Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

Abstract

The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

Figure 1

Malaria functional genomics (X-omics) strategies in the context of target and drug characterization. Selected questions that could be addressed by the application of functional genomics are listed, including those specific to the transcriptome, proteome or interactome (X-omes). Highlighted is the particular focus on the application of this type of strategies to drug(s) and target(s) characterization.

Figure 2

Current pipeline for the homology-based modelling of malaria protein 3D-structures. This scheme emphasizes on the currently available methods to overcome amino acid bias, low sequence identity, protein inserts etc. Future upgrades include the refinement of each of these methods, for instance implementing asymmetric substitution matrices discussed in the text, that take into account the different amino acid distributions of malarial and non-malarial proteins for pairwise alignments.

Figure 3

In silico screening for protein ligands based on structural docking. A urea analog inhibiting malaria plasmepsins was identified with good score from the first WISDOM (World-wide In Silico Docking On Malaria) campaign. The WISDOM initiative successfully deployed large scale in silico docking on the European public EGEE grid infrastructure. The ligand shown here docks inside the binding pocket of plasmepsin, and interacts with key protein residues. The developed and established protocols can be used for new targets, and particularly a second computing challenge, WISDOM II.

Figure 4

Malaria chemogenomics: organization and treatment of genomic, post-genomic and chemical information for the prediction and characterization of target and drug candidates. Genomic data from Plasmodium and other species (a), i.e. protein sequences, should be organized based on sequence similarity (b). This projection should allow the high throughput reconstruction of molecular phylogenies both at the intraspecific (connecting paralogs and alleles) and interspecific (connecting homologues among which orthologs) levels following statistically accurate methods e.g. the TULIP method. Another substantial side of the biological space is designed by representing the knowledge of the biological processes, using stable ontologies e.g. the GO, and dynamic graph representation, e.g. PlasmoCyc (c). Versatile tools should allow the integration of genomic data, biological process representations and global functional profiles obtained with diverse X-omic approaches (4). These tools should comply with the large diversity of technologies and mining methods. The collection of information on the biological response to drugs is one of the doors to connect the biological space with the chemical space, following the "reverse chemical genetic" way, i.e. "from known drugs to biological response" (toxicity, mode of action). The other door to connect the chemical space and the biological space follows the "direct chemical genetic" way, i.e. "from known biological target to drug candidates". In addition to malaria protein structures obtained from crystals, the automated structural annotation of the malaria proteome should be initiated with quality scores (e). Based on protein structure information, virtual docking campaigns such as the WISDOM challenges can be achieved using the power of computer grids. The in silico organization of the small molecules stored in chemolibraries (f) follows similar principles, in particular the determination of three-dimensional structures of small molecules (g) and a clustering of small molecular structures based on drug properties and descriptors (h). Sharing and mining of chemogenomic information, completed with knowledge harvested in unstructured scientific literature, would benefit of the advances in knowledge space design and deployment on knowledge grids.