Characterizing the pocketome of Mycobacterium tuberculosis and application in rationalizing polypharmacological target selection

Chandra, Nagasuma

doi:10.1038/srep06356

Cited by 24 publications

(21 citation statements)

References 69 publications

(99 reference statements)

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“…Among our top scoring targets, many proteins broadly mentioned in the literature as signaling proteins (pnkB, pnkG, DevS) or related to mycolic acid synthesis (IhnA, pcaA, pks13, fas, fad32D), panthenoate biosynthesis (panB) and Cytochromes (cyp 121 and cyp125) have already been highlighted by Chandra's Lab and TDR Targets [75,25,85] as potential drug targets (shown in Table 4). Thus, as expected, there are some common ussual findings.…”

Section: What's Novel In Tb Targets?mentioning

confidence: 99%

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

et al. 2016

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Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect". Based on this idea, we performed an Mtb metabolic network whole proteome analysis of potential RNOS sensitive and relevant targets which includes target druggability and essentiality criteria. Our results, available at http://tuberq.proteinq.com.ar yield new potential TB targets, like I3PS, while also providing and updated view of previous proposals becoming an important tool for researchers looking for new ways of killing TB.

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Section: What's Novel In Tb Targets?mentioning

confidence: 99%

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

et al. 2016

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“…On that account, a diverse dataset of small, drug-like molecules bound to high-quality models with accurately annotated pockets provides an invaluable resource for drug repositioning employing sequence order-independent pocket matching algorithms [ 40 – 43 ]. It is noteworthy that computational drug repurposing has suggested new opportunities to combat tuberculosis [ 44 , 45 ], malaria [ 46 ], and rare diseases [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

2018

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BackgroundThe structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future.ResultsWe created eModel-BDB, a database of 200,005 comparative models of drug-bound proteins based on 1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands.ConclusionsFreely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination.

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“…The best-scoring pose of the cluster with the highest median score was selected and submitted to the PLIP web-server in order to analyse tuberculosis. Previous studies (46,47) have shown that this pathway is particularly exploited by Pseudomonas spp., which is able to use chlorinated compounds as sole carbon sources for nutrition, and this accounts for the prevalence of Pseudomonas in recreational waters.…”

Section: Psi-blast Of Mtbmentioning

confidence: 97%

“…The hotspots (43) and cavities (46) for ArgD were used to explore potential binding of two drugs, Amprenavir and Cochicine, to the ArgD protein and the putative interactions were investigated using a molecular docking approach. The structures of both ligands and of the pyridoxal 5' phosphate (PLP) cofactor were retrieved from Drugbank in the simplified molecular-input line-entry system (SMILES) format (47).…”

Section: Psi-blast Of Mtbmentioning

confidence: 99%

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

Bannerman

Vedithi

Júlvez

et al. 2019

Preprint

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Full Title: Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus Abstract Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance.Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

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Characterizing the pocketome of Mycobacterium tuberculosis and application in rationalizing polypharmacological target selection

Cited by 24 publications

References 69 publications

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

Analysis of metabolic pathways in mycobacteria to aid drug-target identification

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