecular dynamics refinement. , 2003, Bioorganic & medicinal chemistry letters.

[10]  P. Trigg.  Research and training in tropical diseases , 1979 .

[11]  John A Tainer,et al.  Characterization of the electrophile binding site and substrate binding mode of the 26‐kDa glutathione S‐transferase from Schistosoma japonicum , 2003, Proteins.

[12]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[13]  David Abramson,et al.  The Virtual Laboratory: a toolset to enable distributed molecular modelling for drug design on the World‐Wide Grid , 2003, Concurr. Comput. Pract. Exp..

[14]  R. W. Burgess,et al.  Pyrimethamine resistance in Plasmodium vivax malaria. , 1959, Bulletin of the World Health Organization.

[15]  T Lengauer,et al.  The particle concept: placing discrete water molecules during protein‐ligand docking predictions , 1999, Proteins.

[16]  Yongyuth Yuthavong,et al.  Insights into antifolate resistance from malarial DHFR-TS structures , 2003, Nature Structural Biology.

[17]  J. Baird,et al.  Chloroquine Resistance in Plasmodium vivax , 2004, Antimicrobial Agents and Chemotherapy.

[18]  S. Müller.  Redox and antioxidant systems of the malaria parasite Plasmodium falciparum , 2004, Molecular microbiology.

[19]  Martin Hofmann-Apitius,et al.  Design of New Plasmepsin Inhibitors: A Virtual High Throughput Screening Approach on the EGEE Grid , 2007, J. Chem. Inf. Model..

[20]  Gisbert Schneider,et al.  Virtual Screening for Bioactive Molecules: Böhm/Virtual , 2008 .

[21]  Vincent Breton,et al.  Large Scale Deployment of Molecular Docking Application on Computational Grid infrastructures for Combating Malaria , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[22]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[23]  Hans-Joachim Böhm,et al.  A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.

[24]  Y Thebtaranonth,et al.  Interaction of pyrimethamine, cycloguanil, WR99210 and their analogues with Plasmodium falciparum dihydrofolate reductase: structural basis of antifolate resistance. , 2000, Bioorganic & medicinal chemistry.

[25]  W. Kabsch,et al.  X-ray structure of glutathione S-transferase from the malarial parasite Plasmodium falciparum , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[26]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[27]  Marc Zimmermann,et al.  Demonstration of In Silico Docking at a Large Scale on Grid Infrastructure , 2006, HealthGrid.

[28]  M. Perbandt,et al.  Native and Inhibited Structure of a Mu class-related Glutathione S-transferase from Plasmodium falciparum* , 2004, Journal of Biological Chemistry.

[29]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[30]  P. Kollman,et al.  Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. , 2000, Accounts of chemical research.

[31]  W. Sirawaraporn,et al.  Antifolate-resistant mutants of Plasmodium falciparum dihydrofolate reductase. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[32]  C. Ahlm,et al.  Chloroquine-Resistant Plasmodium vivax Malaria in Borneo. , 1996, Journal of travel medicine.

[33]  C. Kesselman,et al.  Computational Grids , 1998, VECPAR.

[34]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[35]  Vincent Breton,et al.  Design and Discovery of Plasmepsin II Inhibitors Using an Automated Workflow on Large‐Scale Grids , 2009, ChemMedChem.

[36]  V. C. Pandey,et al.  Glutathione‐S‐transferase activity in malarial parasites , 1999, Tropical medicine & international health : TM & IH.

[37]  A. Ferrari,et al.  Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. , 2007, Bioorganic & medicinal chemistry.

[38]  G. Degliesposti,et al.  Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening , 2009, Chemical biology & drug design.

[39]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

[40]  G. Schneider,et al.  Virtual Screening for Bioactive Molecules , 2000 .

[41]  John M. Barnard,et al.  Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..

[42]  K. Becker,et al.  Oxidative stress in malaria parasite-infected erythrocytes: host-parasite interactions. , 2004, International journal for parasitology.

[43]  W. Graham Richards,et al.  Virtual screening using grid computing: the screensaver project , 2002, Nature Reviews Drug Discovery.

[44]  Araz Jakalian,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method , 2000 .

[45]  R. Spencer,et al.  High-throughput screening of historic collections: observations on file size, biological targets, and file diversity. , 1998, Biotechnology and bioengineering.

[46]  S Muller.  The redox and antioxidant systems of Plasmodium falciparum , 2004 .

[47]  P. Kollman,et al.  Continuum Solvent Studies of the Stability of DNA, RNA, and Phosphoramidate−DNA Helices , 1998 .

[48]  Johan Montagnat,et al.  Grid-enabled Virtual Screening Against Malaria , 2006, Journal of Grid Computing.

[49]  H O Villar,et al.  Ligand‐based protein alignment and isozyme specificity of glutathione S‐transferase inhibitors , 1997, Proteins.

[50]  Yongyuth Yuthavong,et al.  Basis for antifolate action and resistance in malaria. , 2002, Microbes and infection.