• Users Online: 826
  • Home
  • Print this page
  • Email this page

 Table of Contents  
Year : 2019  |  Volume : 8  |  Issue : 3  |  Page : 252-261

An In silico approach to identify potential inhibitors against multiple drug targets of Mycobacterium tuberculosis

Bioinformatics Centre, Department of Biochemistry and JBTDRC, Mahatma Gandhi Institute of Medical Sciences, Wardha, Maharashtra, India

Date of Web Publication12-Sep-2019

Correspondence Address:
Dr Satish Kumar
Bioinformatics Centre, Mahatma Gandhi Institute of Medical Sciences, Sevagram, Wardha - 442 102, Maharashtra
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijmy.ijmy_109_19

Rights and Permissions

Background: The increasing incidence of multidrug-resistant cases of tuberculosis (TB) and difficulty in treating these cases requires an urgent need to find an effective anti-TB drug. There are many phytochemicals with reported antibacterial and antitubercular activities. Instead of targeting only a single target of Mycobacterium tuberculosis (MTB), this study aims to identify phytochemicals targeting multiple drug targets of MTB through subtractive genomic/proteomic approach followed by in silico screening of phytochemicals with reported anti-TB activity. Methods: Of 614 essential genes of MTB reported in database of essential genes, 15 gene products were selected using different bioinformatic resources and tools such as PANTHER, Venny, NCBI, and BLAST. Results: Virtual screening analysis of these selected drug targets against identified 148 phytochemicals revealed that amentoflavone, carpaine, 13'bromo-tiliacorinine, and 2'nortiliacorinine, able to inhibit more than one target of MTB. Conclusion: These selected compounds may be proposed as potential inhibitors of MTB and need to be tested in TB culture studies in vitro to assess their anti-TB activity.

Keywords: Bioinformatics, drug targets, inhibitors, phytochemicals, tuberculosis

How to cite this article:
Kumar S, Sahu P, Jena L. An In silico approach to identify potential inhibitors against multiple drug targets of Mycobacterium tuberculosis. Int J Mycobacteriol 2019;8:252-61

How to cite this URL:
Kumar S, Sahu P, Jena L. An In silico approach to identify potential inhibitors against multiple drug targets of Mycobacterium tuberculosis. Int J Mycobacteriol [serial online] 2019 [cited 2020 Jan 24];8:252-61. Available from: http://www.ijmyco.org/text.asp?2019/8/3/252/266486

  Introduction Top

Tuberculosis (TB) has prevailed for millennia and remains a major global health problem. It causes epidemic health issues for approximately 10 million people each year. For the past 5 years, it has been the prominent cause of death from a single infectious agent, ranking above HIV/AIDS.[1] Approximately 10.4 million people fell into suffering with TB in 2016, of which 90% were adults, 65% were male, 10% were people living with HIV (74% in Africa), and 56% were in five countries: India, Indonesia, China, the Philippines, and Pakistan.[1] The major threat in combating TB is increasing multidrug- and extensively drug-resistant cases. Although anti-TB drugs such as isoniazid, rifampicin, ethambutol, and streptomycin were introduced till the 1980s, reporting 98% chances of cure,[2] they have major serious side effects such as psychosis, mental confusion, coma, convulsive seizures, vasculitis, clinical hepatitis, and peripheral neuropathy,[3] and the drug targets of these drugs are found to be mutated in most of the drug-resistant clinical strains of Mycobacterium tuberculosis (MTB) leading to drug-resistant TB. There are different studies across the world for identification of drug targets in MTB. As MTB cell wall is mainly associated with its survival, growth, permeability, virulence, and resistance to antibiotics, it is reported as the versatile fountain of drug targets in MTB.[4] Anishetty et al. identified 185 distinct drug targets in MTB through metabolic pathway analysis.[5] Further, Amir et al. identified 55 drug targets in five unique pathways of MTB through in silico approach.[6] Vashisht et al. also proposed the interactome-driven drug target identification in MTB.[7] There are many studies in India showing the inhibitory effect of phytochemicals on MTB growth. Raju et al. studied the antifolate activity of plant polyphenols against MTB.[8] Lauric acid and myristic acid from Allium sativum also reported to inhibit the growth of MTB H37 Ra [9],[10] screened and evaluated five medicinal legumes for antitubercular activity through in vitro study. Gupta et al. also evaluated the anti-TB activity of Alpinia galanga L. axenically under reducing oxygen conditions and in intracellular assays.[11] Jha et al. also proposed the anti-TB activity of alkaloids in leaves of Justicia adhatoda.[12] Zhang and Amzel described a list of new drug candidates along with proposed targets for TB intervention.[13] Structural and functional aspects of enzymes present in MTB peptidoglycan biosynthesis proposed as potential targets for drug development.[14] Hosen et al. performed in silico identification and characterization of novel drug targets in F11 clinical strain of MTB.[15] Melak and Gakkhar employed protein–protein interaction network analysis for identification of nonhomologous protein targets of MTB.[16] Further, proteases of MTB also proposed as potential drug targets.[17] Further, different proteins of MTB, namely KasA,[18] Rv2971 (probable oxidoreductase),[19] cytochrome P450,[20] glutamine synthetase,[21] and cyclopropane mycolic acid synthase 1,[22] have been proposed as potential drug targets for MTB. Bunalema et al. studied the inhibitory effect of crude root bark extracts of Erythrina abyssinica on rifampicin-resistant MTB.[23] Luo et al. performed the antimycobacterial evaluation and phytochemical investigation of selected medicinal plants traditionally used in Mozambique.[24] Tan et al. reported the anti-TB activity of triterpenes and phytosterols from Pandanus tectorius Soland. var. laevis.[25] Wächter et al. showed the growth inhibition of MTB bacilli by saringosterol from Lessonia nigrescens.[26]

There is an urgent need of new anti-TB drug as the death rate due to TB and multidrug- and extensively drug-resistant cases are increasing day by day.[27] Discovery of a new drug, with minimal side effects, may find a ray of hope to control TB. Thus, in this study, instead of targeting only a single drug target of MTB, we aim to target all possible drug targets of MTB obtained computationally through subtractive genomic/proteomic approach. As there are different phytochemicals reported in literature and online resources, with anti-TB activity, in silico screening of identified phytochemicals against all possible drug targets was performed to identify a list of potential phytochemicals showing better inhibition against most of the targets.

  Methods Top

Hardware and software

Dell workstation with Windows operating system, having a hard disc of 1 TB and 8 GB RAM, was used for this in silico study. Further, computational analysis was performed using bioinformatics tools such as AutoDockTools 1.5.4,[28] AutoDock Vina 1.1.2,[29] PyMol molecular visualization packages [30] and online resources.

Data mining

Database of essential genes (DEG) contains all the essential genes currently available. By analyzing all the essential genes in DEG, some principles or regulations could be found to answer the question of what are the basic functions necessary to support cellular life. Those principles could lead to the development of new algorithms to predict essential genes. Some functions encoded by essential genes are expected, such as DNA replication, gene transcription, protein synthesis, energy production, and cell division. Analysis of DEG, which has all essential genes among different organisms, could help to classify those “unexpected” essential genes.[31] We explored the DEG and found a total of 614 essential genes for H37 Rv strain. Subsequently, handling of the 614 genes in PANTHER database [32] was done for functional annotation. PANTHER database is an abundant store of protein families that have been subdivided into functionally related subfamilies, developed using human intelligence. The latest version contains 15,524 protein families, divided into 79,562 functionally distinct protein subfamilies, which covers approximately 90% of mammalian protein-coding genes.[33] The functionally annotated genes obtained from PANTHER database analysis were subjected to Venny [33] for getting multifunctional protein-coding genes. The multifunctional protein-coding genes were further subjected to NCBI BLAST program [34] against Homo sapiens for getting the nonhomologous genes.

Ligand dataset preparation

Evidence from numerous observational and clinical studies suggests that different phytochemicals such as amentoflavone, tiliacorine, sesamin, β-sitosterol, Mauritine M, orientin, and ramontoside have antibacterial and antimycobacterial properties. For screening of drugs, around 700 known anti-TB phytochemicals identified from different sources such as Dr. Duke's Phytochemical and Ethnobotanical Databases, BioPhytMol (a manually curated drug discovery community resource on antimycobacterial phytomolecules and plant extracts),[35] and different literature surveys [36],[37],[38],[39],[40] are considered for this proposed study. A total of 148 phytochemicals were selected from online resources and literature study. The chemical structure files were retrieved from NCBI PubChem database.[41]

Prediction of protein structure and its validation

The three-dimensional (3D) structure of selected proteins was predicted using Phyre2 server. The 3D structure was subjected to structure validation using ProSA-web,[42] Protein Quality Predictor (ProQ),[43] and Research Collaboratory for Structural Bioinformatics (RCSB) validation server.[44] Phyre2 is an assembly of tools available on the web to predict and analyze protein structure, function, and mutations.[45] AutoDockTools 1.5.4 program (ADT, Molecular Graphics Lab at The Scripps Research Institute, North Torrey Pines Road, La Jolla, California, USA) was used to prepare receptor molecule (Glf) by adding all hydrogen atoms into the carbon atoms of the receptor. Kollman charges were also assigned, and the entire receptor molecules were converted to protein data bank extension file format (PDBQT).

Binding-site prediction

UniProt [46] database search was performed for getting the active site of identified proteins. Those proteins, whose active site was not reported, were subjected to 3DLigandSite web server for predicting their active site. 3DLigandSite is a web server for the prediction of ligand-binding sites based on successful manual methods used in the eighth round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP8). 3DLigandSite utilizes protein structure prediction to provide structural models for proteins that have not been solved.[47]

Virtual screening

We advanced for virtual screening of 15 proteins with 148 ligands. Virtual screening prototype is now being widely used for filtering large dataset of compounds for drug discovery. All the 148 compounds were subjected to virtual screening along with the 15 proteins using AutoDock Vina version 1.1.2 virtual screening program.[28],[29] Molecular docking of all the ligands with target protein was performed on the binding site of substrate. A grid of 30, 30, and 30 points in X, Y, and Z directions was centered on the known active site residues of proteins. Ligands showing the highest binding energy were selected for further analysis.

  Results and Discussion Top

In silico analysis using different tools is found to be very useful in developing biologic-based therapeutics for different diseases,[48] exploring different genetic information.[49] Further, the computational approach was also found to be helpful in identifying novel potential inhibitors for MTB drug targets and also useful in TB diagnosis to predict drug resistance.[50] Data mining for MTB H37 Rv strain in DEG database yielded 614 essential genes. Of 614 genes, 301 genes were found to be affecting molecular function (MF) of MTB; 274 genes were found to be actively involved in biological process of MTB; 156 genes were found in various cellular component of MTB; 282 genes were available acting as proteins of various protein classes when casted for functional annotation using PANTHER database. From the available functionally annotated genes from PANTHER database, we filtered 79 multifunctional genes accounting for MF, cellular component, and protein class and involved in biological processes of MTB using Venny.[33] Seventy-nine gene products were subjected to NCBI BLAST against H. sapiens Refseq protein query database which resulted in 30 filtered MTB gene products nonhomologous to H. sapiens. Of 30 gene products, we further shortlisted only 15 proteins those having query coverage and similarity both >70%.

These filtered 15 proteins then undergone molecular modeling using Phyre2 server. Then, we performed the quality analysis of the protein structure using ProSA-web, ProQ, and ProCheck and observed that these protein structures were of good quality based on Ramachandran plot analysis, LGscore, and Z-score [Table 1] Z-plot, Energy Plot, and Ramachandran Plots of all the proteins are shown in [Figure 1].
Table 1: Protein quality analysis results obtained using ProSA-web, ProQ, and ProCheck

Click here to view
Figure 1: Z-plot, Energy Plot, and Ramachandran Plot of (a) AcpM, (b) AroF, (c) Ask, (d) AtpE, (e) DdlA, (f) HisA, (g) NadE, (h) PanC, (i) RibH, (j) RplE, (k) RplW, (l) RpsE, (m) RpsS, (n) TrpA, (o) TrpB, [a1,b1,...:Z-plot; a2,b2,....:Energy Plot; a3,b3,....: Ramachandran Plot of corresponding protein (a,b,...)]

Click here to view

Further, detailed information on protein name, function, and pathways of 15 proteins are given in [Table 2]. We searched for active site/binding site of the 15 proteins in UniProt database where we found that active sites for 8 proteins (Rv2540c, Rv3709c, Rv2981c, Rv1603, Rv2438c, Rv3602c, Rv1416, and Rv1613) were available. Hence, active sites for other 7 proteins (Rv2244, Rv1305, Rv0705, Rv0721, Rv1612, Rv0703, and Rv0716) were predicted using 3DLigandSite server. We advanced further for virtual screening of 15 proteins with 148 ligands using AutoDock Vina software.
Table 2: Protein/Gene ID, protein name, function, and pathway information of Mycobacterium tuberculosis drug targets

Click here to view

This study revealed that 8 of 148 ligands had good interaction with proteins with the lowest binding energy and IUPAC name, CAS registry number and molecular weight of these 8 ligands are given in [Table 3].
Table 3: Selected natural compounds reported to use against Mycobacterium tuberculosis

Click here to view

In our in silico study, we observed that taraxerol inhibits AcpM with binding energy of − 7.2 Kcal/mol [Table 4]. It is interacting with the receptor by forming three hydrogen bonds [Figure 2]a. AcpM is an acyl carrier protein (ACP) reported to involve in meromycolate extension and associated with different pathways of MTB such as fatty acid biosynthesis, fatty acid metabolism, lipid biosynthesis, and lipid metabolism.[51] Further, it is also reported that the ACP domain forms an integral part of the polypeptide, required for fatty acid biosynthesis.[52]
Table 4: Polar contacts information from in silico study between ligands and proteins

Click here to view
Figure 2: Ligand–protein interaction of (a) taraxerol-AcpM, (b) carpaine-AroF, (c) amentoflavone-Ask, (d) 13-bromo-tiliacorinine-AtpE, (e) amentoflavone-DdlA, (f) artonin F-HisA, (g) carpaine-NadE, (h) amentoflavone-PanC, (i) 13-bromo-tiliacorinine-RibH, (j) 2-nortiliacorinine-RplE, (k) amentoflavone-RplW, (l) 2-nortiliacorinine-RpsE, (m) diospyrin-RpsS, (n) bonianic acid B-TrpA, (o) amentoflavone-TrpB

Click here to view

Further, it was observed that carpaine interacted with AroF with the lowest binding energy and had polar contact of 2.7 Š at ARG135 residue of the receptor [Figure 2]b. It is reported that AroF is involved in phenylalanine, tyrosine, and tryptophan biosynthesis; metabolic pathways; biosynthesis of secondary metabolites; biosynthesis of antibiotics; and biosynthesis of amino acids.[46] It is also reported that DNA amplification of AroF-encoded CS from MTB (MtCS) shows that CS is bifunctional.[53]

Our virtual screening study revealed that amentoflavone inhibits Ask with binding energy of −9.9 Kcal/mol [Figure 2]c. The amino acid residues THR156, LEU214, LEU212, ALA205, and ARG355 of Ask protein were formed polar contact with amentoflavone during ligand–protein interaction. It is also reported that catalysis of phosphorylation of the beta-carboxyl group of aspartic acid with ATP is done by Ask to yield 4-phospho-L-aspartate, which is involved in the branched biosynthetic pathway leading to the biosynthesis of amino acids lysine, threonine, isoleucine, and methionine.[51] Further, biosynthesis of aspartate family amino acids, including lysine, threonine, isoleucine, and methionine is also catalyzed by this aspartate kinase (Ask) protein of MTB.[54]

Further, it was observed that 13'-bromo-tiliacorinine had a good binding affinity with AtpE and it formed one hydrogen bond with ASP28 residue of AtpE [Figure 2]d. The AtpE has an important role in translocation across the membrane which makes it a key component of the F0 channel. It also plays a vital role in oxidative phosphorylation and metabolic pathways.[51] It is also noticed that lassomycin binds to a highly acidic region of the ClpC1 ATPase complex and markedly stimulates its ATPase activity without stimulating ClpP1P2-catalyzed protein breakdown, which is essential for viability of mycobacteria.[55]

Further, this in silico approach revealed that the amino acid residues LYS194, ASN329, ARG316, GLU23, and SER201 of DdlA protein were forming polar contact with amentoflavone [Figure 2]e and having binding energy of −10.7 Kcal/mol. DdlA catalyzes the ATP-driven ligation of two D-alanine molecules to form the D-alanyl-D-alanine dipeptide and involved in D-alanine metabolism, peptidoglycan biosynthesis, metabolic pathways, and vancomycin resistance.[51]

HisA is an important drug target of MTB, involved in both the histidine and tryptophan biosynthetic pathways and also plays a key role in histidine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; metabolic pathways; biosynthesis of secondary metabolites; antibiotics; and amino acids.[51] Furthermore, it is reported that two specific single-substrate enzymes (HisA and TrpF), sharing a similar (β/α)(8)-barrel scaffold, catalyze two isomerization reactions in histidine and tryptophan biosynthesis.[56] Our virtual screening study revealed that artonin F inhibits the HisA with binding energy of 7.4 kcal/mol and formed polar contact with ARG16 and SER33 of HisA protein [Figure 2]f.

Further, we observed in our protein–ligand docking study that carpaine interacted with NadE protein with good binding affinity and formed bond with ARG335 residue of NadE protein [Figure 2]g. NadE catalyzes the ATP-dependent amidation of deamido-NAD to form NAD and plays a vital role in NAD(+) biosynthesis.[51] Another study revealed that NAD(+) synthetase present in MTB utilizes both glutamine and ammonia to catalyze NAD(+) production.[57]

It is reported that the enzyme pantothenate synthetase, PanC, is an essential drug target in MTB. It is essential for the in vitro growth and survival of MTB in the mouse model of infection.[58] Our study revealed that amentoflavone interacted with PanC protein with binding energy of −10.7 Kcal/mol and showed interaction with the receptor by forming H-bonds with four residues of receptor, i.e., GLY46, LYS160, HIS44, and ASN69 [Figure 2]h. PanC catalyzes the condensation of pantoate with beta-alanine in an ATP-dependent reaction via a pantoyl-adenylate intermediate and also involved in beta-alanine metabolism, pantothenate and CoA biosynthesis, metabolic pathways, and biosynthesis of secondary metabolites.[51]

Further, RibH, RplE, RplW, RpsE, and RpsS were also reported to have essential functions toward the survival of mycobacteria.[51] It is also reported that species-specific ribosomal RNA expansion segments and ribosomal proteins are possessed by ribosomes of MTB.[59] Our in silico study revealed that 13'bromo-tiliacorinine interacted with RibH protein with binding energy of −8.1 Kcal/mol and formed H-bond with TRP27 residue [Figure 2]i. Further, 2'nortiliacorinine was observed to bind with RplE [Figure 2]j with a binding energy of −8.4 Kcal/mol. In this study, we observed that amentoflavone showed binding affinity of −7.4 Kcal/mol with RplW and formed polar contacts with ILE49 and ASP94 residues [Figure 2]k. Our virtual screening study also revealed that 2'nortiliacorinine inhibited RpsE [Figure 2]l with binding energy of −7.4 Kcal/mol. We also noticed that the binding energy of diospyrin and RpsS was −8.8 Kcal/mol and it formed polar contact with at VAL60, LEU71, SER38, LYS70, and SER35 residue positions [Figure 2]m.

TrpA the alpha-subunit is responsible for the aldol cleavage of indoleglycerol phosphate to indole and glyceraldehyde 3-phosphate and involved in L-tryptophan biosynthesis.[51] It is a potential drug target for MTB.[60] The beta-subunit is responsible for the synthesis of L-tryptophan from indole and L-serine and also participates in glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; metabolic pathways; biosynthesis of secondary metabolites; antibiotics; and amino acids.[51] According to another inspection, TrpB is also an essential drug target of MTB.[61] Virtual screening analysis revealed that bonianic acid B inhibiting TrpA by forming polar contacts with VAL160, LEU218, GLY219, VAL220, GLY239, and ILE237 residues of TrpA [Figure 2]n, whereas amentoflavone shows a good binding affinity with TrpB. It formed polar contacts with TrpB at ARG155, ALA126, ASP319, HIS129, THR204, GLY247, and GLY248 [Figure 2]o residues in protein–ligand complex.

The overall analysis of this in silico study revealed that most of the MTB drug targets of MTB such as Ask, DdlA, PanC, RplW, and TrpB were inhibited by amentoflavone, whereas AroF and NadE were inhibited by carpaine. Further, 13'bromo-tiliacorinine inhibits AtpE and RibH, whereas 2'nortiliacorinine inhibits RplE and RpsE of MTB.

  Conclusion Top

This study shows the usefulness of computational approach for finding effective drug targets of MTB and effective inhibitors from large dataset of phytochemicals. In this study, we explored different resources to identify important drug targets of MTB and further explored 148 different phytochemicals with known anti-TB activity. We identified four phytochemicals such as amentoflavone, carpaine, 13-bromo-tiliacorinine, and 2-nortiliacorinine showing better binding affinity against multiple drug targets of MTB. These compounds may be proposed as potential drug candidates of MTB, and their anti-TB activity needs to be confirmed through in vitro TB culture study.


The authors express gratitude to the Department of Biotechnology, Ministry of Science and Technology and Government of India for their financial support to Sub-DIC Project wherein this study has been carried out. We would like to thank Dr. Renu Swarup, Secretary, DBT; Dr. Suchita Ninawe, Advisor, DBT; Dr. Gulshan Wadhwa, Joint Director, DBT; Shri D.S. Mehta, President, Kasturba Health Society; Dr. B. S. Garg, Secretary, Kasturba Health Society; Dr. Nitin M. Gangane, Dean, MGIMS; and Dr. S.P. Kalantri, Medical Superintendent, Kasturba Hospital, MGIMS, Sevagram, Maharashtra, India, for their encouragement and support.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

World Health Organization. WHO Global Tuberculosis Report. Geneva, Switzerland: World Health Organization; 2017.  Back to cited text no. 1
Prakash S, Bhimba BV. Pharmaceutical development of novel microalgal compounds for MDR Mycobacterium tuberculosis. Nat Prod Radiance 2005;4:264-9.  Back to cited text no. 2
Arbex MA, Varella Mde C, Siqueira HR, Mello FA. Antituberculosis drugs: Drug interactions, adverse effects, and use in special situations. Part 1:First-line drugs. J Bras Pneumol 2010;36:626-40.  Back to cited text no. 3
Bhat ZS, Rather MA, Maqbool M, Lah HU, Yousuf SK, Ahmad Z. Cell wall: A versatile fountain of drug targets in Mycobacterium tuberculosis. Biomed Pharmacother 2017;95:1520-34.  Back to cited text no. 4
Anishetty S, Pulimi M, Pennathur G. Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Comput Biol Chem 2005;29:368-78.  Back to cited text no. 5
Amir A, Rana K, Arya A, Kapoor N, Kumar H, Siddiqui MA. Mycobacterium tuberculosis H37Rv: In silico drug targets identification by metabolic pathways analysis. Int J Evol Biol 2014;2014:284170.  Back to cited text no. 6
Vashisht R, Mondal AK, Jain A, Shah A, Vishnoi P, Priyadarshini P, et al. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis. PLoS One 2012;7:e39808.  Back to cited text no. 7
Raju A, Degani MS, Khambete MP, Ray MK, Rajan MG. Antifolate activity of plant polyphenols against Mycobacterium tuberculosis. Phytother Res 2015;29:1646-51.  Back to cited text no. 8
Muniyan R, Gurunathan J. Lauric acid and myristic acid from Allium sativum inhibit the growth of Mycobacterium tuberculosis H37Ra: In silico analysis reveals possible binding to protein kinase B. Pharm Biol 2016;54:2814-21.  Back to cited text no. 9
Kumar JK, Devi Prasad AG, Chaturvedi V. Phytochemical screening of five medicinal legumes and their evaluation for in vitro anti-tubercular activity. Ayu 2014;35:98-102.  Back to cited text no. 10
[PUBMED]  [Full text]  
Gupta P, Bhatter P, D'souza D, Tolani M, Daswani P, Tetali P, et al. Evaluating the anti Mycobacterium tuberculosis activity of Alpinia galanga (L.) willd. Axenically under reducing oxygen conditions and in intracellular assays. BMC Complement Altern Med 2014;14:84.  Back to cited text no. 11
Jha DK, Panda L, Lavanya P, Ramaiah S, Anbarasu A. Detection and confirmation of alkaloids in leaves of Justicia adhatoda and bioinformatics approach to elicit its anti-tuberculosis activity. Appl Biochem Biotechnol 2012;168:980-90.  Back to cited text no. 12
Zhang Y, Amzel LM. Tuberculosis drug targets. Curr Drug Targets 2002;3:131-54.  Back to cited text no. 13
Moraes GL, Gomes GC, Monteiro de Sousa PR, Alves CN, Govender T, Kruger HG, et al. Structural and functional features of enzymes of Mycobacterium tuberculosis peptidoglycan biosynthesis as targets for drug development. Tuberculosis (Edinb) 2015;95:95-111.  Back to cited text no. 14
Hosen MI, Tanmoy AM, Mahbuba DA, Salma U, Nazim M, Islam MT, et al. Application of a subtractive genomics approach for in silico identification and characterization of novel drug targets in Mycobacterium tuberculosis F11. Interdiscip Sci 2014;6:48-56.  Back to cited text no. 15
Melak T, Gakkhar S. Potential non homologous protein targets of Mycobacterium tuberculosis H37Rv identified from protein-protein interaction network. J Theor Biol 2014;361:152-8.  Back to cited text no. 16
Roberts DM, Personne Y, Ollinger J, Parish T. Proteases in Mycobacterium tuberculosis pathogenesis: Potential as drug targets. Future Microbiol 2013;8:621-31.  Back to cited text no. 17
Schiebel J, Kapilashrami K, Fekete A, Bommineni GR, Schaefer CM, Mueller MJ, et al. Structural basis for the recognition of mycolic acid precursors by KasA, a condensing enzyme and drug target from Mycobacterium tuberculosis. J Biol Chem 2013;288:34190-204.  Back to cited text no. 18
Shahine A, Prasetyoputri A, Rossjohn J, Beddoe T. A structural characterization of the isoniazid Mycobacterium tuberculosis drug target, Rv2971, in its unliganded form. Acta Crystallogr F Struct Biol Commun 2014;70:572-7.  Back to cited text no. 19
Lu Y, Qiao F, You XF, Yang XY. Research progresses of Mycobacterium tuberculosis cytochrome P450s as a potential drug target. Yao Xue Xue Bao 2014;49:427-34.  Back to cited text no. 20
Mowbray SL, Kathiravan MK, Pandey AA, Odell LR. Inhibition of glutamine synthetase: A potential drug target in Mycobacterium tuberculosis. Molecules 2014;19:13161-76.  Back to cited text no. 21
Taira J, Ito T, Nakatani H, Umei T, Baba H, Kawashima S, et al. In silico structure-based drug screening of novel antimycobacterial pharmacophores by DOCK-GOLD tandem screening. Int J Mycobacteriol 2017;6:142-8.  Back to cited text no. 22
[PUBMED]  [Full text]  
Bunalema L, Kirimuhuzya C, Tabuti JR, Waako P, Magadula JJ, Otieno N, et al. The efficacy of the crude root bark extracts of Erythrina abyssinica on rifampicin resistant Mycobacterium tuberculosis. Afr Health Sci 2011;11:587-93.  Back to cited text no. 23
Luo X, Pires D, Aínsa JA, Gracia B, Mulhovo S, Duarte A, et al. Antimycobacterial evaluation and preliminary phytochemical investigation of selected medicinal plants traditionally used in Mozambique. J Ethnopharmacol 2011;137:114-20.  Back to cited text no. 24
Tan MA, Takayama H, Aimi N, Kitajima M, Franzblau SG, Nonato MG. Antitubercular triterpenes and phytosterols from Pandanus tectorius soland. Var. Laevis. J Nat Med 2008;62:232-5.  Back to cited text no. 25
Wächter GA, Franzblau SG, Montenegro G, Hoffmann JJ, Maiese WM, Timmermann BN. Inhibition of Mycobacterium tuberculosis growth by saringosterol from Lessonia nigrescens. J Nat Prod 2001;64:1463-4.  Back to cited text no. 26
Jena L, Harinath BC. Anti-tuberculosis therapy: Urgency for new drugs and integrative approach. Biomed Biotechnol Res J 2018;2:16-9.  Back to cited text no. 27
  [Full text]  
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and autoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-91.  Back to cited text no. 28
Trott O, Olson AJ. AutoDock vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455-61.  Back to cited text no. 29
DeLano WL. The PyMOL Molecular Graphics System. San Carlos, CA: DeLano Scientific LLC; 2002.  Back to cited text no. 30
Luo H, Lin Y, Gao F, Zhang CT, Zhang R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res 2014;42:D574-80.  Back to cited text no. 31
Mi H, Muruganujan A, Casagrande JT, Thomas PD. Large-scale gene function analysis with the PANTHER classification system. Nat Protoc 2013;8:1551-66.  Back to cited text no. 32
Oliveros JC. VENNY. An Interactive Tool for Comparing Lists with Venn's Diagrams; 2007-2015. Available from: http://bioinfogp.cnb.csic.es/tools/venny/index.html. [Last accessed on 2018 Sep 15].  Back to cited text no. 33
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403-10.  Back to cited text no. 34
Sharma A, Dutta P, Sharma M, Rajput NK, Dodiya B, Georrge JJ, et al. BioPhytMol: A drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts. J Cheminform 2014;6:46.  Back to cited text no. 35
Mehta S, Mehta SS, Patyal P, Bhatnagar S. Herbal drugs as anti-tuberculosis agents. Ayurvedic Herb Med 2015;5:1895-900.  Back to cited text no. 36
Ibekwe NN, Ameh SJ. Plant natural products research in tuberculosis drug discovery and development: A situation report with focus on Nigerian biodiversity. Afr J Biotechnol 2014;13:2307-20.  Back to cited text no. 37
Jiménez-Arellanes MA, Gutiérrez-Rebolledo G, Rojas-Tomé S, Meckes-Fischer M. Medicinal plants, an important reserve of antimycobacterial and antitubercular drugs: An update. J Infect Dis Ther 2014;2:185.  Back to cited text no. 38
Santhosh RS, Suriyanarayanan B. Plants: A source for new antimycobacterial drugs. Planta Med 2014;80:9-21.  Back to cited text no. 39
Arya V. A review on anti-tubercular plants. Int J Pharm Tech Res 2011;3:872-80.  Back to cited text no. 40
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009;37:W623-33.  Back to cited text no. 41
Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007;35:W407-10.  Back to cited text no. 42
Wallner B, Fang H, Elofsson A. Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller. Proteins 2003;53 Suppl 6:534-41.  Back to cited text no. 43
Laskowski RA, MacArthur MW, Thornton JM. PROCHECK: Validation of protein structure coordinates. International Tables for Crystallography. Vol. F, ch. 21.4. 2012; p. 684-7.  Back to cited text no. 44
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 2015;10:845-58.  Back to cited text no. 45
UniProt Consortium T. UniProt: The universal protein knowledgebase. Nucleic Acids Res 2018;46:2699.  Back to cited text no. 46
Wass MN, Kelley LA, Sternberg MJ 3DLigandSite: Predicting ligand-binding sites using similar structures. Nucleic Acids Res 2010;38:W469-73.  Back to cited text no. 47
Tayebeh F. Effectiveness assessment of protein drugs and vaccines through in silico analysis. Biomed Biotechnol Res J 2018;2:106-11.  Back to cited text no. 48
Ndzi EN, Indu Viswanath AN, Adzemye NG, Tamgue O, Nsongka MV, Nair AS, et al. Upregulated bovine tuberculosis microRNAs trigger oncogenic pathways: An in silico perception. Int J Mycobacteriol 2019;8:70-4.  Back to cited text no. 49
[PUBMED]  [Full text]  
Jeanes C, O'Grady J. Diagnosing tuberculosis in the 21st century – Dawn of a genomics revolution? Int J Mycobacteriol 2016;5:384-91.  Back to cited text no. 50
  [Full text]  
Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30.  Back to cited text no. 51
Schaeffer ML, Agnihotri G, Kallender H, Brennan PJ, Lonsdale JT. Expression, purification, and characterization of the Mycobacterium tuberculosis acyl carrier protein, AcpM. Biochim Biophys Acta 2001;1532:67-78.  Back to cited text no. 52
Ely F, Nunes JE, Schroeder EK, Frazzon J, Palma MS, Santos DS, et al. The Mycobacterium tuberculosis Rv2540c DNA sequence encodes a bifunctional chorismate synthase. BMC Biochem 2008;9:13.  Back to cited text no. 53
Yang Q, Yu K, Yan L, Li Y, Chen C, Li X. Structural view of the regulatory subunit of aspartate kinase from Mycobacterium tuberculosis. Protein Cell 2011;2:745-54.  Back to cited text no. 54
Gavrish E, Sit CS, Cao S, Kandror O, Spoering A, Peoples A, et al. Lassomycin, a ribosomally synthesized cyclic peptide, kills Mycobacterium tuberculosis by targeting the ATP-dependent protease ClpC1P1P2. Chem Biol 2014;21:509-18.  Back to cited text no. 55
Due AV, Kuper J, Geerlof A, von Kries JP, Wilmanns M. Bisubstrate specificity in histidine/tryptophan biosynthesis isomerase from Mycobacterium tuberculosis by active site metamorphosis. Proc Natl Acad Sci U S A 2011;108:3554-9.  Back to cited text no. 56
Bellinzoni M, Buroni S, Pasca MR, Guglierame P, Arcesi F, De Rossi E, et al. Glutamine amidotransferase activity of NAD+ synthetase from Mycobacterium tuberculosis depends on an amino-terminal nitrilase domain. Res Microbiol 2005;156:173-7.  Back to cited text no. 57
Kumar A, Casey A, Odingo J, Kesicki EA, Abrahams G, Vieth M, et al. A high-throughput screen against pantothenate synthetase (PanC) identifies 3-biphenyl-4-cyanopyrrole-2-carboxylic acids as a new class of inhibitor with activity against Mycobacterium tuberculosis. PLoS One 2013;8:e72786.  Back to cited text no. 58
Yang K, Chang JY, Cui Z, Li X, Meng R, Duan L, et al. Structural insights into species-specific features of the ribosome from the human pathogen Mycobacterium tuberculosis. Nucleic Acids Res 2017;45:10884-94.  Back to cited text no. 59
Abrahams KA, Cox JA, Fütterer K, Rullas J, Ortega-Muro F, Loman NJ, et al. Inhibiting mycobacterial tryptophan synthase by targeting the inter-subunit interface. Sci Rep 2017;7:9430.  Back to cited text no. 60
Shen H, Yang Y, Wang F, Zhang Y, Ye N, Xu S, et al. Characterization of the putative tryptophan synthase beta-subunit from Mycobacterium tuberculosis. Acta Biochim Biophys Sin (Shanghai) 2009;41:379-88.  Back to cited text no. 61


  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3], [Table 4]


Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

  In this article
Results and Disc...
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded145    
    Comments [Add]    

Recommend this journal