International Journal of Evolutionary BiologyVolume 2014 (2014), Article ID 284170, 8 pageshttp://dx.doi.org/10.1155/2014/284170

Research Article

Mycobacterium tuberculosis H37Rv: In Silico Drug Targets Identification by Metabolic Pathways Analysis

Asad Amir, Khyati Rana, Arvind Arya, Neelesh Kapoor, Hirdesh Kumar, and Mohd Asif Siddiqui

Department of Biotechnology, Meerut Institute of Engineering and Technology, N.H. 58, Delhi-Roorkee Highway, Baghpat Road Bypass Crossing, Meerut 250005, India

Received 10 May 2013; Revised 26 October 2013; Accepted 26 December 2013; Published 25 February 2014

Academic Editor: Graziano Pesole

Copyright © 2014 Asad Amir et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Mycobacterium tuberculosis (Mtb) is a pathogenic bacteria species in the genus Mycobacterium and the causative agent of most cases of tuberculosis. Tuberculosis (TB) is the leading cause of death in the world from a bacterial infectious disease. This antibiotic resistance strain lead to development of the new antibiotics or drug molecules which can kill or suppress the growth of Mycobacterium tuberculosis. We have performed an in silico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen Mycobacterium tuberculosis (H37Rv). Novel efforts in developing drugs that target the intracellular metabolism of M. tuberculosis often focus on metabolic pathways that are specific to M. tuberculosis. We have identified five unique pathways for Mycobacterium tuberculosis having a number of 60 enzymes, which are nonhomologous to Homo sapiens protein sequences, and among them there were 55 enzymes, which are nonhomologous to Homo sapiens protein sequences. These enzymes were also found to be essential for survival of the Mycobacterium tuberculosis according to the DEG database. Further, the functional analysis using Uniprot showed involvement of all the unique enzymes in the different cellular components.

1. Introduction

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), remains a major health threat. Each year, 8 million new TB cases appear and 2 million individuals die of TB [1]. Further, about half a million new multidrug resistant TB cases are estimated to occur every year [2]. The existing drugs, although of immense value in controlling the disease to the extent that is being done today, have several shortcomings, the most important of them being the emergence of drug resistance rendering even the front-line drugs inactive. In addition, drugs such as rifampicin have high levels of adverse effects making them prone to patient incompliance. Another important problem with most of the existing antimycobacterials is their inability to act upon latent forms of the bacillus. In addition to these problems, the vicious interactions between the HIV (human immunodeficiency virus) and TB have led to further challenges for antitubercular drug discovery [3].

Recently, genome-scale metabolic network reconstructions for different organisms have enabled systematic analyses of metabolic functions and predictions of metabolism-related phenotypes. By collecting all possible biochemical reactions for specific organisms, different groups have reconstructed metabolic networks for bacteria, for example, Escherichia coli, Helicobacter pylori, and Chromohalobacter salexigens, eukaryotic microorganisms, mice, and even humans [4–6]. The website of the Systems Biology Research Group at the University of California, San Diego (http://gcrg.ucsd.edu/), provides a continuously updated list of genome-scale metabolic network reconstructions. Analysis of metabolic networks can provide insights into an organism’s ability to grow under specific conditions. For example, given a specific set of nutrient conditions, flux balance analysis (FBA) of metabolic networks can accurately predict microbial cellular growth rates. In a recent work, a group of researchers used an approximate representation of in-host nutrient availability inferred from the literature to simulate the in-host metabolism of Salmonella typhimurium [7]. Moreover, metabolic network analyses can then be used to identify organism-specific essential genes by predicting the attenuation of microbial growth of specific deletion mutants [8–10].

The computational approach has been used to investigate novel drug targets in other pathogenic organisms such as Pseudomonas aeruginosa and in Helicobacter pylori [5, 11].

As most currently known, antibacterials are essentially inhibitors of certain bacterial enzymes; all enzymes specific to bacteria can be considered as potential drug targets [12]. In this study, we have adopted a strategy for comparative metabolic pathway analysis to find out some potential targets against M. tuberculosis (H37Rv). Only those enzymes which show unique properties than the host were selected as the target. Metabolic genes that are essential for pathogen growth but are not present in humans constitute actual and potential drug targets.

2. Materials and Methods

KEGG (Kyoto Encyclopedia of Gene and Genome) (http://www.genome.jp/pathways.html) [13] pathway database was used as a source of metabolic pathway information. Metabolic pathway identification numbers of the host H. sapiens and the pathogen M. tuberculosis (H37Rv) were extracted from the KEGG database. Pathways which do not appear in the host but are present in the pathogen according to KEGG database have been identified as pathways unique to M. tuberculosis as in comparison to the host H. sapiens. Enzymes in these unique pathways as well as enzymes involved in other metabolic pathways under carbohydrate metabolism, energy metabolism, lipid metabolism, nucleotide metabolism, amino acid metabolism, metabolism of other amino acids, and glycan biosynthesis were identified from the KEGG database. The corresponding protein sequences of enzymes involved in unique pathways were identified and their protein sequences were retrieved in FASTA format from KEGG database.

The unique enzymes were further analyzed for essentiality to pathogen by DEG (Database of Essential Genes) database (http://tubic.tju.edu.cn/deg/) [14], and considered cutoff score was >100 to enhance the specificity of enzyme in M. tuberculosis.

The obtained targets genes were further analyzed by UniProt (Universal Protein Resource) (http://www.uniprot.org/) database to find out their functions. This is required to find out the surface membrane proteins which could be probable vaccine targets.

3. Results and Discussion

3.1. Identification of Unique Pathways and Potential Drug Targets

Tuberculosis (TB) is a major cause of illness and death worldwide, especially in Asia and Africa. Globally, 9.2 million new cases and 1.7 million deaths from TB occurred in 2006, of which 0.7 million cases and 0.2 million deaths were in HIV-positive people [2]. The existing drugs have several shortcomings, the most important of them being the emergence of drug resistance.

No new anti-Mtb drugs have been developed for well over 20 years. In view of the increasing development of resistance to the current leading anti-Mtb drugs, novel strategies are desperately needed to avert the “global catastrophe” forecast by the WHO (World Health Organization). Therefore, computational approach for drug targets identification, specifically for Mtb, can produce a list of reliable targets very rapidly. These methods have the advantage of speed and low cost and, even more importantly, provide a systems view of the whole microbe at a time. Since it is generally believed that the genomes of bacteria contain genes both with and without homologues to the human host. Using computational approach for target identification it is very quick to produce a desirable list.

In the present study, 5 unique pathways, C5-branched dibasic acid metabolism, carbon fixation pathways in prokaryotes, methane metabolism, lipopolysaccharide biosynthesis, and peptidoglycan biosynthesis with 60 new nonhomologous targets were identified through in silico comparative metabolic pathway analysis of Homo sapiens and M. tuberculosis H37Rv using KEGG database. Pathways which are not present in the Homo sapiens but present in the Mycobacterium are designated as unique pathways. Design and targeting inhibitors against these nonhomologous sequences could be the better approach for generation of new drugs. Thus total 5 unique metabolic pathways have been taken in M. tuberculosis (Table 1).

Table 1: Unique pathways of M. tuberculosis when compared to H. sapiens.

S. no.
Pathway name
Human
Mycobacterium  tuberculosis H37Rv

1
Carbohydrate Metabolism

1.1
    C5-Branched dibasic acid metabolism
Absent
Present

2
Energy Metabolism

2.1
    Photosynthesis
Absent
Absent

2.2
    Carbon fixation pathways in prokaryotes
Absent
Present

2.3
    Methane metabolism
Absent
Present

3
Lipid Metabolism

3.1
    Fatty acid elongation in mitochondria
Present
Absent

3.2
    Sphingolipid metabolism
Present
Absent

3.3
    Arachidonic acid metabolism
Present
Absent

4
Nucleotide Metabolism
All Present
All Present

5
Amino Acid Metabolism
All Present
All Present

6
Metabolism of Other Amino Acids
All Present
All Present

6.1
    Phosphonate and phosphinate metabolism
Absent
Absent

7
Glycan Biosynthesis and Metabolism

7.1
    N-Glycan biosynthesis
Present
Absent

7.2
    Various types of N-glycan biosynthesis

Absent

7.3
    Mucin type O-Glycan biosynthesis
Present
Absent

7.4
    Other types of O-glycan biosynthesis
Present
Absent

7.5
    Glycosaminoglycan biosynthesis—chondroitin sulfate
Present
Absent

7.6
    Glycosaminoglycan biosynthesis—heparan sulfate
Present
Absent

7.7
    Glycosaminoglycan biosynthesis—keratan sulfate
Present
Absent

7.8
    Glycosaminoglycan degradation
Present
Absent

7.9
    Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
Present
Absent

7.10
    Glycosphingolipid biosynthesis—lacto and neolacto series
Present
Absent

7.11
    Glycosphingolipid biosynthesis—globo series
Present
Absent

7.12
    Glycosphingolipid biosynthesis—ganglio series
Present
Absent

7.13
    Lipopolysaccharide biosynthesis
Absent
Present

7.14
    Peptidoglycan biosynthesis
Absent
Present

7.15
    Other Glycan degradation
Present
Absent

3.2. Identification of Essential Genes

Essential genes are those indispensable for the survival of an organism, and their functions are, therefore, considered a foundation of life. Total 55 enzymes out of all were found to be essential for M. tuberculosis life cycle (Table 2). These targets were found to be potential targets and could be considered for rational drug design. Using metabolic pathway information as the starting point for the identification of potential targets has its advantages as each step in the pathway is validated as the essential function for the survival of the bacterium.

Table 2: Essential enzymes using DEG.

S. no.
Entry no.
Protein name
Essential enzyme

1.
Rv1820
Acetolactate synthase
Yes

2.
Rv0951
Succinyl-CoA synthetase subunit beta
Yes

3.
Rv2987c
Isopropylmalate isomerase small subunit
Yes

4.
Rv1475c
Aconitate hydratase (EC: 4.2.1.3)
Yes

5.
Rv0066c
Isocitrate dehydrogenase (EC: 1.1.1.42)
Yes

6.
Rv2454c
2-Oxoglutarate ferredoxin oxidoreductase subunit beta (EC: 1.2.7.3)
Yes

7.
Rv1240
Malate dehydrogenase (EC: 1.1.1.37)
Yes

8.
Rv1098c
Fumarate hydratase (EC: 4.2.1.2)
Yes

9.
Rv0247c
Fumarate reductase iron-sulfur subunit (EC: 1.3.99.1)
Yes

10.
Rv3356c
Bifunctional 5,10-methylene-tetrahydrofolate dehydrogenase/5,10-methylene-tetrahydrofolate Cyclohydrolase (EC: 1.5.1.5 3.5.4.9)
Yes

11.
Rv0951
Succinyl-CoA synthetase subunit beta (EC: 6.2.1.5)
Yes

12.
Rv0904c
Putative acetyl-coenzyme A carboxylase carboxyl transferase subunit beta (EC: 6.4.1.2)
Yes

13.
Rv0973c
Acetyl-/propionyl-coenzyme A carboxylase subunit alpha (EC: 6.3.4.14)
Yes

14.
Rv1492
Methylmalonyl-CoA mutase small subunit (EC: 5.4.99.2)
Yes

15.
Rv3667
Acetyl-CoA synthetase (EC: 6.2.1.1)
Yes

16.
Rv0409
Acetate kinase (EC: 2.7.2.1)
Yes

17.
Rv0408
Phosphate acetyltransferase (EC: 2.3.1.8)
Yes

18.
Rv0243
Acetyl-CoA acetyltransferase (EC: 2.3.1.9)
Yes

19.
Rv0860
Fatty oxidation protein FadB
Yes

20.
Rv3667
Acetyl-CoA synthetase (EC: 6.2.1.1)
Yes

21.
Rv0373c
Carbon monoxyde dehydrogenase large subunit (EC: 1.2.99.2)
No

22.
Rv2900c
Formate dehydrogenase H (EC: 1.2.1.2)
No

23.
Rv1023
Phosphopyruvate hydratase (EC: 4.2.1.11)
Yes

24.
Rv1240
Malate dehydrogenase (EC: 1.1.1.37)
Yes

25.
Rv0070c
Serine hydroxymethyltransferase (EC: 2.1.2.1)
Yes

26.
Rv2205c
Hypothetical protein
Yes

27.
Rv0761c
Zinc-containing alcohol dehydrogenase NAD dependent AdhB (EC: 1.1.1.1)
Yes

28.
Rv0489
Phosphoglyceromutase (EC: 5.4.2.1)
Yes

29.
Rv0363c
Fructose-bisphosphate aldolase (EC: 4.1.2.13)
Yes

30.
Rv2029c
Phosphofructokinase PfkB (phosphohexokinase) (EC: 2.7.1.—)
Yes

31.
Rv1908c
Catalase-peroxidase-peroxynitritase T KatG (EC: 1.11.1.6)
Yes

32.
Rv0070c
Serine hydroxymethyltransferase (EC: 2.1.2.1)
Yes

33.
Rv0728c
D-3-phosphoglycerate dehydrogenase (EC: 1.1.1.95)
Yes

34.
Rv0505c
Phosphoserine phosphatase (EC: 3.1.3.3)
Yes

35.
Rv0884c
Phosphoserine aminotransferase (EC: 2.6.1.52)
Yes

36.
Rv0409
Acetate kinase (EC: 2.7.2.1)
Yes

37.
Rv0408
Phosphate acetyltransferase (EC: 2.3.1.8)
Yes

38.
Rv3667
Acetyl-CoA synthetase (EC: 6.2.1.1)
Yes

39.
Rv2611c
Lipid A biosynthesis lauroyl acyltransferase (EC: 2.3.1. —)
Yes

40.
Rv0114
D-alpha,beta-D-heptose-1,7-biphosphate phosphatase (EC: 2. —.—.—)
Yes

41.
Rv0113
Phosphoheptose isomerase (EC: 5. —.—.—)
Yes

42.
Rv1315
UDP-N-acetylglucosamine 1-carboxyvinyltransferase (EC: 2.5.1.7)
Yes

43.
Rv0482
UDP-N-acetylenolpyruvoylglucosamine reductase (EC: 1.1.1.158)
Yes

44.
Rv2152c
UDP-N-acetylmuramate-L-alanine ligase (EC: 6.3.2.8)
Yes

45.
Rv2155c
UDP-N-acetylmuramoyl-L-alanyl-D-glutamate synthetase (EC: 6.3.2.9)
Yes

46.
Rv2157c
UDP-N-acetylmuramoylalanyl-D-glutamyl-2,6-diaminopimelate-D-alanyl-D-alanyl ligase MurF
Yes

47.
Rv2156c
Phospho-N-acetylmuramoyl-pentapeptide-transferase (EC: 2.7.8.13)
Yes

48.
Rv2153c
Undecaprenyldiphospho-muramoylpentapeptide beta-N-acetylglucosaminyltransferase (EC: 2.4.1.227)
Yes

49.
Rv2911
D-alanyl-D-alanine carboxypeptidase (EC: 3.4.16.4)
No

50.
Rv2981c
D-alanyl-alanine synthetase A (EC: 6.3.2.4)
Yes

51.
Rv2136c
Undecaprenyl pyrophosphate phosphatase (EC: 3.6.1.27)
Yes

52.
Rv2911
D-alanyl-D-alanine carboxypeptidase (EC: 3.4.16.4)
No

53.
Rv2158c
UDP-N-acetylmuramoylalanyl-D-glutamate-2,6-diaminopimelate ligase (EC: 6.3.2.13)
Yes

54.
Rv2157c
UDP-N-acetylmuramoylalanyl-D-glutamyl-2,6-diaminopimelate-D-alanyl-D-alanyl ligase MurF
Yes

55.
Rv2156c
Phospho-N-acetylmuramoyl-pentapeptide-transferase (EC: 2.7.8.13)
Yes

56.
Rv2153c
Undecaprenyldiphospho-muramoylpentapeptide beta-N-acetylglucosaminyltransferase (EC: 2.4.1.227)
Yes

57.
Rv3910
Transmembrane protein
Yes

58.
Rv0016c
Penicillin-binding protein PbpA
Yes

59.
Rv2163c
Penicillin-binding membrane protein PbpB
Yes

60.
Rv2911
D-alanyl-D-alanine carboxypeptidase (EC: 3.4.16.4)
No

3.3. Identification of Drug Target’s Functions Using UniProt

The subcellular localization analysis of all supposed essential and unique enzymes of M. tuberculosis were evaluated by UniProt server. As it was suggested that, membrane associated protein could be the better target for developing vaccines. After functional analysis unique enzymes involved in cellular components like cell wall, cytoplasm, extracellular region, plasma membrane, and so forth, their biological processes and their functions have been retrieved (Table 3).

Table 3: Shows function of all Essential proteins.

S. no.
Accession. no.
Celllular component
Biological process
Molecular function

1
Rv1820
Not known
Branched chain family amino acid biosynthetic process
Acetolactate synthase activity, magnesium ion binding, thiamine pyrophosphate binding

2.
Rv0951
Cell wall, cytosol
Growth, tricarboxylic acid cycle
ATP binding, metal ion binding, succinate-CoA ligase (ADP-forming) activity

3.
Rv2987c
Plasma membrane, 3-isopropylmalate dehydratase complex
Growth, leucine biosynthetic process
3-Isopropylmalate dehydratase activity

4.
Rv1475c
Cell wall, cytosol, extracellular region, plasma membrane
Growth, response to iron ion
4 iron, 4 sulfur cluster binding, aconitate hydratase activity, iron-responsive element binding

5.
Rv0066c
Cytosol, extracellular region, plasma membrane
Tricarboxylic acid cycle
NAD binding, isocitrate dehydrogenase (NADP+) activity, magnesium ion binding, protein homodimerization activity

6.
Rv2454c
Cell wall, cytosol
Oxidation-reduction process
2-Oxoglutarate synthase activity, magnesium ion binding, thiamine pyrophosphate binding

7.
Rv1240
Cytosol, plasma membrane
Glycolysis, malate metabolic process, tricarboxylic acid cycle
L-malate dehydrogenase activity, binding

8.
Rv1098c
Cytosol, extracellular region, plasma membrane
Growth, tricarboxylic acid cycle
Fumarate hydratase activity

9.
Rv0247c
Plasma membrane
Tricarboxylic acid cycle
Electron carrier activity, iron-sulfur cluster binding, succinate dehydrogenase activity

10.
Rv3356c
Extracellular region, plasma membrane
Folic acid-containing compound biosynthetic process, growth, histidine biosynthetic process, methionine biosynthetic process, one-carbon metabolic process, oxidation-reduction process, purine nucleotide biosynthetic process
Binding, methenyltetrahydrofolate cyclohydrolase activity, methylenetetrahydrofolate dehydrogenase (NADP+) activity

11.
Rv0951
Cell wall, cytosol
Growth, tricarboxylic acid cycle
ATP binding. metal ion binding, succinate-CoA ligase (ADP-forming) activity

12.
Rv0904c
Acetyl-CoA carboxylase complex, plasma membrane
Mycolic acid biosynthetic process
ATP binding, acetyl-CoA carboxylase activity, protein binding

13.
Rv0973c
Plasma membrane
Growth
ATP binding, biotin binding, biotin carboxylase activity

14.
Rv1492
Cell wall, cytosol, plasma membrane
Lactate fermentation to propionate and acetate, propionate metabolic process, methylmalonyl pathway
Cobalamin binding, metal ion binding, methylmalonyl-CoA mutase activity

15.
Rv3667
Cell wall, plasma membrane
Not known
AMP binding, ATP binding, acetate-CoA ligase activity

16.
Rv0409
Cytoplasm
Organic acid metabolic process
ATP binding, acetate kinase activity

17.
Rv0408
Cytoplasm, extracellular region
Not known
Phosphate acetyltransferase activity

18.
Rv0243
Cytosol, plasma membrane
Growth of symbiont in host cell
Acetyl-CoA C-acyltransferase activity

19.
Rv0860
Cytosol, plasma membrane
Fatty acid metabolic process, oxidation-reduction process
Coenzyme binding, oxidoreductase activity

20.
Rv3667
Cell wall, plasma membrane
Not known
AMP binding, ATP binding, acetate-CoA ligase activity

21.
Rv1023
Cell surface, extracellular region, phosphopyruvate hydratase complex, plasma membrane
Glycolysis, growth
Magnesium ion binding, phosphopyruvate hydratase activity

22.
Rv1240
Cytosol, plasma membrane
Glycolysis, malate metabolic process, tricarboxylic acid cycle
L-malate dehydrogenase activity, binding

23.
Rv0070c
Not known
Not known
Not known

24.
Rv2205c
Not known
Organic acid phosphorylation
Glycerate kinase activity

25.
Rv0761c
Oxidation-reduction process
Cytoplasm, plasma membrane
alcohol dehydrogenase (NAD) activity, zinc ion binding

26.
Rv0489
Plasma membrane
Glycolysis
Phosphoglycerate mutase activity

27.
Rv0363c
Extracellular region, plasma membrane
Glycolysis, protein homotetramerization
Fructose-bisphosphate aldolase activity, zinc ion binding

28.
Rv2029c
Not known
Carbohydrate metabolic process
Kinase activity, phosphotransferase activity, alcohol group as acceptor

29.
Rv1908c
Not known
Hydrogen peroxide catabolic process, oxidation-reduction process, response to antibiotic
Catalase activity, heme binding

30.
Rv0070c
Not Known
Not Known
Not known

31.
Rv0728c
Not Known
Oxidation-reduction process
NAD binding, phosphoglycerate dehydrogenase activity

32.
Rv0505c
Integral to plasma membrane
Not Known
Metal ion binding, phosphatase activity

33.
Rv0884c
Cytoplasm, extracellular region, plasma membrane
L-serine biosynthetic process, growth, pyridoxine biosynthetic process
O-phospho-L-serine: 2-oxoglutarate aminotransferase activity, pyridoxal phosphate binding

34.
Rv0409
Cytoplasm
Organic acid metabolic process
ATP binding, acetate kinase activity

35.
Rv0408
Cytoplasm, extracellular region
Not known
Phosphate acetyltransferase activity

36.
Rv3667
Cell wall, plasma membrane
Not known
AMP binding, ATP binding, acetate-CoA ligase activity

37.
Rv2611c
Integral to membrane, plasma membrane
Glycolipid biosynthetic process, growth, lipopolysaccharide core region biosynthetic process
Acyltransferase activity

38.
Rv0114
Cytoplasm
Carbohydrate metabolic process, histidine biosynthetic process
Histidinol-phosphatase activity

39.
Rv0113
Cytoplasm
Carbohydrate metabolic process
D-sedoheptulose 7-phosphate isomerase activity, metal ion binding, sugar binding

40.
Rv1315
Cytoplasm
UDP-N-acetylgalactosamine biosynthetic process, cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
UDP-N-acetylglucosamine 1-carboxyvinyltransferase activity

41.
Rv0482
Cytoplasm
Cell cycle, cell division, cellular cell wall organization, oxidation-reduction process, peptidoglycan biosynthetic process, regulation of cell shape
UDP-N-acetylmuramate dehydrogenase activity, flavin adenine dinucleotide binding

42.
Rv2152c
Cytoplasm
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, UDP-N-acetylmuramate-L-alanine ligase activity

43.
Rv2155c
Cytosol
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, UDP-N-acetylmuramoylalanine-D-glutamate ligase activity, protein binding

44.
Rv2157c
Cytoplasm
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, UDP-N-acetylmuramoyl-tripeptide-D-alanyl-D-alanine ligase activity, UDP-N-acetylmuramoylalanyl-D-glutamyl-2,6-diaminopimelate-D-alanyl-D-alanine ligase activity

45.
Rv2156c
Integral to membrane, plasma membrane
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
Phospho-N-acetylmuramoyl-pentapeptide-transferase activity

46.
Rv2153c
Plasma membrane
Cell cycle, cell division, cellular cell wall organization, growth, regulation of cell shape, UDP-N-acetylgalactosamine biosynthetic process, lipid glycosylation, peptidoglycan biosynthetic process
Carbohydrate binding, undecaprenyldiphospho-muramoylpentapeptide beta-N-acetylglucosaminyltransferase activity

47.
Rv2981c
Cell wall, cytoplasm, plasma membrane
Cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, D-alanine-D-alanine ligase activity, metal ion binding

48.
Rv2136c
Integral to membrane, plasma membrane
Cellular cell wall organization, peptidoglycan biosynthetic process, regulation of cell shape, dephosphorylation, response to antibiotic, response to nitrosative stress
Undecaprenyl-diphosphatase activity

49.
Rv2158c
Cytosol, plasma membrane
Cell cycle, cell division, cellular cell wall organization, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, UDP-N-acetylmuramoylalanyl-D-glutamate-2,6-diaminopimelate ligase activity

50.
Rv2157c
Cytoplasm
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
ATP binding, UDP-N-acetylmuramoyl-tripeptide-D-alanyl-D-alanine ligase activity, UDP-N-acetylmuramoylalanyl-D-glutamyl-2,6-diaminopimelate-D-alanyl-D-alanine ligase activity

51.
Rv2156c
Integral to membrane, plasma membrane
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape
Phospho-N-acetylmuramoyl-pentapeptide-transferase activity

52.
Rv2153c
Plasma membrane
Cell cycle, cell division, cellular cell wall organization, growth, peptidoglycan biosynthetic process, regulation of cell shape, UDP-N-acetylgalactosamine biosynthetic process
Carbohydrate binding, undecaprenyldiphospho-muramoylpentapeptide beta-N-acetylglucosaminyltransferase activity

53.
Rv3910
Integral to plasma membrane
Not known
Not known

54.
Rv0016c
Cell septum, cytosol, integral to membrane, plasma membrane
Cellular cell wall organization, peptidoglycan biosynthetic process, regulation of cell shape
Penicillin binding, transferase activity

55.
Rv2163c
Extracellular region
Growth, peptidoglycan-based cell wall biogenesis
Penicillin binding, protein binding

In conclusion, the computational genomic approach has facilitated the search for potential drug targets against M. tuberculosis. Use of the DEG database is more efficient than conventional methods for identification of essential genes and it facilitates the exploratory identification of the most relevant drug targets in the pathogen. The current study can be carried forward to design a drug that can block these drug targets. The microorganisms are fast in gaining resistance to the existing drugs, so designing better and effective drugs needs a faster method.

Appendix

See Tables 1, 2, and 3.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

S. H. E. Kaufmann, “Envisioning future strategies for vaccination against tuberculosis,” Nature Reviews Immunology, vol. 6, no. 9, pp. 699–704, 2006.

B. Greenwood, “A global action plan for the prevention and control of pneumonia,” Bulletin of the World Health Organization, vol. 86, no. 5, p. 322, 2008.

P. Nunn, B. Williams, K. Floyd, C. Dye, G. Elzinga, and M. Raviglione, “Tuberculosis control in the era of HIV,” Nature Reviews Immunology, vol. 5, no. 10, pp. 819–826, 2005.

A. M. Feist and B. Palsson, “The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli,” Nature Biotechnology, vol. 26, no. 6, pp. 659–667, 2008.

A. Dutta, S. K. Singh, P. Ghosh, R. Mukherjee, S. Mitter, and D. Bandyopadhyay, “In silico identification of potential therapeutic targets in the human pathogen Helicobacter pylori,” In Silico Biology, vol. 6, no. 1-2, pp. 43–47, 2006.

Ö. Ates, E. T. Oner, and K. Y. Arga, “Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043,” BMC Systems Biology, vol. 5, article 12, 2011.

A. Raghunathan, J. Reed, S. Shin, B. Palsson, and S. Daefler, “Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction,” BMC Systems Biology, vol. 3, article 38, 2009.

A. M. Feist, C. S. Henry, J. L. Reed et al., “A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information,” Molecular Systems Biology, vol. 3, article 121, 2007.

N. C. Duarte, M. J. Herrgård, and B. Ø. Palsson, “Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model,” Genome Research, vol. 14, no. 7, pp. 1298–1309, 2004.

A. K. Chavali, J. D. Whittemore, J. A. Eddy, K. T. Williams, and J. A. Papin, “Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major,” Molecular Systems Biology, vol. 4, article 177, 2008.

K. R. Sakharkar, M. K. Sakharkar, and V. T. K. Chow, “A novel genomics approach for the identification of drug targets in pathogens, with special reference to Pseudomonas aeruginosa,” In Silico Biology, vol. 4, no. 3, pp. 355–360, 2004.

M. Y. Galperin and E. V. Koonin, “Searching for drug targets in microbial genomes,” Current Opinion in Biotechnology, vol. 10, no. 6, pp. 571–578, 1999.

M. Kanehisa, S. Goto, S. Kawashima, and A. Nakaya, “Thed KEGG databases at GenomeNet,” Nucleic Acids Research, vol. 30, no. 1, pp. 42–46, 2002.

R. Zhang, H.-Y. Ou, and C.-T. Zhang, “DEG: a database of essential genes,” Nucleic Acids Research, vol. 32, pp. D271–D272, 2004.