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Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria.

Joubert F, Harrison CM, Koegelenberg RJ, Odendaal CJ, de Beer TA - Malar. J. (2009)

Bottom Line: Searching by chemical structure is also available.An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics and Computational Biology Unit, Department of Biochemistry, University of Pretoria, Pretoria, South Africa. fourie.joubert@up.ac.za

ABSTRACT

Background: Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.

Methods: A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.

Results: An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.

Conclusion: The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

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The Discovery summary page, showing the tabbed environment for the different result classes.
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Figure 3: The Discovery summary page, showing the tabbed environment for the different result classes.

Mentions: When performing a simple search from a protein perspective, users may enter a PlasmoDB/Ensembl ID or a keyword. The user is then presented with either the specified protein, or a list of proteins matching the keyword by description or by one of its relationships. This list is separated by species. The user is then able to further explore the hits of interest. More powerful is the advanced search function, where the user may enter search terms in several categories, and select for the presence or absence of specific relations (Figure 2). The results are returned as above. When exploring the data for a selected protein, the user is presented with a tabbed environment with different categories of data (Figure 3). The first tab shows a summary of the protein's GO and EC terms, a graphical summary of the InterPro features and a view of the protein sequence. Next is a view of the orthologs and paralogs predicted by OrthoMCL, together with a Jalview display of the T-Coffee alignment of these sequences. This is followed by the InterPro analysis results of the protein sequence, the position of the protein in the KEGG metabolic pathway maps and tertiary structure information such as ModBase models or BLAST results against PDB. The protein interaction tab provides results of possible protein-protein interactions based on Y2H studies, PlasmoMAP interactions and orthology to DIP and MINT interacting partners. The ligand interaction tab provides predicted ligand interactions based on KEGG annotations and BLAST results against PDB and DrugBank. Host-pathogen interaction predictions are based on orthology to MINT and DIP interacting partners, with species filtering.


Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria.

Joubert F, Harrison CM, Koegelenberg RJ, Odendaal CJ, de Beer TA - Malar. J. (2009)

The Discovery summary page, showing the tabbed environment for the different result classes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2724543&req=5

Figure 3: The Discovery summary page, showing the tabbed environment for the different result classes.
Mentions: When performing a simple search from a protein perspective, users may enter a PlasmoDB/Ensembl ID or a keyword. The user is then presented with either the specified protein, or a list of proteins matching the keyword by description or by one of its relationships. This list is separated by species. The user is then able to further explore the hits of interest. More powerful is the advanced search function, where the user may enter search terms in several categories, and select for the presence or absence of specific relations (Figure 2). The results are returned as above. When exploring the data for a selected protein, the user is presented with a tabbed environment with different categories of data (Figure 3). The first tab shows a summary of the protein's GO and EC terms, a graphical summary of the InterPro features and a view of the protein sequence. Next is a view of the orthologs and paralogs predicted by OrthoMCL, together with a Jalview display of the T-Coffee alignment of these sequences. This is followed by the InterPro analysis results of the protein sequence, the position of the protein in the KEGG metabolic pathway maps and tertiary structure information such as ModBase models or BLAST results against PDB. The protein interaction tab provides results of possible protein-protein interactions based on Y2H studies, PlasmoMAP interactions and orthology to DIP and MINT interacting partners. The ligand interaction tab provides predicted ligand interactions based on KEGG annotations and BLAST results against PDB and DrugBank. Host-pathogen interaction predictions are based on orthology to MINT and DIP interacting partners, with species filtering.

Bottom Line: Searching by chemical structure is also available.An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics and Computational Biology Unit, Department of Biochemistry, University of Pretoria, Pretoria, South Africa. fourie.joubert@up.ac.za

ABSTRACT

Background: Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties.

Methods: A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available.

Results: An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum.

Conclusion: The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

Show MeSH
Related in: MedlinePlus