Dataset Information

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

ABSTRACT: BACKGROUND:In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes. METHODS:The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ?770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. RESULTS:A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. CONCLUSION:Potential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to resistance genes is more likely to disrupt the communication to these genes. Purposely selected literature review of the top 14 proteins showed that many of them in this list were proposed as drug targets of the pathogen.

SUBMITTER: Melak T

PROVIDER: S-EPMC4467812 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

Melak Tilahun T Gakkhar Sunita S

Clinical and translational medicine 20150605 1

<h4>Background</h4>In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This stud ...[more]

PMID: 26061871

Similar Datasets

Project description:BackgroundMycobacterium tuberculosis, an intracellular pathogen encounters redox stress throughout its life inside the host. In order to protect itself from the redox onslaughts of host immune system, M. tuberculosis appears to have developed accessory thioredoxin-like proteins which are represented by ORFs encoding WhiB-like proteins. We have earlier reported that WhiB1/Rv3219 is a thioredoxin like protein of M. tuberculosis and functions as a protein disulfide reductase. Generally thioredoxins have many substrate proteins. The current study aims to identify the substrate protein(s) of M. tuberculosis WhiB1.ResultsUsing yeast two-hybrid screen, we identified alpha (1,4)-glucan branching enzyme (GlgB) of M. tuberculosis as a interaction partner of WhiB1. In vitro GST pull down assay confirmed the direct physical interaction between GlgB and WhiB1. Both mass spectrometry data of tryptic digests and in vitro labeling of cysteine residues with 4-acetamido-4' maleimidyl-stilbene-2, 2'-disulfonic acid showed that in GlgB, C95 and C658 are free but C193 and C617 form an intra-molecular disulfide bond. WhiB1 has a C37XXC40 motif thus a C40S mutation renders C37 to exist as a free thiol to form a hetero-disulfide bond with the cysteine residue of substrate protein. A disulfide mediated binary complex formation between GlgB and WhiB1C40S was shown by both in-solution protein-protein interaction and thioredoxin affinity chromatography. Finally, transfer of reducing equivalent from WhiB1 to GlgB disulfide was confirmed by 4-acetamido-4' maleimidyl-stilbene-2, 2'-disulfonic acid trapping by the reduced disulfide of GlgB. Two different thioredoxins, TrxB/Rv1471 and TrxC/Rv3914 of M. tuberculosis could not perform this reaction suggesting that the reduction of GlgB by WhiB1 is specific.ConclusionWe conclude that M. tuberculosis GlgB has one intra-molecular disulfide bond which is formed between C193 and C617. WhiB1, a thioredoxin like protein interacts with GlgB and transfers its electrons to the disulfide thus reduces the intra-molecular disulfide bond of GlgB. For the first time, we report that GlgB is one of the in vivo substrate of M. tuberculosis WhiB1.

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

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

Publications

Maximum flow approach to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv from protein-protein interaction network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets