Abstract
BackgroundDrug resistance to anti-malarial compounds remains a serious problem, with resistance to newer pharmaceuticals developing at an alarming rate. The development of new anti-malarials remains a priority, and the rational selection of putative targets is a key element of this process. Discovery-2 is an update of the original Discovery in silico resource for the rational selection of putative drug target proteins, enabling researchers to obtain information for a protein which may be useful for the selection of putative drug targets, and to perform advanced filtering of proteins encoded by the malaria genome based on a series of molecular properties.MethodsAn updated in silico resource has been 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 properties used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions. Newly added features include drugability measures from ChEMBL, automated literature relations and links to clinical trial information. Searching by chemical structure is also available.ResultsThe updated functionality of the Discovery-2 resource is presented, together with a detailed case study of the Plasmodium falciparum S-adenosyl-L-homocysteine hydrolase (PfSAHH) protein. A short example of a chemical search with pyrimethamine is also illustrated.ConclusionThe updated Discovery-2 resource allows researchers to obtain detailed properties of proteins from the malaria genome, which may be of interest in the target selection process, and to perform advanced filtering and selection of proteins based on a relevant range of molecular characteristics.
Highlights
Drug resistance to anti-malarial compounds remains a serious problem, with resistance to newer pharmaceuticals developing at an alarming rate
While in-depth studies are ongoing on a relatively small number of selected putative targets for future exploitation, not many resources are available that focus on performing data mining and target identification on the complete malaria genome, in concert with relations to chemical compounds
Recent approaches have illustrated the value of predicting the association of chemical compounds with putative protein drug targets, especially when the targets of compounds such as the GSK dataset with known activity against the parasite may be extrapolated using protein-ligand interaction databases such as ChemProt [8,9]
Summary
Drug resistance to anti-malarial compounds remains a serious problem, with resistance to newer pharmaceuticals developing at an alarming rate. While in-depth studies are ongoing on a relatively small number of selected putative targets for future exploitation, not many resources are available that focus on performing data mining and target identification on the complete malaria genome, in concert with relations to chemical compounds. Recent approaches have illustrated the value of predicting the association of chemical compounds with putative protein drug targets, especially when the targets of compounds such as the GSK dataset with known activity against the parasite may be extrapolated using protein-ligand interaction databases such as ChemProt [8,9]. The Discovery resource attempts to use a similar approach in associating chemical compounds with malaria proteins using sequence homology, and selective chemical similarity searches. The resource contains chemical compounds from the ChEMBL database with chemical search functionality and putative ligand-protein prediction information. Chemical searches may be performed using keywords, SMILES strings or chemical structures
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