• Kiranmayi Patnala Department of Biochemistry, Institute of Science, GITAM University
  • Kunal Zaveri


Objective: Staphylococcus aureus, a superbug and antibiotic resistant pathogen, is one of the most infection causing organism, ranging from skin
allergies to severe lethal conditions. The prolonged use of different antibiotics and lack of optimal treatment over the antibiotic resistant species, led
to the identification of new, better and promising therapeutic candidates.
Methods: A systematic in silico filtration process was employed, which includes subtractive channels and reverse vaccinology techniques.
Results: Here, we report 12 possible drug targets and two vaccine candidates based on essentiality, non-human homolog, virulent and localization,
commonly in all the strains. Further characterization studies such as pathway analysis, chokepoint and structure prediction revealed, two proteins
as the best drug targets one being novel and the other druggable. Only one protein has shown the characteristic feature of vaccine candidate, having
antigenic property and an IgG binding domain.
Conclusion: Two best drug targets were commonly identified in all the strains of S. aureus namely UDP-N-acetylmuramoyl-L-alanyl-D-glutamate--L-lysine
ligase (MurE) and cell division protein FtsA, whereas the best common vaccine candidate includes Peptidoglycan binding protein. The therapeutic candidates
reported in the present study might facilitate screening of new and better antimicrobial compounds, for an optimal treatment of S. aureus infections.
Keywords: Staphylococcus aureus, Drug target, Vaccine candidates, Subtractive proteomics, Reverse vaccinology.


1. Hughes JP, Rees S, Kalindjian SB, Philpott KL. Principles of early drug
discovery. Br J Pharmacol 2011;162(6):1239-49.
2. Schmitt M, Schuler-Schmid U, Schmidt-Lorenz W. Temperature limits
of growth, TNase and enterotoxin production of Staphylococcus aureus
strains isolated from foods. Int J Food Microbiol 1990;11:1-19.
3. Rode TM, Møretrø T, Langsrud S, Holck A. Responses of
Staphylococcus Aureus To Environmental Stresses. Stress Response of
Asian J Pharm Clin Res, Vol 9, Suppl. 2, 2016, 283-291
Zaveri and Patnala
Foodborne microorganismsicroorganisms. 2012:509-46.
4. Gordon RJ, Lowy FD. Pathogenesis of methicillin-resistant
Staphylococcus aureus infection. Clin Infect Dis 2008;46 Suppl 5:S350-9.
5. Argudín MÁ, Mendoza MC, Rodicio MR. Food poisoning and
Staphylococcus aureus enterotoxins. Toxins (Basel) 2010;2(7):1751-73.
6. Jevons MP. “Celbenin” - resistant staphylococci. Br Med J. BMJ
Group; 1961. p. 124. Available from:
7. Hiramatsu K, Cui L, Kuroda M, Ito T. The emergence and evolution
of methicillin-resistant Staphylococcus aureus. Trends Microbiol
8. Rice LB. Federal funding for the study of antimicrobial resistance in
nosocomial pathogens: No eskape. J Infect Dis 2008;197(8):1079-81.
9. Richards MS, Rittman M, Gilbert TT, Opal SM, DeBuono BA, Neill
RJ, et al. Investigation of a staphylococcal food poisoning outbreak in a
centralized school lunch program. Public Health Rep 1993;108(6):765-71.
10. Do Carmo LS, Cummings C, Linardi VR, Dias RS, De Souza JM, De
Sena MJ, et al. A case study of a massive staphylococcal food poisoning
incident. Foodborne Pathog Dis 2004;1(4):241-6.
11. Asao T, Kumeda Y, Kawai T, Shibata T, Oda H, Haruki K, et al. An
extensive outbreak of staphylococcal food poisoning due to low-fat
milk in Japan: Estimation of enterotoxin A in the incriminated milk and
powdered skim milk. Epidemiol Infect 2003;130(1):33-40.
12. Schmid D, Gschiel E, Mann M, Huhulescu S, Ruppitsch W, Böhm G,
et al. Outbreak of acute gastroenteritis in an Austrian boarding school,
September, 2006. European Centre for Disease Prevention and Control
(ECDC) - Health Comunication Unit; 2007. Available from: http://
13. Weiler N, Leotta GA, Zárate MN, Manfredi E, Alvarez ME, Rivas M.
Foodborne outbreak associated with consumption of ultrapasteurized
milk in the Republic of Paraguay. Rev Argent Microbiol 2011;43(1):33-6.
14. Joshi S, Ray P, Manchanda V, Bajaj J, Gautam V, Goswami P, et al.
Methicillin resistant Staphylococcus aureus (MRSA) in India:
Prevalence & susceptibility pattern. Indian J Med Res 2013;137:363-9.
15. Moellering RC Jr. Vancomycin: A 50-year reassessment. Clin Infect Dis
2006;42 Suppl 1:S3-4.
16. Shoemaker DM, Simou J, Roland WE. A review of daptomycin
for injection (cubicin) in the treatment of complicated skin and skin
structure infections. Ther Clin Risk Manag 2006;2(2):169-74.
17. Sieradzki K, Tomasz A. Inhibition of cell wall turnover and autolysis by
vancomycin in a highly vancomycin-resistant mutant of Staphylococcus
aureus. J Bacteriol 1997;179(8):2557-66.
18. Projan SJ. Why is big pharma getting out of antibacterial drug
discovery? Curr Opin Microbiol 2003;6(5):427-30.
19. Tatusova T, Ciufo S, Fedorov B, O’Neill K, Tolstoy I. RefSeq microbial
genomes database: New representation and annotation strategy. Nucleic
Acids Res 2014;42:D553-9.
20. 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.
21. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local
alignment search tool. J Mol Biol 1990;215(3):403-10.
22. Garg A, Gupta D. VirulentPred: A SVM based prediction method for
virulent proteins in bacterial pathogens. BMC Bioinformatics 2008;9:62.
23. Yu C, Chen Y, Lu C, Hwang J. Prediction of protein subcellular
localization. Amino Acids 2006;651:643-51.
24. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb
3.0: Improved protein subcellular localization prediction with refined
localization subcategories and predictive capabilities for all prokaryotes.
Bioinformatics 2010;26(13):1608-15.
25. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS:
An automatic genome annotation and pathway reconstruction server.
Nucleic Acids Res 2007;35:W182-5.
26. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P,
et al. Drugbank: A comprehensive resource for in silico drug discovery
and exploration. Nucleic Acids Res 2006;34:D668-72.
27. Doytchinova IA, Flower DR. VaxiJen: A server for prediction of
protective antigens, tumour antigens and subunit vaccines. BMC
Bioinformatics 2007;8:4.
28. Möller S, Croning MD, Apweiler R. Evaluation of methods for
the prediction of membrane spanning regions. Bioinformatics
29. Mitchell A, Chang HY, Daugherty L, Fraser M, Hunter S, Lopez R,
et al. The interpro protein families database: The classification resource
after 15 years. Nucleic Acids Res 2015;43:D213-21.
30. Yao B, Zhang L, Liang S, Zhang C. SVMTriP: A method to predict
antigenic epitopes using support vector machine to integrate tri-peptide
similarity and propensity. PLoS One 2012;7(9):e45152.
31. Human Microbiome Project Consortium. A framework for human
microbiome research. Nature 2012;486(7402):215-21.
32. Human Microbiome Project Consortium. Structure, function and diversity
of the healthy human microbiome. Nature 2012;486(7402):207-14.
33. Kuroda M, Ohta T, Uchiyama I, Baba T, Yuzawa H, Kobayashi I, et al.
Whole genome sequencing of meticillin-resistant Staphylococcus
aureus. Lancet 2001;357(9264):1225-40.
34. Huynen MA, Diaz-Lazcoz Y, Bork P. Differential genome display.
Trends Genet 1997;13(10):389-90.
35. Ludin P, Woodcroft B, Ralph SA, Mäser P. In silico prediction of
antimalarial drug target candidates. Int J Parasitol Drugs drug Resist
36. Damte D, Suh JW, Lee SJ, Yohannes SB, Hossain MA, Park SC.
Putative drug and vaccine target protein identification using
comparative genomic analysis of KEGG annotated metabolic pathways
of Mycoplasma hyopneumoniae. Genomics 2013;102(1):47-56.
37. Chhabra G, Sharma P, Anant A, Deshmukh S, Kaushik H, Gopal K,
et al. Identification and modeling of a drug target for Clostridium
perfringens SM101. Bioinformation 2010;4(7):278-89.
38. Rathi B, Sarangi AN, Trivedi N. Genome subtraction for novel target
definition in Salmonella typhi. Bioinformation 2009;4(4):143-50.
39. Narayan Sarangi A, Aggarwal R, Rahman Q, Trivedi N. Subtractive
genomics approach for in silico identification and characterization of
novel drug targets in Neisseria meningitidis serogroup B. J Comput Sci
Syst Biol 2009;2:255-8.
40. Sharma V, Gupta P, Dixit A. In silico identification of putative drug
targets from different metabolic pathways of Aeromonas hydrophila. In
Silico Biol 2008;8(3-4):331-8.
41. Dutta A, Singh SK, Ghosh P, Mukherjee R, Mitter S, Bandyopadhyay
D. In silico identification of potential therapeutic targets in the human
pathogen Helicobacter pylori. In Silico Biol 2006;6(1-2):43-7.
42. Koonin EV. How many genes can make a cell: The minimal-gene-set
concept. Annu Rev Genomics Hum Genet 2000;1:99-116.
43. Gerdes S, Edwards R, Kubal M, Fonstein M, Stevens R, Osterman A.
Essential genes on metabolic maps. Curr Opin Biotechnol
44. Duffield M, Cooper I, McAlister E, Bayliss M, Ford D, Oyston P.
Predicting conserved essential genes in bacteria: In silico identification
of putative drug targets. Mol Biosyst 2010;6(12):2482-9.
45. Cegelski L, Marshall GR, Eldridge GR, Hultgren SJ. The biology and future
prospects of antivirulence therapies. Nat Rev Microbiol 2008;6(1):17-27.
46. Rask-Andersen M, Almén MS, Schiöth HB. Trends in the exploitation
of novel drug targets. Nat Rev Drug Discov 2011;10(8):579-90.
47. Yeh I, Hanekamp T, Tsoka S, Karp PD, Altman RB. Computational
analysis of Plasmodium falciparum metabolism: Organizing
genomic information to facilitate drug discovery. Genome Res
48. Campbell SF. Science, art and drug discovery: A personal perspective.
Clin Sci (Lond) 2000;99(4):255-60.
49. Meroueh SO, Bencze KZ, Hesek D, Lee M, Fisher JF, Stemmler TL,
et al. Three-dimensional structure of the bacterial cell wall
peptidoglycan. Proc Natl Acad Sci U S A 2006;103(12):4404-9.
50. Reed P, Atilano ML, Alves R, Hoiczyk E, Sher X, Reichmann NT,
et al. Staphylococcus aureus survives with a minimal peptidoglycan
synthesis machine but sacrifices virulence and antibiotic resistance.
PLoS Pathog 2015;11(5):e1004891.
51. Ruane KM, Lloyd AJ, Fülöp V, Dowson CG, Barreteau H, Boniface A,
et al. Specificity determinants for lysine incorporation in Staphylococcus
aureus peptidoglycan as revealed by the structure of a MurE enzyme
ternary complex. J Biol Chem 2013;288(46):33439-48.
52. Aarsman ME, Piette A, Fraipont C, Vinkenvleugel TM, NguyenDistèche
M, den Blaauwen T.
Maturation of the Escherichia

occurs in two steps. Mol Microbiol 2005;55(6):1631-45.
53. Lutkenhaus J, Addinall SG. Bacterial cell division and the Z ring. Annu
Rev Biochem 1997;66:93-116.
54. Pichoff S, Lutkenhaus J. Tethering the Z ring to the membrane through
a conserved membrane targeting sequence in FtsA. Mol Microbiol
55. Bork P, Sander C, Valencia A. An ATPase domain common to
prokaryotic cell cycle proteins, sugar kinases, actin, and hsp70 heat
shock proteins. Proc Natl Acad Sci U S A 1992;89(16):7290-4.
56. Ojima I, Kumar K, Awasthi D, Vineberg JG. Drug discovery targeting
cell division proteins, microtubules and FtsZ. Bioorg Med Chem
57. Chène P. ATPases as drug targets: Learning from their structure. Nat
Rev Drug Discov 2002;1(9):665-73.
268 Views | 249 Downloads
How to Cite
Patnala, K., and K. Zaveri. “SCREENING OF PUTATIVE THERAPEUTIC CANDIDATES IN SUPERBUG (STAPHYLOCOCCUS AUREUS): A SYSTEMATIC IN SILICO APPROACH”. Asian Journal of Pharmaceutical and Clinical Research, Vol. 9, no. 8, Oct. 2016, pp. 283-91, doi:10.22159/ajpcr.2016.v9s2.13852.
Original Article(s)