Publications (7) View all
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Article: Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis
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ABSTRACT: A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.PLoS ONE 07/2012; · 4.09 Impact Factor -
Article: Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis
[show abstract] [hide abstract]
ABSTRACT: A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.PLoS ONE 07/2012; 7(7):e39808. · 4.09 Impact Factor -
Article: Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.
[show abstract] [hide abstract]
ABSTRACT: A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.PLoS ONE 01/2012; 7(7):e39808. · 4.09 Impact Factor -
Article: Location of disorder in coiled coil proteins is influenced by its biological role and subcellular localization: a GO-based study on human proteome.
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ABSTRACT: Intrinsic disorder in proteins has been explored to study lack of structure-function aspects of many proteins. The current study focuses on coiled coils which are often linked to intrinsic disorder. We present a sequence level analysis of human coiled coils to find out if this is universally true for all coiled coils. When annotated coiled-coil regions were collected from UniProt and investigated with disorder prediction tools namely-IUPred and DISpro, three patterns were commonly observed-disordered coiled coils (DisCCs), ordered coiled coils (OCCs) and the last one having a disordered region outside the coiled-coil region (DOCCs). Differential enrichment in the gene ontology was seen in these three categories. We found that OCCs are enriched in structural components of the extracellular space including the fibrinogen complex and laminin complex. On the contrary, DisCCs were found to be exclusively over-represented in proteins involved in actin filament, lamellipodium, cell junction, macromolecule complexes, ciliary rootlet and nucleolus. DOCCs are found to be associated with many regulatory and adaptor functions including positive regulation of calcium ion transport via store-operated calcium channel activity, cytoskeletal adaptor activity etc. Other than the GO-based analysis, sequence level analysis showed that disordered coiled-coil regions bear a high proportion of low-complexity regions as compared to ordered coiled coils. The former also has a higher probability of forming a dimer as compared to the ordered counterpart. Our study shows that the in silico approach of mapping of disorder in or around coiled coils in other biological systems or organisms can be applied to understand and rationalize the mode of action of these dynamic motifs.Molecular BioSystems 01/2012; 8(1):346-52. · 3.53 Impact Factor -
Article: A web server for predicting inhibitors against bacterial target GlmU protein.
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ABSTRACT: The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design. This study describes the models developed for predicting inhibitory activity (IC50) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r2) between actual and predicted pIC50. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r2). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r2) between predicted and actual pIC50. It was observed that some molecular descriptors used in this study had high correlation with pIC50. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis. These results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC50) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU.BMC Pharmacology 01/2011; 11:5.
