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DNA polymerase III ? subunit from Mycobacterium tuberculosis H37Rv: Homology modeling and molecular docking of its inhibitor.

ABSTRACT: UNLABELLED:The alpha subunit of Mycobacterial DNA polymerase III holo enzyme catalyzes the polymerization of both DNA strands. The present investigation reports three dimensional (3-D) structure model of DNA polymerase III ? subunit of Mycobacterium tuberculosis H37Rv (MtbDnaE1) generated using homology modeling with the backbone structure of DNA polymerase III ? of Thermus aquaticus as a template. The model was evaluated at various structure verification servers, which assess the stereo chemical parameters of the residues in the model, as well as structural and functional domains. Comparative analysis of MtbDnaE1 structure reveals the structure of its catalytic domain to be unrelated to that of the human. Successful docking of known inhibitor of bacterial DNA polymerases, 251D onto the modeled MtbDnaE1 was also performed. Therefore, the structure model of MtbDnaE1, a potential anti-mycobacterial target, opens a new avenue for structure-based drug designing against the pathogen. ABBREVIATIONS:aa - amino acid(s), PolIII? - DNA polymerase III alpha subunit, Taq Pol III? - Pol III? of Thermus aquaticus, MtbDnaE1 - PolIII? of Mycobacterium tuberculosis.

SUBMITTER: Chhabra G

PROVIDER: S-EPMC3082860 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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DNA polymerase III α subunit from Mycobacterium tuberculosis H37Rv: Homology modeling and molecular docking of its inhibitor.

Chhabra Gagan G Dixit Aparna A C Garg Lalit L

Bioinformation 20110326 2

<h4>Unlabelled</h4>The alpha subunit of Mycobacterial DNA polymerase III holo enzyme catalyzes the polymerization of both DNA strands. The present investigation reports three dimensional (3-D) structure model of DNA polymerase III α subunit of Mycobacterium tuberculosis H37Rv (MtbDnaE1) generated using homology modeling with the backbone structure of DNA polymerase III α of Thermus aquaticus as a template. The model was evaluated at various structure verification servers, which assess the stereo ...[more]

PMID: 21544168

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Project description:The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.

Project description:Computational prediction and protein structure modeling have come to the aid of various biological problems in determining the structure of proteins. These technologies have revolutionized the biological world of research, allowing scientists and researchers to gain insights into their biological questions and design experimental research much more efficiently. Pathogenic Mycobacterium spp. is known to stay alive within the macrophages of its host. Mycobacterium tuberculosis is an acid-fast bacterium that is the most common cause of tuberculosis and is considered to be the main cause of resistance of tuberculosis as a leading health issue. The genome of Mycobacterium tuberculosis contains more than 4,000 genes, of which the majority are of unknown function. An attempt has been made to computationally model and dock one of its proteins, Rv1250 (MTV006.22), which is considered as an apparent drug-transporter, integral membrane protein, and member of major facilitator superfamily (MFS). The most widely used techniques, i.e., homology modeling, molecular docking, and molecular dynamics (MD) simulation in the field of structural bioinformatics, have been used in the present work to study the behavior of Rv1250 protein from M. tuberculosis. The structure of unknown TB protein, i.e., Rv1250 was retrived using homology modeling with the help of I-TASSER server. Further, one of the sites responsible for infection was identified and docking was done by using the specific Isoniazid ligand which is an inhibitor of this protein. Finally, the stability of protein model and analysis of stable and static interaction between protein and ligand molecular dynamic simulation was performed at 100 ns The designing of novel Rv1250 enzyme inhibitors is likely achievable with the use of proposed predicted model, which could be helpful in preventing the pathogenesis caused by M. tuberculosis. Finally, the MD simulation was done to evaluate the stability of the ligand for the specific protein.

Project description:BACKGROUND: Mycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains. RESULTS: This study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach. CONCLUSION: In this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Project description:BackgroundHomology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes ('Hole finding protocol') coupled with the identification of candidate proteins for the predicted orphan enzyme ('Hole filling protocol'). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.ResultsThe application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using 'Hole finding protocol'. The 'Hole-filling protocol' was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.ConclusionsWe have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

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DNA polymerase III ? subunit from Mycobacterium tuberculosis H37Rv: Homology modeling and molecular docking of its inhibitor.

Publications

DNA polymerase III α subunit from Mycobacterium tuberculosis H37Rv: Homology modeling and molecular docking of its inhibitor.

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