results and discussion - identification of drug targets from bacterial genomoe

Chapter - V Results and Discussion

_________________________________________________________________________

Identification and Validation of Drug Targets

129

Chapter V

RESULTS AND DISCUSSION

. .

5.1 COMPUTATIONAL APPROACH FOR TARGET IDENTIFICATION

AND VALIDATION

A new strategic approach was designed to identify potential drug

targets from bacterial genome and validate those targets using

computational methods.

Fig. 3: Approach - Target prediction and validation

The above figure represents the steps involved in prediction and

validation of drug targets from microbial genome. The target is predicted by

comparing the bacterial genome with the database of essential genes and

then comparing these predicted essential genes with the human

genes/protein to identify non homologues drug target. Previously


_________________________________________________________________________


130

subtractive genomics approach was used (Sakharkar et al., 2004; Anirban

Dutta et al., 2006) to identify potential drug targets in Pseudomonas

aeruginosa and Helicobacter pylori. In our approach the target identification

and validation process is automated so that the user can submit the input

(genome of a pathogenic microbe) and get the output as target sequences.

The target sequences were analyzed for its functional role using sequence

analysis tools (BLAST and Pfam). The validation of these drug targets were

done by comparing these obtained against the approved and proposed

genes/proteins from the Drugbank database.

Target identification involves two steps as shown in the above Fig. 3.

The essential genes in the microbes are identified by comparing them with

the sequences of Database of Essential Genes. The genes which are

homologous with the DEG are designated as essential genes. The

approach involves comparing each gene from the genome and comparing

them with the DEG database. The genes are compared based on the

specified cut off valve and are stored in a text file. The text file would

contain the gene sequences in fasta format. These matching genes will

become the input for the next step.


_________________________________________________________________________


131

Fig. 8: Screenshot of the web based tool

The input genome sequence in text file format is uploaded in the

region marked as ‘input reference files’. The database or set of sequences

to be compared can be uploaded in the next region marked as ‘file to

compare’. Once you have uploaded both these sequences, on clicking the

submit button the tool compares each sequence from the input file

sequence and compares with all the sequences in the ‘file to compare’

sequences.

By default it compares these two set of sequences with the e-value of

1e–3. These sequences can also be compared based on modifying the

e-value cut offs.


_________________________________________________________________________


132

Step-1: Comparison with Database of essential genes

In the first step of comparison, the input genome sequences of a

bacterial organism are compared with the database of essential genes. The

sequence which matches with the DEG is written separately in a file. These

genes are designated as essential genes to the bacterial species. This

represents a pool of drug targets. Since drug discovery industry focuses on

specific drug targets, these targets have to be drilled down to specific gene

or protein target. This is achieved in the further steps in the algorithm.

Step-2: Comparison with Human Homologue

This step represents excluding human homologues. The target should

not be homologous with humans and hence this step involves comparison

of the essential genes predicted from the previous step with the human

genes or proteins. The sensitivity and allergic reactions to the drug arises

as a result of drug interfering with the host metabolic process apart from

the target organism. If there is high level of stringency implemented in this

step it can avoid lot of pit falls which may arise in the clinical trials. Most of

the drug which has a reasonable biochemical effect often fails in the clinical

testing as they interfere with the host mechanism. This is a very crucial

step in the process of drug design and discovery. Now, the tool has to be

run the second time to compare the input files (predicted bacterial essential

genes) with the human genes sequences. To compare with the human

genes for related sequences, they were downloaded from the NCBI ftp site.


_________________________________________________________________________


133

The essential genes identified in step 1 are compared with the human

genes. The genes which were homologous with the human genes are

excluded in this step. These genes are designated as target genes. These

genes were stored in a separate text file in fasta format.

Fig. 8: Screenshot of the web based tool

Step-3: Comparison with Approved /Predicted Targets

The final step includes comparison of the target genes with the

approved targets or already predicted targets to validate the findings. The

predicted targets were validated by comparing them against the approved

and proposed targets from DrugBank. DrugBank has more than 2500 non-

redundant drug targets. The validation results reveal that most of the

predicted targets using our approach fetched new targets when compared

with the existing target database.


_________________________________________________________________________


134

5.2 APPLICATION DEVELOPMENT

Based on the designed approach a web based application was

developed using Java. The application initially takes the input genome data

and the essential genes in text file format or .ffn file format. Once it

compares, the related sequences are retrieved in a separate text file in a

specific location. These essential genes are then compared with the human

genes.

The comparison was carried out using BLAST program (BLASTall

exe). BLAST executables were downloaded from NCBI site

(ftp://ftp.ncbi.nlm.nih.gov/blast/executables/) and it was customized to

compare the input genome data with the essential genes and thereafter

with the human genes to exclude the homologues. The web-based

application was developed using JSP, Servlets and applying Struts

framework. Using the developed application, the potential targets were

identified for 80 pathogenic organisms and they were validated (Table-1).


_________________________________________________________________________


135

5.2.1 Data Analysis for Target prediction and Validation

Table-1

List of pathogenic organisms and predicted drug targets

S.No.

List of Pathogens

Total number of genes in the genome Number of Potential

Targets Proteins/Coding genes

Proteins from plasmids

1 Acinetobacter baumannii AB0057 3790 11 91

2 Bacillus anthracis 5311 61

3 Bacillus subtilis 4177 162

4 Bacillus_cereus_ATCC_10987 5903 241 114

5 Bacteroides_fragilis_YCH46 4578 47 67

6 Bacteroides_fragilis_NCTC_9434 4184 47 86

7 Bartonella henselae 1488 67

8 Bordetella parapertussis 4185 95

9 Bordetella bronchiseptica 4994 88

10 Bordetella pertussis 3436 88

11 Burkholderia_mallei_ATCC_23344 5024 97

12 Brucella abortus 3000 80

http://www.speedylook.com/Bordetella_parapertussis.html

http://www.speedylook.com/Bordetella_bronchiseptica.html

http://www.speedylook.com/Burkholderia_mallei.html


_________________________________________________________________________


136

S.No.

List of Pathogens




13 Brucella suis 1330 3272 79

14 Chlamydia trachomatis 880 44

15 Chlamydophila_pneumoniae_AR39 1112 43

16 Clostridium botulinum 3548 90

17 Clostridium_difficile_630 3742 11 75

18 Clostridium perfringens 2558 20 76

19 Clostridium_perfringens_ATCC_13124 2876 78

20 Clostridium_tetani_E88 2373 59 71

21 Coxiella_burnetii_RSA_331 1930 45 79

22 Corynebacterium diphtheriae 2272 42

23 Campylobacter_fetus_82-40 1719 92

24 Campylobacter jejuni 1838 93

25 Ehrlichia_chaffeensis_Arkansas 1105 44

26 Escherichia_coli_K_12_substr__MG1655 4149 164

27 Escherichia_coli_UTI89 5021 145 184

28 Francisella tularensis 1754 76

29 Haemophilus influenzae 1792 452

http://www.speedylook.com/Chlamydia_trachomatis.html

http://www.speedylook.com/Chlamydophila_pneumoniae.html

http://www.speedylook.com/Clostridium_perfringens.html

http://130.54.99.137/Corynebacterium/e-diphtheriae/e-diphtheriae.html

http://130.54.99.137/Campylobacter/e-jejuni/e-jejuni.html

http://www.speedylook.com/Haemophilus_influenzae.html


_________________________________________________________________________


137

S.No.

List of Pathogens




30 Helicobacter pylori 1489 242

31 Klebsiella pneumoniae 5425 343 158

32 Listeria monocytogenes 2846 86

33 Listeria_monocytogenes_Clip81459 2766 86

34 Listeria_monocytogenes_HCC23 2974 85

35 Leptospira interrogans 4724 81

36 Leptospira_interrogans_serovar_Copenhageni 3658 81

37 Leptospira_biflexa_serovar_Patoc__Patoc_1__Ames 3667 59 79

38 Mycobacterium leprae 1605 52

39 Mycobacterium tuberculosis 3989 44

40 Mycobacterium_tuberculosis_F11 3941 53

41 Mycobacterium_tuberculosis_H37Ra 4034 53

42 Mycoplasma pneumoniae 689 151

43 Mycoplasma genitalium 475 220

44 Neisseria gonorrhoeae 2002 81

45 Neisseria meningitidis 1917 84

46 Pasteurella multocida 2015 170

http://130.54.99.137/Klebsiella/e-Klebsiella/e-Klebsiella.html

http://www.speedylook.com/Listeria.html

http://130.54.99.137/Leptospira/e-Leptospira/e-icterohaemorrhagiae.html

http://www.speedylook.com/Mycobacterium_leprae.html

http://130.54.99.137/photodata/TB.html

http://www.speedylook.com/Pasteurella_multocida.html


_________________________________________________________________________


138

S.No.

List of Pathogens




47 Proteus mirabilis 3607 55 123

48 Propionibacterium_acnes_KPA171202 2297 61

49 Psendomonas aeruginosa 5566 109

50 Rickettsia_rickettsii_Iowa 1384 62

51 Rickettsia_akari_Hartford 1259 57

52 Salmonella_enterica_Paratypi_ATCC_9150 4093 148

53 Salmonella_enterica_serovar_Typhi_Ty2 4318 148

54 Serratia_proteamaculans_568 4891 51 148

55 Streptococcus_pyogenes_MGAS10270 1986 64

56 Salmonella typhimurium 4423 102 152

57 Staphylococcus_aureus_JH9 2697 29 117

58 Staphylococcus_epidermidis_ATCC_12228 2419 66 85

59 Shigella dyseneriae 4271 231 153

60 Stenotrophomonas_maltophilia_K279a 4386 92

61 Streptococcus pneumoniae 2202 72

62 Treponema pallidum 1028 33

63 Ureaplasma urealyticum 646 53

http://130.54.99.137/Salmonella/e-Salmonella/e-Salmonella.html

http://130.54.99.137/S.epi-e.html


_________________________________________________________________________


139

S.No.

List of Pathogens




64 Vibrio cholerae 3693 121

65 Vibrio_parahaemolyticus 4832 133

66 Vibrio_vulnificus_CMCP6 4472 118

67 Wolinella_succinogenes 2042 116

68 Yersinia enterocolitica 3979 72 147

69 Yersinia pseudotuberculosis 4124 200 136

70 Yersinia pestis_KIM 4054 116 137

71 Clostridium_perfringens str 13 2660 63 76

72 Clostridium_acetobutylicum 3672 176 97

73 Desulfovibrio_vulgaris_DP4 2941 150 76

74 Microcystis_aeruginosa_NIES_843 6312 64

75 Pseudomonas aeruginosa PA7 6286 123

76 Acidobacterium_capsulatum_ATCC_51196 3377 80

77 Chlamydia_trachomatis_L2b_UCH_1_proctitis 874 46

78 Staphylococcus_aureus_COL 2612 3 116

79 Staphylococcus_aureus_Mu50 2697 34 110

80 Staphylococcus aureus subsp. aureus N315 2588 31 114

http://130.54.99.137/photodata/cholerae.html

http://www.speedylook.com/Yersinia_enterocolitica.html

http://130.54.99.137/photodata/pest.html


_________________________________________________________________________


140

The table shows the number of targets predicted for selected

pathogenic organisms. A total of 8171 drug targets were predicted from

these 80 organisms. The minimal number of targets were found in

Treponema pallidum (33 targets) and the maximum target were found in

Haemophilus influenza (452 targets). The predicted targets were organized

in a web based database.

5.2.2 Case scenario – Mycobacterium tuberculosis

Tuberculosis has re-emerged as a global health concern due to

declining efficiency of current therapeutic agents and development of multi

drug resistant strains of Mycobacterium tuberculosis. The currently used

drug combination is no longer considered an eternal solution for treating

the disease. These drugs were originally discovered and formulated in

1940’s and it’s still in the clinician’s prescription. Due to advancements in

genome sequence technologies, the current research has resulted in few

clinical trials. In 1938 the complete genome sequence of M.tuberculosis

was completed. Since then numerous initiatives are carried out using the

genome data to identify TB drug targets.

Growing concern and potential solutions

Nowadays, about 70% of the bacteria that cause infections in

hospitals are resistant to at least one of the antibiotic agents most

commonly used for treatment. Some organisms are resistant to all

approved antibiotics and can only are treated with experimental and

potentially toxic drugs.


_________________________________________________________________________


141

Factors causing resistance

Incorrect use of antibiotics

Patient related factors

Prescriber’s prescription

Use of monotherapy

Commercial promotion

Over the counter sale of antibiotics

Under use of microbiological testing and globalization

Incorrect use of antibiotics such as too short a time, at too low a

dose, at inadequate potency or for the wrong diagnosis always enhances

the likelihood of bacterial resistance to these drugs. Due to the selection

pressure caused by antibiotic use, a large pool of resistant genes has been

created and this antibiotic resistance places an increased burden on

society in terms of high morbidity, mortality and cost. As a whole antibiotic

resistance increases the healthcare cost, increasing the severity of disease

and death rates of few infections. CDC has estimated that some 150 million

prescriptions every year are unnecessary.

The analysis of the Mycobacterium tuberculosis genome data using

our application showed 53 potential targets. These targets were analysed

for their conservity among other organisms using blast searchers and the

results are tabulated.


_________________________________________________________________________


142

Table-2

Validated Drug Targets from Mycobacterium tuberculosis

S. № Target protein Conservity

1. Cell division protein rodA Conserved only among the Mycobacterial organisms.

2. Cell division protein FtsA Conserved only among the Mycobacterial organisms.

3. Replicative DNA helicase Conserved among the Mycobacterial and few other organisms.

4. Dihydroxy-acid dehydratase Conserved only among the Mycobacterial organisms..

5. Fructose-bisphosphate aldolase fba

Conserved among the Mycobacterial and few other organisms.

6. Transcription antitermination protein nusG


7. 50S ribosomal protein L1 rplA Conserved among the Mycobacterial and few other organisms.

8. 30S ribosomal protein S19 rpsS and 50S ribosomal protein L22 rplV


9. 50S ribosomal protein L22 rplV and 30S ribosomal protein S3 rpsC


10. 50S ribosomal protein L24 rplX and 50S ribosomal protein L5 rplE


11. 30S ribosomal protein S8 rpsH Conserved among the Mycobacterial organisms and Streptomyces griseus subsp. griseus NBRC 13350

12. 30S ribosomal protein S5 rpsE Conserved only among the Mycobacterial organisms.


_________________________________________________________________________


143


13. Preprotein translocase subunit secY

Conserved only among the Mycobacterial organisms.

14. Acetyl-CoA carboxylase carboxyl transferase beta subunit accD3


15. lytB-related protein lytB2 Conserved only among the Mycobacterial organisms.

16. Conserved hypothetical protein excinuclease ABC subunit C uvrC


17. Conserved hypothetical protein Conserved only among the Mycobacterial organisms.

18. DNA polymerase subunit III alpha dnaE1

Conserved only among the Mycobacterial and few other organisms.

19. Drug efflux membrane protein Conserved only among the Mycobacterial and few other pathogenic organisms.

20. Initiation factor IF-3 infC Conserved only among the Mycobacterial organisms.

21. Phenylalanyl-tRNA synthetase subunit beta pheT and phenylalanyl-tRNA synthetase subunit alpha pheS


22. Cytotoxin/hemolysin and inorganic polyphosphate/ATP-NAD kinase-


23. ScpA/B family protein and initiation inhibitor protein


24. Preprotein translocase ATPase subunit secA2

Conserved only among the Mycobacterial and few other pathogenic organisms. This target sequence matches with the already approved target sequences from drug bank.

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=253318418&db=Nucleotide&from=2816821&to=2817726&view=gbwithparts&RID=AA5AYC9A01N










_________________________________________________________________________


144


25. UDP-N-acetylmuramate-alanine ligase MurC


26. UDP-N-acetylglucosamine-N-acetylmuramyl- (pentapeptide)pyrophosphoryl-undecaprenol-N- acetylglucosamine transferase MurG


27. Cell division protein ftsW Conserved only among the Mycobacterial organisms.

28. UDP-N-acetylmuramoylalanine-D-glutamate ligase MurD


29. Phospho-N-acetylmuramoyl-pentappeptidetransferase MurX


30. Phospho-N-acetylmuramoyl-pentapeptide-transferase and UDP-N-acetylmuramoylalanyl-D-glutamyl-2,6-diaminopimelate-D-alanyl-D-alanyl ligase


31. UDP-N-acetylmuramoylalanyl-D-glutamate-2,6-diaminopimelat E ligase MurE and UDP-N-acetylmuramoylalanyl-D-glutamyl-2, 6-diaminopimelate-D-alanyl-D-alanyl ligase MurF


32. Methylase MraW, conserved proline rich membrane protein and penicillin-binding membrane protein pbpB


33. Nicotinate-nucleotide adenylyltransferase nadD


34. Ribonuclease E rne and C4-dicarboxylate-transport transmembrane protein dctA



_________________________________________________________________________


145


35. Glyoxalase II and histidyl-tRNA synthetase hiss


36. N utilization substance protein A nusA


37. 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase gcpE


38. Uridylate kinase pyrH Conserved only among the Mycobacterial organisms.

39. 50S ribosomal protein L19 rplS Conserved only among the Mycobacterial organisms and few pathogenic organisms.

40. tRNA (guanine-N(1))-methyltransferase trmD


41. Phosphopantetheine adenylyltransferase kdtB


42. ATP-dependent DNA helicase recG


43. ATP-dependent DNA helicase II uvrD2


44. ATP-dependent DNA helicase II uvrD2


45. Preprotein translocase subunit Conserved only among the Mycobacterial organisms and few pathogenic organisms.

46. Uracil phosphoribosyltransferase upp


47. Error-prone DNA polymerase Conserved only among the Mycobacterial and few other organisms.

48. 1-deoxy-D-xylulose-5-phosphate synthase lytB-related protein lytB1

Conserved among the Mycobacterial organisms and all major pathogenic organisms.





















_________________________________________________________________________


146


49. DNA-directed RNA polymerase subunit alpha rpoA

Conserved only among the Mycobacterial organisms and few pathogenic organisms.

50. translation initiation factor IF-1 infA


51. alpha, alpha-trehalose-phosphate synthase otsA


52. aspartate-semialdehyde dehydrogenase asd


53. Bifunctional UDP-galactofuranosyl transferase glfT and UDP-galactopyranose mutase glf

Conserved only among the Mycobacterial organisms and few pathogenic organisms. UDP-galactopyranose mutase glf matches with the already approved target sequences from drug bank.

Most of the targets predicted from the organism were new compared

to the approved targets from the Drug Bank. Of the 53 targets obtained

from Mycobacterium tuberculosis only two targets (Preprotein translocase

ATPase subunit secA2 and Bifunctional UDP-galactofuranosyl transferase

glfT and UDP-galactopyranose mutase glf were matching with the drug

bank.

Sequencing of bacterial genomes has been progressing with

breathtaking speed. Industrial research is now facing the challenge of

translating this information efficiently into drug discovery. Complete

genome sequences of bacterial organisms have revolutionized the search












_________________________________________________________________________


147

for antibiotics. The search for new antibiotics can be assisted by

computational methods such as homology-based analyses, structural

genomics, motif analyses, protein-protein interactions, and experimental

functional genomics (Loferer, 2000).

The greatest success of computer-aided structure-based drug design

to date is the HIV-1 protease inhibitors that have been approved by the

United States Food and Drug Administration and reached the market

(Wlodawer and Vondrasek., 1998). There have been many successful

computer-assisted molecular design attempts to involve the use of QSAR to

improve activity of lead compounds. An example of the success story is

that of SAR work carried out on antibacterial agent, Norfloxacin (Koga

et al., 1980) that showed 6-fluro derivative of norfloxacin being 500 fold

more potent over nalidixic acid. Other examples of drugs that were

developed using computer assisted drug design include Captopril

(antihypertensive), Crixican (anti-HIV) (Greer et al., 1994), Teveten

(antihypertensive) (Keenan, 1993), Aricept (for Alzheimers disease)

(Kawakami et al., 1996), Trusopt (for Glaucoma) (Greer et al., 1994) and

Zomig (for migraine) (Glen et al., 1995).

Similarly applying CADD concepts for these new targets will results in

development of novel therapeutics as well as to manage multi -drug

resistance. The database developed using the targets will serve as a key

resource to facilitate drug design and discovery.


_________________________________________________________________________


148

The data analysis was performed for a selected list of 80 pathogenic

microbes. The average time taken for screening 2000 gene sequences was

found to be 60 minutes. Though the developed approach was used to

analyze these 80 organisms, a special emphasis was given for the

Mycobacterium tuberculosis as it is a highly drug resistant organism. A

comprehensive data analysis was performed for Mycobacterium

tuberculosis. The predicted targets were analyzed for i ts functional role

using bioinformatics tools. The target sequences like gene name, protein

product, function, EC. NO, pathway were retrieved from the sequence

database and separately populated in a web based database developed

using JSP. This web based database will be made available free for the

educational research institutions to promote discovery and development of

novel drugs.

5.3 DATABASE DEVELOPMENT

Database of bacterial drug targets

The predicted targets from the selected pathogenic organism’s gene

name, protein product, Enzyme Commission Number, function, functional

information were collected and populated in a web based database to act

as a reservoir for drug discovery.


_________________________________________________________________________


149

Database Development Details

Fig. 9: Screenshot of the database input screen

Figure-9 shows the input screen for the database. The input data can

be provided manually or as a single upload in a spreadsheet. The

implementation of AJAX concepts for the search process renders effective

querying methods and retrieves the results faster.


_________________________________________________________________________


150

Figure-10: Screenshot of the database screen

Figures 9 and 10 shows the database input screen and the data

updated in the database. The database also has option to upload the data

directly from a Microsoft spreadsheet.

The present research pursuit was initiated owing to the prevalence of

multi-drug resistance and the pressing need for new drugs. Resistance is

more likely when newly introduced antibiotics are chemically similar to ones

already rendered ineffective. Therefore, new antimicrobial compounds

should ideally have novel mechanisms of action. This demands design and

development of compounds which is different in structure and mechanisms

of action. Hence a new approach in drug design and discovery would

eventually lead to novel class of drugs.

_____