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targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.

Raman K, Yeturu K, Chandra N - BMC Syst Biol (2008)

Bottom Line: The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us.Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets.The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Supercomputer Education and Research Centre and Bioinformatics Centre, Indian Institute of Science, Bangalore 560 012, India. karthik@rishi.serc.iisc.ernet.in

ABSTRACT

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

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Related in: MedlinePlus

List of Identified Targets. Distribution of the functional classes of the 451 targets identified in the H-List. The number of targets present in each of the functional classes is also indicated.
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Figure 3: List of Identified Targets. Distribution of the functional classes of the 451 targets identified in the H-List. The number of targets present in each of the functional classes is also indicated.

Mentions: The functional classes of the 451 targets (H-List) identified by this study are indicated in Fig. 3. The list is also available as supplementary material [See Additional file 4]. This list includes several known targets and many that have been proposed as potential targets. Some known targets have been eliminated because they have failed one or more filters in the targetTB pipeline. The passage of known and proposed targets for anti-tubercular drugs in the targetTB pipeline is detailed in Table 3 (also see Additional File 5). Some examples of proteins that are in the H-List include known targets such as InhA, EmbA and FabH, as well as many targets that have been proposed for anti-tubercular drug discovery, such as GlfT2, a bi-functional UDP-galactofuranosyl transferase, the fatty acid synthase Fas, the pantothenate kinase PanK, a glutamine-synthetase adenylyltransferase GlnE and the sensor histidine kinase DevS. The list also indicates several proteins that have been suggested as potential drug targets in literature, but eliminated from the targetTB pipeline on account of failing one or more of the filters.


targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis.

Raman K, Yeturu K, Chandra N - BMC Syst Biol (2008)

List of Identified Targets. Distribution of the functional classes of the 451 targets identified in the H-List. The number of targets present in each of the functional classes is also indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651862&req=5

Figure 3: List of Identified Targets. Distribution of the functional classes of the 451 targets identified in the H-List. The number of targets present in each of the functional classes is also indicated.
Mentions: The functional classes of the 451 targets (H-List) identified by this study are indicated in Fig. 3. The list is also available as supplementary material [See Additional file 4]. This list includes several known targets and many that have been proposed as potential targets. Some known targets have been eliminated because they have failed one or more filters in the targetTB pipeline. The passage of known and proposed targets for anti-tubercular drugs in the targetTB pipeline is detailed in Table 3 (also see Additional File 5). Some examples of proteins that are in the H-List include known targets such as InhA, EmbA and FabH, as well as many targets that have been proposed for anti-tubercular drug discovery, such as GlfT2, a bi-functional UDP-galactofuranosyl transferase, the fatty acid synthase Fas, the pantothenate kinase PanK, a glutamine-synthetase adenylyltransferase GlnE and the sensor histidine kinase DevS. The list also indicates several proteins that have been suggested as potential drug targets in literature, but eliminated from the targetTB pipeline on account of failing one or more of the filters.

Bottom Line: The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us.Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets.The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

View Article: PubMed Central - HTML - PubMed

Affiliation: Supercomputer Education and Research Centre and Bioinformatics Centre, Indian Institute of Science, Bangalore 560 012, India. karthik@rishi.serc.iisc.ernet.in

ABSTRACT

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.

Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.

Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Show MeSH
Related in: MedlinePlus