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The putative compatible solute-binding protein ProX from Mycobacterium tuberculosis H37Rv: biochemical characterization and crystallographic data.

ABSTRACT: In Mycobacterium tuberculosis, the proX gene encodes a putative compatible solute-binding protein (MtProX). However, it was found through sequence alignment that the MtProX protein has very different ligand-binding residues compared with other compatible solute-binding proteins, implying that MtProX may bind to ligands that are as yet uncharacterized. In this work, it was demonstrated that MtProX binds to polyphenols such as phloretin, monoacetylphloroglucinol and 2,4-dihydroxyacetophloroglucinol with dissociation constants between 20 and 70?µM. Crystals of MtProX were obtained using a precipitant consisting of 0.2?M NaCl, 0.1?M Tris pH 8.5, 25%(w/v) polyethylene glycol 3350. The crystals diffracted to 2.10?Å resolution and belonged to space group P43212, with unit-cell parameters a = b = 90.17, c = 161.92?Å, ? = ? = ? = 90.0°. Assuming the presence of two MtProX molecules in the asymmetric unit, the Matthews coefficient was calculated to be 2.74?Å3?Da-1, which corresponds to a solvent content of 55%.

SUBMITTER: Zhao JH

PROVIDER: S-EPMC5894108 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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The putative compatible solute-binding protein ProX from Mycobacterium tuberculosis H37Rv: biochemical characterization and crystallographic data.

Zhao Jian Hong JH Chen Jiang Huai JH Wang Yong Y Wang Zhi Ping ZP He Yong Xing YX

Acta crystallographica. Section F, Structural biology communications 20180323 Pt 4

In Mycobacterium tuberculosis, the proX gene encodes a putative compatible solute-binding protein (MtProX). However, it was found through sequence alignment that the MtProX protein has very different ligand-binding residues compared with other compatible solute-binding proteins, implying that MtProX may bind to ligands that are as yet uncharacterized. In this work, it was demonstrated that MtProX binds to polyphenols such as phloretin, monoacetylphloroglucinol and 2,4-dihydroxyacetophloroglucino ...[more]

PMID: 29633971

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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

The putative compatible solute-binding protein ProX from Mycobacterium tuberculosis H37Rv: biochemical characterization and crystallographic data.

Publications

The putative compatible solute-binding protein ProX from Mycobacterium tuberculosis H37Rv: biochemical characterization and crystallographic data.

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