Target identification of Mycobacterium tuberculosis phenotypic\textit{Mycobacterium tuberculosis phenotypic} hits using a concerted chemogenomic, biophysical and structural approach

Mycobacterium phenotypic hits are a good reservoir for new chemotypes for the treatment of tuberculosis. However, the absence of defined molecular targets and modes of action could lead to failure in drug development. Therefore, a combination of ligand-based and structure-based chemogenomic approaches followed by biophysical and biochemical validation have been used to identify targets for Mycobacterium tuberculosis phenotypic hits. Our approach identified EthR and InhA as targets for several hits, with some showing dual activity against these proteins. From the 35 predicted EthR inhibitors, eight exhibited an IC50 below 50 μM against M. tuberculosis EthR and three were confirmed to be also simultaneously active against InhA. Further hit validation was performed using X-ray crystallography yielding eight new crystal structures of EthR inhibitors. Although the EthR inhibitors attain their activity against M. tuberculosis by hitting yet undefined targets, these results provide new lead compounds that could be further developed to be used to potentiate the effect of EthA activated pro-drugs, such as ethionamide, thus enhancing their bactericidal effect.GM is grateful to the European Molecular Biology Laboratory and Marie Sklodowska-Curie Actions for funding this work. VM and MB acknowledge Bill & Melinda Gates Foundation [subcontract by the Foundation for the National Institutes of Health (NIH)] (OPP1024021). VM and MS acknowledge the European Community’s Seventh Framework Programme [grant number 260872]. GP would like to acknowledge the Wellcome Trust and the European Molecular Biology Laboratory for funding. JPO was funded by the member nation states of the European Molecular Biology Laboratory. TLB acknowledges The Wellcome Trust for funding and support (grant number 200814/Z/16/Z)

Data-mining of potential antitubercular activities from molecular ingredients of traditional Chinese medicines

Background. Traditional Chinese medicine encompasses a well established alternate system of medicine based on a broad range of herbal formulations and is practiced extensively in the region for the treatment of a wide variety of diseases. In recent years, several reports describe in depth studies of the molecular ingredients of traditional Chinese medicines on the biological activities including anti-bacterial activities. The availability of a well-curated dataset of molecular ingredients of traditional Chinese medicines and accurate in-silico cheminformatics models for data mining for antitubercular agents and computational filters to prioritize molecules has prompted us to search for potential hits from these datasets.Results. We used a consensus approach to predict molecules with potential antitubercular activities from a large dataset of molecular ingredients of traditional Chinese medicines available in the public domain. We further prioritized 160 molecules based on five computational filters (SMARTSfilter) so as to avoid potentially undesirable molecules. We further examined the molecules for permeability across Mycobacterial cell wall and for potential activities against non-replicating and drug tolerant Mycobacteria. Additional in-depth literature surveys for the reported antitubercular activities of the molecular ingredients and their sources were considered for drawing support to prioritization.Conclusions. Our analysis suggests that datasets of molecular ingredients of traditional Chinese medicines offer a new opportunity to mine for potential biological activities. In this report, we suggest a proof-of-concept methodology to prioritize molecules for further experimental assays using a variety of computational tools. We also additionally suggest that a subset of prioritized molecules could be used for evaluation for tuberculosis due to their additional effect against non-replicating tuberculosis as well as the additional hepato-protection offered by the source of these ingredients

Mapping and characterization of G-quadruplexes in Mycobacterium tuberculosis gene promoter regions

Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), one of the top 10 causes of death worldwide in 2015. The recent emergence of strains resistant to all current drugs urges the development of compounds with new mechanisms of action. G-quadruplexes are nucleic acids secondary structures that may form in G-rich regions to epigenetically regulate cellular functions. Here we implemented a computational tool to scan the presence of putative G-quadruplex forming sequences in the genome of Mycobacterium tuberculosis and analyse their association to transcription start sites. We found that the most stable G-quadruplexes were in the promoter region of genes belonging to definite functional categories. Actual G-quadruplex folding of four selected sequences was assessed by biophysical and biomolecular techniques: all molecules formed stable G-quadruplexes, which were further stabilized by two G-quadruplex ligands. These compounds inhibited Mycobacterium tuberculosis growth with minimal inhibitory concentrations in the low micromolar range. These data support formation of Mycobacterium tuberculosis G-quadruplexes in vivo and their potential regulation of gene transcription, and prompt the use of G4 ligands to develop original antitubercular agents

A web server for predicting inhibitors against bacterial target GlmU protein

Background: The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design. Results: This study describes the models developed for predicting inhibitory activity (IC50) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r2) between actual and predicted pIC50. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r2). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r2) between predicted and actual pIC50. It was observed that some molecular descriptors used in this study had high correlation with pIC50. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis. Conclusion: These results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC50) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU

In vivo and in silico investigation of selected herbal compounds as anti-tubercular agents

Purpose: To investigate whether some herbal compounds, namely, arctiin, NSC333050, cnicin, and arctigenin, can be used as anti-tubercular agents using in vivo and in silico techniques.Methods: A set of structurally diverse herbal compounds were screened for anti-tubercular activity against the Mycobacterium tuberculosis (Mtb) H37v strain by determining their microbial inhibitory concentration (MIC) and cytotoxicity. The compounds were also screened using in silico techniques, such as molecular docking and absorption, distribution, metabolism, and excretion (ADME) prediction.Results: The in vivo methods, such as determination of MIC and cytotoxicity assay, revealed that some of the herbal compounds showed superior anti-tubercular activity. In silico approaches involving molecular docking simulations for the mycobacterial enzymes Mtb DNA gyrase, Mtb betalactamase, Mtb diaminopelargonic acid synthase, and Mtb cytidine 5'-triphosphate synthase (CTP) confirmed that the inhibitory activities of the herbal compounds occurred at the active sites of these enzyme. In silico ADME prediction also confirmed the pharmacokinetic safety of these herbal compounds.Conclusion: Arctiin, NSC333050, cnicin, and arctigenin, are suitable candidates for clinical evaluation for the treatment of respiratory infections caused by Mtb.Keywords: Tuberculosis, Microbial inhibitory concentration, Arctiin, Cnicin, Arctigenin, In silico, Respiratory infectio

Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

Criteria used to filter high priority M.tuberculosis drug targets. The genes highlighted in bold satisfied all the selection criteria. The hyphen (−) indicates exclusion from further analysis. Abbreviations used: NUI- Not under investigation, PDB- Protein Data Bank, TBSGC- TB Structural Genome Consortium. References 12-Sassetti et al., 2003; 34-Lamichhane et al., 2003. Data can be viewed in Microsoft excel. (XLS 12 kb

Pharmacokinetics of β-Lactam Antibiotics:Clues from the Past to Help Discover Long-Acting Oral Drugs in the Future

β-Lactams represent perhaps the most important class of antibiotics yet discovered. However, despite many years of active research, none of the currently approved drugs in this class combine oral activity with long duration of action. Recent developments suggest that new β-lactam antibiotics with such a profile would have utility in the treatment of tuberculosis. Consequently, the historical β-lactam pharmacokinetic data have been compiled and analyzed to identify possible directions and drug discovery strategies aimed toward new β-lactam antibiotics with this profile