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ABSTRACT: 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/.
Chemistry Central Journal 03/2013; 7(1):49. · 3.28 Impact Factor
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ABSTRACT: 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/.
Chemistry Central Journal. 01/2013; 7(1):49.
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ABSTRACT: Restriction fragment length polymorphism (RFLP) based on IS6110 is considered the gold standard for Mycobacterium tuberculosis molecular typing. It is useful to discriminate among M. tuberculosis strains, investigate outbreaks and distinguish between reactivation and re-infection. We studied polymorphisms among M. tuberculosis isolates from northern India using RFLP to determine the presence of a correlation between IS6110 based fingerprints and drug resistance and to look for relapse and transmission among patients and their contacts. RFLP patterns of PvuII digested genomic DNA of 100 M. tuberculosis isolates were analyzed using southern blotting with a 245 bp IS6110 probe. Drug sensitivity testing (DST) was conducted for rifampicin (40 microg/ml), isoniazid (1 microg/ml), ethambutol (2 microg/ml) and streptomycin (4 microg/ml) using the proportion method. A high degree of polymorphism was seen among the M. tuberculosis isolates and the number of IS6110 copies varied from 0 to 14, with a predominance of isolates with 11 bands. Seventy-five isolates had a high number of bands, 9 had an intermediate number, 6 isolates had a low number and 10 isolates had no bands. No correlation between IS6110 band numbers and RFLP banding patterns was found with drug resistance or for any particular geographical area, although clustering was seen amongst MDR-TB cases. No cases of relapses or transmissions were seen.
The Southeast Asian journal of tropical medicine and public health 09/2012; 43(5):1161-8. · 0.60 Impact Factor
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ABSTRACT: Abstract Background: The diagnosis of pulmonary tuberculosis is still a major challenge. Using a polymerase chain reaction (PCR), one can detect Mycobacterium tuberculosis in clinical samples within a few hours. However, single gene targets may result in false negativity due to the absence of target DNA in some M. tuberculosis isolates. The objective of this study was to develop and evaluate a multiplex PCR (M-PCR) using IS6110 and devR primers for the detection of M. tuberculosis in sputum samples. Methods: Sputum samples were collected from: (1) 200 confirmed cases of tuberculosis; (2) 100 suspected cases of tuberculosis diagnosed on the basis of clinical and radiological findings; (3) 200 non-tubercular patients suffering from respiratory diseases other than tuberculosis, in whom tuberculosis had been excluded. All 500 sputum samples were subjected to PCR using IS6110 primers, and M-PCR using IS6110 and devR primers; results were compared with conventional techniques. Results: It was found that M-PCR was 97.5% successful in detecting the presence of tuberculosis in the confirmed tuberculosis group as compared to 84.5% by IS6110-based PCR. In the suspected tuberculosis group, M-PCR could detect 45% of cases as compared to 40% by IS6110-based PCR. Overall, the specificities of both the PCR and M-PCR were found to be 96.5%. Conclusions: This study demonstrated that the M-PCR assay is more sensitive than the IS6110-based PCR for the detection of M. tuberculosis in sputum specimens and could be applied in situations of highly suspected tuberculosis when all others tests including IS6110 PCR are negative.
Scandinavian Journal of Infectious Diseases 06/2012; 44(10):739-44. · 1.72 Impact Factor
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ABSTRACT: Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general.
This paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitative structure activity relationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value (Ki) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R/q2 of 0.82/0.67 and MAE of 2.43. Additionally, Support Vector Machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R/q2 values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values (q2/r2) in the range of 0.78-0.83/0.93-0.95.
Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.osdd.net/raghava/kidoq, which allows the prediction of Ki value of a new ligand molecule against DHDPS.
BMC Bioinformatics 03/2010; 11:125. · 2.75 Impact Factor
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Article:
K
BMC Bioinformatics. 01/2010; 11:125.
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BMC Bioinformatics. 01/2010; 11:53.
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ABSTRACT: Abstract
Background
An explosive global spreading of multidrug resistant Mycobacterium tuberculosis ( Mtb ) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols.
Results
In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions.
Conclusion
The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time.
BMC Bioinformatics. 01/2010;
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[show abstract]
[hide abstract]
ABSTRACT: Abstract
Background
Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general.
Results
This paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitative structure activity relationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value ( K <sub>i</sub>) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R / q <sup>2 </sup>of 0.82/0.67 and MAE of 2.43. Additionally, Support Vector Machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R / q <sup>2 </sup>values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values ( q <sup>2</sup>/ r <sup>2</sup>) in the range of 0.78-0.83/0.93-0.95.
Conclusions
Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "K i DoQ" has been developed http://crdd.osdd.net/raghava/kidoq , which allows the prediction of K <sub>i </sub>value of a new ligand molecule against DHDPS.
BMC Bioinformatics. 01/2010;
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[show abstract]
[hide abstract]
ABSTRACT: An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols.
In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions.
The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time.
BMC Bioinformatics 01/2010; 11 Suppl 1:S53. · 2.75 Impact Factor
