Dataset Information

In silico screening for novel inhibitors of DNA polymerase III alpha subunit of Mycobacterium tuberculosis (MtbDnaE2, H37Rv).

ABSTRACT: Tuberculosis, a pandemic disease is caused by Mycobacterium tuberculosis (Mtb). DNA polymerase III encoded by DnaE2 of Mtb is specifically required for its survival in vivo, and hence can be considered to be a potential drug target. Amino acid sequence analysis of the MtbDnaE2 and its human counterpart does not show any significant similarity. Therefore, a 3D model of the MtbDnaE2 was generated using Modeller 9v10 with the template structure of E. Coli DNA polymerase III alpha subunit (2HNH_A). The generated models were validated using a number of programmes such as RAMPAGE/PROCHECK, VERIFY_3D, and ProSA. MtbDnaE2 has few conserved residues and four conserved domains similar to that present in DNA polymerase III of E. coli. In silico screening was performed with bioactive anti-tuberculosis compounds and 6-AU (a known inhibitor of DNA polymerase III of Bacillus subtilis) and its analogues against the modeled MtbDnaE2 structure. Docking was performed using GOLD v5.2 software which resulted in the identification of top ten compounds with high GOLD fitness scores and binding affinity (X-Score). To further evaluate the efficacy of these compounds, in silico ADMET analysis was performed using MedChem Designer v3. Given their high binding affinity to the targeted MtbDnaE2, which is essential for DNA replication in the Mtb and good ADMET properties, these compounds are promising candidates for further evaluation and development as anti-tubercular agents.

SUBMITTER: Jadaun A

PROVIDER: S-EPMC4374717 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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In silico screening for novel inhibitors of DNA polymerase III alpha subunit of Mycobacterium tuberculosis (MtbDnaE2, H37Rv).

Jadaun Alka A Sudhakar D Raja R Subbarao N N Dixit Aparna A

PloS one 20150326 3

Tuberculosis, a pandemic disease is caused by Mycobacterium tuberculosis (Mtb). DNA polymerase III encoded by DnaE2 of Mtb is specifically required for its survival in vivo, and hence can be considered to be a potential drug target. Amino acid sequence analysis of the MtbDnaE2 and its human counterpart does not show any significant similarity. Therefore, a 3D model of the MtbDnaE2 was generated using Modeller 9v10 with the template structure of E. Coli DNA polymerase III alpha subunit (2HNH_A). ...[more]

PMID: 25811866

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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:BackgroundMycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.ResultsWe completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.ConclusionAlthough we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

Dataset Information

In silico screening for novel inhibitors of DNA polymerase III alpha subunit of Mycobacterium tuberculosis (MtbDnaE2, H37Rv).

Publications

In silico screening for novel inhibitors of DNA polymerase III alpha subunit of Mycobacterium tuberculosis (MtbDnaE2, H37Rv).

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