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FadA5 a thiolase from Mycobacterium tuberculosis: a steroid-binding pocket reveals the potential for drug development against tuberculosis.

ABSTRACT: With the exception of HIV, tuberculosis (TB) is the leading cause of mortality among infectious diseases. The urgent need to develop new antitubercular drugs is apparent due to the increasing number of drug-resistant Mycobacterium tuberculosis (Mtb) strains. Proteins involved in cholesterol import and metabolism have recently been discovered as potent targets against TB. FadA5, a thiolase from Mtb, is catalyzing the last step of the ?-oxidation reaction of the cholesterol side-chain degradation under release of critical metabolites and was shown to be of importance during the chronic stage of TB infections. To gain structural and mechanistic insight on FadA5, we characterized the enzyme in different stages of the cleavage reaction and with a steroid bound to the binding pocket. Structural comparisons to human thiolases revealed that it should be possible to target FadA5 specifically, and the steroid-bound structure provides a solid basis for the development of inhibitors against FadA5.

SUBMITTER: Schaefer CM

PROVIDER: S-EPMC4286435 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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FadA5 a thiolase from Mycobacterium tuberculosis: a steroid-binding pocket reveals the potential for drug development against tuberculosis.

Schaefer Christin M CM Lu Rui R Nesbitt Natasha M NM Schiebel Johannes J Sampson Nicole S NS Kisker Caroline C

Structure (London, England : 1993) 20141204 1

With the exception of HIV, tuberculosis (TB) is the leading cause of mortality among infectious diseases. The urgent need to develop new antitubercular drugs is apparent due to the increasing number of drug-resistant Mycobacterium tuberculosis (Mtb) strains. Proteins involved in cholesterol import and metabolism have recently been discovered as potent targets against TB. FadA5, a thiolase from Mtb, is catalyzing the last step of the β-oxidation reaction of the cholesterol side-chain degradation ...[more]

PMID: 25482540

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Project description:BackgroundMycobacterium 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.ResultsThis 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.ConclusionIn 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/.

Dataset Information

FadA5 a thiolase from Mycobacterium tuberculosis: a steroid-binding pocket reveals the potential for drug development against tuberculosis.

Publications

FadA5 a thiolase from Mycobacterium tuberculosis: a steroid-binding pocket reveals the potential for drug development against tuberculosis.

Similar Datasets

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