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International Journal of Genomics
Volume 2016, Article ID 7361361, 8 pages
http://dx.doi.org/10.1155/2016/7361361
Research Article

Drug Target Identification and Prioritization for Treatment of Ovine Foot Rot: An In Silico Approach

Gene Regulation Laboratory, National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India

Received 7 April 2016; Accepted 16 May 2016

Academic Editor: Ian Dunham

Copyright © 2016 Abhishek Acharya and Lalit C. Garg. 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

Ovine foot rot is an infection of the feet of sheep, mainly caused by Dichelobacter nodosus. In its virulent form, it is highly contagious and debilitating, causing significant losses in the form of decline in wool growth and quality and poor fertility. Current methods of treatment are ineffective in complete eradication. Effective antibiotic treatment of foot rot is hence necessary to ensure better outcomes during control phases by reduction in culling count and the possibility of carriers of the infection. Using computational approaches, we have identified a set of 297 proteins that are essential to the D. nodosus and nonhomologous with sheep proteins. These proteins may be considered as potential vaccine candidates or drug targets for designing antibiotics against the bacterium. This core set of drug targets have been analyzed for pathway annotation to identify 67 proteins involved in unique bacterial pathways. Choke-point analysis on the drug targets identified 138 choke-point proteins, 29 involved in unique bacterial pathways. Subcellular localization was also predicted for each target to identify the ones that are membrane associated or secreted extracellularly. In addition, a total of 13 targets were identified that are common in at least 10 pathogenic bacterial species.

1. Introduction

Dichelobacter nodosus is a Gram-negative anaerobic bacterium and the main causative organism of ovine foot rot [1]. Foot rot is a contagious and crippling disease affecting the feet of sheep, characterized by a range of symptoms depending on severity, from a nonprogressive interdigital dermatitis (in benign foot rot) to extensive foot infection and separation of hoof from underlying soft tissue, as in case of virulent foot rot [24]. The extent of severity depends on the nature of the particular isolate and the climatic conditions. Typically, moist conditions and temperatures above 10°C are essential for transmission [5]. The disease has significantly affected the sheep industry due to morbidity and decline in wool and meat production [6].

The treatment of foot rot generally involves foot-paring and washing with antiseptic solutions such as 10% zinc-sulphate. Foot-paring is generally carried out to remove the diseased tissue and promote healthy foot-structure [4]; however the effectiveness of paring in treatment of foot rot is questionable [79] and has been shown to be associated with increased incidences of infection [10]. The application of antibiotics, antibacterial sprays, and solutions has seen much better recovery of affected sheep. Although a number of vaccination programs have been successful in Nepal [11], Bhutan [12], and Australia [13], these are examples where only a single serogroup of D. nodosus was infecting the flock. While efforts are underway to develop effective monovalent/bivalent vaccines that can provide adequate cross-protection against multiple strains of D. nodosus[14, 15], it is also necessary to develop effective drug-based strategies for the effective treatment of infected flocks.

Large scale sequencing of complete genomes of various pathogens and their hosts has provided a large amount of sequence data at our disposal which could be of much help in identification of drug targets and development of antibiotics. The genomes of D. nodosus and the host organism, Ovis aries, have been sequenced completely and form the basis of the current in silico analysis. Subtractive genomics approach has been used previously for identification of potential drug targets in different pathogenic bacteria such as Helicobacter pylori [16], Pseudomonas aeruginosa [17, 18], Mycobacterium tuberculosis [19], Aeromonas hydrophila [20], and Clostridium perfringens [21]. Ideally, a drug target should be nonhomologous with host proteins as this would decrease the chances of nonspecific interactions with host proteins and associated side-effects. It is also advantageous if the target protein is known to be “essential” for bacterial survival; any disruption in the functioning of such a protein would lead to death of the bacterial cell. An additional resource that has aided the in silico identification of essential genes in pathogenic organisms is the Database of Essential Genes (DEG) [22]. This database contains records for all the essential genes that are currently known and the records are updated as new essential genes are identified and characterized. At present, the DEG consists of essential genes data for 37 pathogenic bacterial species.

In the present work, we performed in silico analysis utilizing the BLAST [23] utility and DEG to identify putative drug targets in D. nodosus. Further, we have carried out multiple analyses on the list of putative drug targets to classify them on the basis of the pathway/biological process they are involved in and their subcellular localization. Choke-point analyses of the metabolic pathways are a very good method to identify proteins that could be effective drug targets and have been used previously for drug target identification [2426]. The main objective of this study is to identify prioritized groups of proteins which could be attractive drug targets and can be investigated further using computational and experimental drug discovery methods.

2. Materials and Methods

2.1. Subtractive Genomics and Prediction of Essential Genes

For the purpose of analysis, complete genome of D. nodosus (strain VCS1703A17) and its associated annotation data file were downloaded from NCBI database [14]. Essential genes in D. nodosus were predicted by using the Database of Essential Genes (DEG) server [22]. D. nodosus whole genome sequence along with the annotation data was given as input to the server. The server uses the annotation data to identify the genes and performs BLAST search against DEG. Based on previous studies using similar workflow, an Expectation value cut-off of 10−10 and a minimum bit score of 100 were used to shortlist the essential genes [27, 28]. The corresponding protein sequences of all the essential genes were obtained from NCBI and a BLASTP search was performed against a database of sheep protein sequences using an Expectation value cut-off of 10−4 for filtering significant hits. Essential genes that were found to be nonhomologous were shortlisted as the putative drug targets. In addition, the results were screened to remove all hypothetical and unknown proteins.

2.2. Pathway and Subcellular Localization Analysis of Putative Drug Targets

The putative drug targets that were shortlisted were further analyzed using KAAS (KEGG Automated Annotation Server) to obtain information about the different biological processes and metabolic pathways in which the putative drug targets were involved [29]. This online utility provides rapid and high performance functional annotations of genes by performing BLAST comparison against the KEGG genes database. It automatically assigns number to genes and constructs pathways and BRITE hierarchies.

2.3. Choke-Point Analysis

Choke-point analysis of the metabolic pathways of D. nodosus was conducted using the BioCyc database which analyzes the pathways information for D. nodosus to provide a list of choke-point reactions and the respective protein catalyzing the reaction [30]. The list of potential drug targets obtained for Dichelobacter was cross-referenced against this list of choke-point reactions to identify those drug targets that were choke-point proteins in addition to being essential and nonhomologous with host proteins. The results of this analysis were manually cross-checked with KEGG pathways database [31].

2.4. Subcellular Localization

PSORTb server was used to predict the subcellular localization of the potential drug targets in order to analyze the distribution of the drug targets into different compartments of the cell [32]. The results were also cross-checked using the CELLO web server [33].

2.5. Conservation across Multiple Pathogenic Species

Putative drug targets were analyzed to identify the ones that are also essential to 12 other pathogenic bacterial species, namely, Helicobacter pylori 26695, Acinetobacter baylyi ADP1, Haemophilus influenzae Rd KW20, Bacillus subtilis 168, Mycobacterium tuberculosis H37Rv, Staphylococcus aureus N315, Campylobacter jejuni subsp. jejuni ATCC 700819, Francisella novicida U112, Salmonella enterica serovar Typhimurium SL1344, Mycobacterium tuberculosis H37RvIII, Streptococcus pneumoniae, and Vibrio cholerae N16961. BLASTP was performed against the protein sequence database of the aforementioned species present in DEG. An -value of 10−5 and a bit score of 100 were used for the analyses. A flowchart of the workflow employed for the present study is depicted in Figure 1.

Figure 1: Flowchart depicting the workflow used in this study for identification of putative drug targets and target prioritization against Dichelobacter nodosus.

3. Results and Discussion

3.1. Subtractive Genomics and Essentiality Prediction for Filtering Drug Targets

In silico subtractive genomic analysis is a very fast and efficient method for identifying proteins in pathogenic species that are absent in the host. These proteins could serve as potential drug targets against the pathogens infecting the host tissues. Another important condition is the essentiality of the pathogen-specific proteins. Essential proteins are those which are believed to be critical for the survival of a cell. Although the essentiality of a gene is dependent on specific environment and cellular conditions, in general, the essentiality of a protein target is a positive indicator for druggability of the target. Therefore, we have identified a subset of proteins in Dichelobacter nodosus that are both essential to the pathogen and nonhomologous with ovine proteins.

The 1.39 Mb genome of D. nodosus VCS1703A is the smallest known genome of an anaerobe, containing 1354 annotated genes that encode for 1280 proteins [14]. BLAST analysis of the genome using DEG server gave a list of 787 protein coding genes that were predicted to be essential for the survival of D. nodosus. Thereafter, BLASTP analysis was performed for these 787 protein sequences against the sheep protein sequence database to identify 410 proteins that gave no significant hits; that is, they do not have a significant homology with any of the host proteins. Out of these protein sequences, 49 were hypothetical protein sequences and were not considered for any further analysis. A final list of 361 proteins was obtained that were most likely to be ideal drug targets against D. nodosus.

3.2. Pathway Annotation of Drug Targets

The candidate proteins were analyzed using KAAS for pathway annotation [29]. Out of 361 proteins, pathway annotation for 297 proteins was reported by the KAAS; the remaining 64 proteins had no pathway annotation information. The distribution of these 297 proteins into different metabolic pathways is depicted in Figure 2. Pathway annotation data file for the 297 drug targets is provided as Supplementary Material available online at http://dx.doi.org/10.1155/2016/7361361. The majority of the targets are involved in transcription and translations (60 proteins) and transport/secretion pathways (49 proteins), accounting for approximately 33% of the drug targets. Amino acid metabolism and replication and repair pathways each account for roughly 10% of the total drug targets. It should be noted that the 64 proteins which had no pathway annotation information are also potential drug targets that may be taken up for analysis and drug discovery studies. Hereafter, we have performed various analyses on this core set of 297 drug targets to identify subset of proteins with specific characteristics that may be relevant to specific drug development projects.

Figure 2: A pie-chart depicting the distribution of the 297 drug targets in Dichelobacter nodosus into the major metabolic pathways.

The bacterial pathways can be divided into two groups: (1) the pathways that are unique to bacteria only and are completely absent in mammalian host termed “unique bacterial pathways” and (2) the pathways that are common to both bacteria and the mammalian host termed “common pathways.” The unique bacterial pathways include 67 proteins annotated to the (i) two-component system, (ii) peptidoglycan biosynthesis, (iii) lipopolysaccharide biosynthesis, (iv) microbial metabolism in diverse environments, (v) photosynthesis, (vi) bacterial secretion systems, and (vii) D-alanine metabolism. The proteins belonging to the unique pathways are an ideal group of drug targets that are completely absent in host cell; host cell lacks the complete pathway and its associated proteins.

3.3. Identification of Metabolic Choke-Points in D. nodosus

We also performed a choke-point analysis on the list of 297 proteins to identify choke-point proteins. A reaction of metabolic network of a given organism which either consumes a specific substrate or produces a specific product is defined as a choke-point reaction [34]. The metabolite in focus should not be a final end product. Inhibiting a choke-point reaction/protein may lead to cell toxicity and death due to accumulation of an intermediate metabolite (in case of a protein utilizing a unique substrate) or due to paucity of one or more essential downstream metabolites (in case of a protein producing a unique product) [34]. Out of the 297 drug targets that were analyzed, 138 were identified as choke-point proteins. Out of the total identified choke-point proteins, 29 proteins belong to the unique pathways and the rest are part of common pathways (see Table 1). Table 2 lists a subset of 29 choke-point proteins that belong to unique pathways in bacterial system. Proteins belonging to this subset will be (a) safer targets as the complete pathway is absent in the host and, therefore, probability of cross-interaction of drugs is further reduced, (b) druggable targets due to presence of substrate-binding pockets, which may be gainfully exploited for drug development, and (c) effective targets because inhibition of these choke-point proteins is expected to produce a blockade in the pathway which may create an unsustainable condition inside the bacterial cell. Hence, this group of proteins are predicted to be attractive candidates in their respective pathways for the design of potent inhibitors.

Table 1: Distribution of the identified 297 putative drug targets into “unique bacteria pathways” and “common pathways.” The number of proteins that are involved in choke-point reactions within each group is also tabulated.
Table 2: List of 29 proteins that are part of unique bacterial pathways that are completely absent in host and are also choke-point proteins.
3.4. Classification Based on Predicted Subcellular Localization

Determination of subcellular localization of proteins is useful in genome/proteome analysis and annotation. Especially in case of pathogenic species, knowledge of subcellular localization of proteins is particularly useful in revealing cell surface and extracellularly secreted proteins that may be involved in pathogenesis. Since these proteins are the most accessible to any form of external intervention, hence they are considered attractive vaccine as well as drug targets. The distribution of the predicted subcellular localization for the 297 putative drug targets is depicted in Figure 3 (see Supplementary Material for raw data). While none of the proteins were predicted to be extracellular, 89 were predicted to be membrane-associated proteins, out of which 76 were inner membrane-associated, 9 were periplasmic, and 4 were outer membrane-associated proteins. A total of 187 proteins were predicted to be cytoplasmic proteins and for the remaining 21 the subcellular localization was unknown. It should be noted that the absence of any predicted extracellular protein could be a consequence of the workflow employed in the present study that biases the obtained results towards cytoplasmic and membrane proteins. Since extracellular proteins are generally not essential for the survival of the pathogen, they would not appear in the list of targets identified based on a homology with known essential proteins. However, many of them may be critical for promoting pathogenicity and survival of the pathogen inside the host tissues. Such secreted effector proteins can also be attractive targets for drug as well as vaccine development. It may therefore be useful to carry out subcellular localization prediction without incorporating a priori essentiality criteria for shortlisting protein targets, thereby allowing the identification of pathogen-specific extracellular proteins.

Figure 3: A pie-chart showing the distribution of the drug targets in Dichelobacter nodosus on the basis of their subcellular localization.

The 21 protein targets for which no localization prediction was obtained could also be considered for further investigations to identify correct localization and prioritized accordingly towards drug development studies. Experimental localization studies using fluorescent tags may be performed for this set of protein targets; this would aid in uncovering novel drug targets that are specific to the pathogen of interest.

3.5. Identifying Drug Targets against Multiple Pathogens

Since ovine foot rot is characterized by lesions at the hoof that are largely exposed, there is a possibility of multiple infections developing at the lesion. Proteins that are essential in multiple pathogens can be ideal drug targets for designing of broad-spectrum antibiotics that can be used for treating difficult cases of mixed infections. Therefore, we analyzed the 297 drug targets to look for conservation across 12 pathogenic bacterial species (see Section 2.5 for the list) by performing a BLASTP analysis against the DEG database for these 12 species [22]. The results of this analysis are depicted in Figure 4. Out of the 297 drug targets, we found 259 proteins to be essential and similar in at least 1 species. On the other hand, none of the proteins of D. nodosus were found to be essential and similar in all 12 species; only a single protein was found to have a similar match in 11 species. The 13 drug targets that were essential and conserved in at least 10 pathogenic bacterial species are tabulated in Table 3. These proteins candidates could potentially be targeted for drug development for treating infections caused by multiple pathogens and can be studied further for development of broad-spectrum antibiotics. Further, we find that, out of these 13 proteins, 5 proteins are choke-points within pathways that are unique to bacterial cells (indicated with in Table 3). These proteins include FtsI and penicillin-binding protein 2 that are targets for broad-spectrum β-lactam antibiotics. The other three (MurA, MurC, and MurG) are proteins that are essential for peptidoglycan biosynthesis; while MurA is already a target for fosfomycin, MurC and MurG could be explored further using computational and experimental methods as targets for design of broad-spectrum antibiotics. Computational studies may include development of homology-based protein models, virtual screening, and simulation studies of targets for drug discovery. Using sequence homology information, it is also possible to predict drug molecules that are likely to be good inhibitors of the candidate protein. For novel targets with no significant homology to available structures, crystallographic studies can be performed to aid the computational efforts for designing novel drugs.

Table 3: List of 13 proteins in Dichelobacter nodosus that were common in at least 10 pathogenic bacterial species. The results for the choke-point analysis for each protein are also tabulated.
Figure 4: Bar-graph depicting the number of Dichelobacter nodosus genes that are homologous with proteins of different number of pathogenic bacterial species.

4. Conclusion

In silico comparative genomics and bioinformatics approaches allow us to rationally narrow down the number of targets that may be considered for drug discovery workflows. We have identified a set of 361 proteins that are essential for Dichelobacter nodosus and are nonhomologous with the sheep proteome. The prediction of essential genes in the present study is based on the assumption that proteins homologous with known essential genes should also be essential. It is therefore recommended that, before selecting a final list of targets for drug development, experimental studies are conducted to validate the essentiality of the target proteins. Essentiality of a protein may be assayed in bacteria using conditional or temperature-sensitive mutants. From this set, 297 proteins with associated pathway annotations were examined further for subcellular localization, conservation in multiple pathogens, and so forth. Such analyses allow the identification of a specialized set of targets that are suitable for drug discovery approaches.

In summary, the present study has resulted in the generation of a list of proteins that may be considered for target-based drug discovery. In addition, the results also suggest that essentiality-based selection criteria of putative drug targets may not be suitable for detection of novel extracellular effectors of Dichelobacter; perhaps, consideration of this aspect will facilitate future computational studies that focus on identification of putative bacterial effector proteins. In general, the work lays down the foundation for future computational and experimental studies on the identified drug targets for design of novel drugs against ovine foot rot.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

Acknowledgments

Financial support from the Department of Biotechnology, India, is acknowledged.

References

  1. W. I. Beveridge, “Foot-rot in sheep: a transmissible disease due to infection with Fusiformis nodosus (n.sp.),” in Studies on Its Cause, Epidemiology and Control, Bulletin no. 140, Council for Scientific and Industrial Research, Melbourne, Australia, 1941. View at Google Scholar
  2. J. R. Egerton, D. S. Roberts, and I. M. Parsonson, “The aetiology and pathogenesis of ovine foot-rot. I. A histological study of the bacterial invasion,” Journal of Comparative Pathology, vol. 79, no. 2, pp. 207–216, 1969. View at Publisher · View at Google Scholar · View at Scopus
  3. D. J. Stewart, B. L. Clark, and R. G. Jarrett, “Differences between strains of Bacteroides nodosus in their effects on the severity of foot-rot, bodyweight and wool growth in Merino sheep,” Australian Veterinary Journal, vol. 61, no. 11, pp. 348–352, 1984. View at Publisher · View at Google Scholar · View at Scopus
  4. D. J. Stewart, “Footrot of sheep,” in Footrot and Foot Abscess of Ruminants, J. R. Egerton, W. K. Yong, and G. G. Riffkin, Eds., pp. 5–45, CRC Press, Boca Raton, Fla, USA, 1989. View at Google Scholar
  5. R. J. Whittington and P. J. Nicholls, “Effects of the severity and duration of lesions on the primary and anamnestic humoral responses of sheep to Dichelobacter nodosus and observations of natural resistance to footrot,” Research in Veterinary Science, vol. 59, no. 2, pp. 128–135, 1995. View at Publisher · View at Google Scholar · View at Scopus
  6. J. R. Egerton, J. T. Seaman, and R. I. Walker, “Eradication of virulent footrot from New South Wales,” in Proceedings of the 13th International Symposium and 5th Conference on Lameness in Ruminants, pp. 215–221, Maribor, Slovenija, February 2004.
  7. B. Hosie, “Footrot and lameness in sheep,” Veterinary Record, vol. 154, no. 2, pp. 37–38, 2004. View at Google Scholar · View at Scopus
  8. G. J. Wassink, R. Grogono-Thomas, L. J. Moore, and L. E. Green, “Risk factors associated with the prevalence of footrot in sheep from 1999 to 2000,” Veterinary Record, vol. 152, no. 12, pp. 351–358, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. S. R. Moorhouse, T. M. Skerman, and R. S. Green, “Further investigations of zinc sulphate footbathing for the prevention and treatment of ovine footrot,” New Zealand Veterinary Journal, vol. 31, no. 6, pp. 100–102, 1983. View at Publisher · View at Google Scholar
  10. L. E. Green, G. J. Wassink, R. Grogono-Thomas, L. J. Moore, and G. F. Medley, “Looking after the individual to reduce disease in the flock: a binomial mixed effects model investigating the impact of individual sheep management of footrot and interdigital dermatitis in a prospective longitudinal study on one farm,” Preventive Veterinary Medicine, vol. 78, no. 2, pp. 172–178, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. J. R. Egerton, S. C. Ghimire, O. P. Dhungyel et al., “Eradication of virulent footrot from sheep and goats in an endemic area of Nepal and an evaluation of specific vaccination,” Veterinary Record, vol. 151, no. 10, pp. 290–295, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. R. B. Gurung, O. P. Dhungyel, P. Tshering, and J. R. Egerton, “The use of an autogenous Dichelobacter nodosus vaccine to eliminate clinical signs of virulent footrot in a sheep flock in Bhutan,” The Veterinary Journal, vol. 172, no. 2, pp. 356–363, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. O. P. Dhungyel, D. R. Lehmann, and R. J. Whittington, “Pilot trials in Australia on eradication of footrot by flock specific vaccination,” Veterinary Microbiology, vol. 132, no. 3-4, pp. 364–371, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. G. S. A. Myers, D. Parker, K. Al-Hasani et al., “Genome sequence and identification of candidate vaccine antigens from the animal pathogen Dichelobacter nodosus,” Nature Biotechnology, vol. 25, no. 5, pp. 569–575, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. O. Dhungyel, N. Schiller, J. Eppleston et al., “Outbreak-specific monovalent/bivalent vaccination to control and eradicate virulent ovine footrot,” Vaccine, vol. 31, no. 13, pp. 1701–1706, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. 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. View at Google Scholar
  17. 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. View at Google Scholar · View at Scopus
  18. D. Perumal, C. S. Lim, K. R. Sakharkar, and M. K. Sakharkar, “Differential genome analyses of metabolic enzymes in Pseudomonas aeruginosa for drug target identification,” In Silico Biology, vol. 7, no. 4-5, pp. 453–465, 2007. View at Google Scholar · View at Scopus
  19. S. Anishetty, M. Pulimi, and G. Pennathur, “Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis,” Computational Biology and Chemistry, vol. 29, no. 5, pp. 368–378, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. V. Sharma, P. Gupta, and A. Dixit, “In silico identification of putative drug targets from different metabolic pathways of Aeromonas hydrophila,” In Silico Biology, vol. 8, no. 3-4, pp. 331–338, 2008. View at Google Scholar · View at Scopus
  21. G. Chhabra, P. Sharma, A. Anant et al., “Identification and modeling of a drug target for Clostridium perfringens SM101,” Bioinformation, vol. 4, no. 7, pp. 278–289, 2010. View at Publisher · View at Google Scholar
  22. R. Zhang, H.-Y. Ou, and C.-T. Zhang, “DEG: a database of essential genes,” Nucleic Acids Research, vol. 32, no. 1, pp. D271–D272, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Singh, B. K. Malik, and D. K. Sharma, “Choke point analysis of metabolic pathways in E.histolytica: a computational approach for drug target identification,” Bioinformation, vol. 2, no. 2, pp. 68–72, 2007. View at Publisher · View at Google Scholar
  25. D. Perumal, C. S. Lim, K. R. Sakharkar, and M. K. Sakharkar, “‘Load points’ and ‘choke points’ as nodes for prioritizing drug targets in Pseudomonas aeruginosa,” Current Bioinformatics, vol. 4, no. 1, pp. 48–53, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Fatumo, K. Plaimas, J.-P. Mallm et al., “Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico,” Infection, Genetics and Evolution, vol. 9, no. 3, pp. 351–358, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. A. M. Butt, I. Nasrullah, S. Tahir, and Y. Tong, “Comparative genomics analysis of Mycobacterium ulcerans for the identification of putative essential genes and therapeutic candidates,” PLoS ONE, vol. 7, no. 8, Article ID e43080, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Damte, J.-W. Suh, S.-J. Lee, S. B. Yohannes, M. A. Hossain, and S.-C. Park, “Putative drug and vaccine target protein identification using comparative genomic analysis of KEGG annotated metabolic pathways of Mycoplasma hyopneumoniae,” Genomics, vol. 102, no. 1, pp. 47–56, 2013. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Moriya, M. Itoh, S. Okuda, A. C. Yoshizawa, and M. Kanehisa, “KAAS: an automatic genome annotation and pathway reconstruction server,” Nucleic Acids Research, vol. 35, no. 2, pp. W182–W185, 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Caspi, T. Altman, J. M. Dale et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases,” Nucleic Acids Research, vol. 38, no. 1, pp. D473–D479, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000. View at Publisher · View at Google Scholar · View at Scopus
  32. N. Y. Yu, J. R. Wagner, M. R. Laird et al., “PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes,” Bioinformatics, vol. 26, no. 13, pp. 1608–1615, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. C.-S. Yu, Y.-C. Chen, C.-H. Lu, and J.-K. Hwang, “Prediction of protein subcellular localization,” Proteins: Structure, Function and Genetics, vol. 64, no. 3, pp. 643–651, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. I. Yeh, T. Hanekamp, S. Tsoka, P. D. Karp, and R. B. Altman, “Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery,” Genome Research, vol. 14, no. 5, pp. 917–924, 2004. View at Publisher · View at Google Scholar · View at Scopus