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Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate ?-decarboxylase.

ABSTRACT: L-aspartate ?-decarboxylase (ADC) belongs to a class of pyruvoyl dependent enzymes and catalyzes the conversion of aspartate to ?-alanine in the pantothenate pathway, which is critical for the growth of several micro-organisms, including Mycobacterium tuberculosis (Mtb). Its presence only in micro-organisms, fungi and plants and its absence in animals, particularly human, make it a promising drug target. We have followed a chemoinformatics-based approach to identify potential drug-like inhibitors against Mycobacterium tuberculosis L-aspartate ?-decarboxylase (MtbADC). The structure-based high throughput virtual screening (HTVS) mode of the Glide program was used to screen 333,761 molecules of the Maybridge, National Cancer Institute (NCI) and Food and Drug Administration (FDA) approved drugs databases. Ligands were rejected if they cross-reacted with S-adenosylmethionine (SAM) decarboxylase, a human pyruvoyl dependent enzyme. The lead molecules were further analyzed for physicochemical and pharmacokinetic parameters, based on Lipinski's rule of five, and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. This analysis resulted in eight small potential drug-like inhibitors that are in agreement with the binding poses of the crystallographic ADC:fumarate and ADC:isoasparagine complex structures and whose backbone scaffolds seem to be suitable for further experimental studies in therapeutic development against tuberculosis.

SUBMITTER: Sharma R

PROVIDER: S-EPMC3314653 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase.

Sharma Reetu R Kothapalli Roopa R Van Dongen Antonius M J AM Swaminathan Kunchithapadam K

PloS one 20120328 3

L-aspartate α-decarboxylase (ADC) belongs to a class of pyruvoyl dependent enzymes and catalyzes the conversion of aspartate to β-alanine in the pantothenate pathway, which is critical for the growth of several micro-organisms, including Mycobacterium tuberculosis (Mtb). Its presence only in micro-organisms, fungi and plants and its absence in animals, particularly human, make it a promising drug target. We have followed a chemoinformatics-based approach to identify potential drug-like inhibitor ...[more]

PMID: 22470451

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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/.

Dataset Information

Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate ?-decarboxylase.

Publications

Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase.

Similar Datasets

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