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

Kasam V, Salzemann J, Botha M, Dacosta A, Degliesposti G, Isea R, Kim D, Maass A, Kenyon C, Rastelli G, Hofmann-Apitius M, Breton V - Malar. J. (2009)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany. kasam@scai.fraunhofer.de

ABSTRACT

Background: 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.

Motivation: Recent 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.

Methods: 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.

Results: On 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.

Conclusion: The 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.

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Related in: MedlinePlus

PfGST-compound hydrogen bonding interaction. Displays PfGST-compound hydrogen bonding interaction. Interaction informations are displayed for the best compounds which have comparable hydrogen bonding pattern like that of co-crystallized ligand, a.GTX (See table 6 for summary of interactions for best compounds).
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Figure 8: PfGST-compound hydrogen bonding interaction. Displays PfGST-compound hydrogen bonding interaction. Interaction informations are displayed for the best compounds which have comparable hydrogen bonding pattern like that of co-crystallized ligand, a.GTX (See table 6 for summary of interactions for best compounds).

Mentions: To understand the interactions between PfGST and final hits, the ligand plots for each complex (PfGST and the compound) were generated and further visualized manually. Protein ligand interactions are studied in three dimensions and for clarity in displaying they are depicted as 2D interaction diagrams. These interactions presented here are generated using the ligand plot module of MOE software. It is evident from Figure 8 that inhibitors are located in the center of the active site, and are stabilized by hydrogen bonding interactions. The hydrogen bonding information along with their distances is listed in Table 6. Figure 8 displays the binding modes of the five best compounds in the active site of the PfGST_a chain. To allow the comparison of binding mode of the compounds and co-crystallized ligand, ligand plot and interactions information is generated for GTX (Cocrystallized ligand of PfGST). It is obvious from Table 6 and Figure 8 that the compounds listed here possess comparable binding poses and patterns. Especially compounds ZINC03533756, ZINC03830430, ZINC03580546, ZINC02305869 generated interaction patterns very similar to the one observed with GTX; making hydrogen bonding to Val59 and Ser72 with backbone as well as with side chains of the amino acids.


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

Kasam V, Salzemann J, Botha M, Dacosta A, Degliesposti G, Isea R, Kim D, Maass A, Kenyon C, Rastelli G, Hofmann-Apitius M, Breton V - Malar. J. (2009)

PfGST-compound hydrogen bonding interaction. Displays PfGST-compound hydrogen bonding interaction. Interaction informations are displayed for the best compounds which have comparable hydrogen bonding pattern like that of co-crystallized ligand, a.GTX (See table 6 for summary of interactions for best compounds).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2691744&req=5

Figure 8: PfGST-compound hydrogen bonding interaction. Displays PfGST-compound hydrogen bonding interaction. Interaction informations are displayed for the best compounds which have comparable hydrogen bonding pattern like that of co-crystallized ligand, a.GTX (See table 6 for summary of interactions for best compounds).
Mentions: To understand the interactions between PfGST and final hits, the ligand plots for each complex (PfGST and the compound) were generated and further visualized manually. Protein ligand interactions are studied in three dimensions and for clarity in displaying they are depicted as 2D interaction diagrams. These interactions presented here are generated using the ligand plot module of MOE software. It is evident from Figure 8 that inhibitors are located in the center of the active site, and are stabilized by hydrogen bonding interactions. The hydrogen bonding information along with their distances is listed in Table 6. Figure 8 displays the binding modes of the five best compounds in the active site of the PfGST_a chain. To allow the comparison of binding mode of the compounds and co-crystallized ligand, ligand plot and interactions information is generated for GTX (Cocrystallized ligand of PfGST). It is obvious from Table 6 and Figure 8 that the compounds listed here possess comparable binding poses and patterns. Especially compounds ZINC03533756, ZINC03830430, ZINC03580546, ZINC02305869 generated interaction patterns very similar to the one observed with GTX; making hydrogen bonding to Val59 and Ser72 with backbone as well as with side chains of the amino acids.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754 Sankt Augustin, Germany. kasam@scai.fraunhofer.de

ABSTRACT

Background: 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.

Motivation: Recent 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.

Methods: 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.

Results: On 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.

Conclusion: The 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.

Show MeSH
Related in: MedlinePlus