16. Capelli R, Peri C, Villa R et al (2018)
BPSL1626: reverse and structural vaccinology
reveal a novel candidate for vaccine design
against Burkholderia pseudomallei. Antibodies
7:26
17. Pajon R, Yero D, Niebla O et al (2009) Identification of new meningococcal serogroup B
surface antigens through a systematic analysis
of neisserial genomes. Vaccine 28:532–541
18. He Y, Xiang Z, Mobley HLT (2010) Vaxign:
the first web-based vaccine design program
for reverse vaccinology and applications for
vaccine development. J Biomed Biotechnol
2010:297505.
https://doi.org/10.1155/
2010/297505
19. Naz K, Naz A, Ashraf ST, Rizwan M,
Ahmad J, Baumbach J, Ali A (2019) PanRV:
pangenome-reverse vaccinology approach for
identifications of potential vaccine candidates
in microbial pangenome. BMC Bioinformatics
20:123.
https://doi.org/10.1186/
s12859-019-2713-9
20. Solanki V, Tiwari V (2018) Subtractive proteomics to identify novel drug targets and
reverse vaccinology for the development of
chimeric vaccine against Acinetobacter baumannii. Sci Rep 8:9044
21. Jaiswal V, Chanumolu SK, Gupta A, Chauhan
RS, Rout C (2013) Jenner-predict server: prediction of protein vaccine candidates (PVCs)
in bacteria based on host-pathogen interactions. BMC Bioinformatics 14:211
22. Zvi A, Rotem S, Bar-Haim E, Cohen O, Shafferman A (2011) Whole-genome immunoinformatic analysis of F. tularensis: predicted
CTL epitopes clustered in hotspots are prone
to elicit a T-cell response. PLoS One 6:
e20050
23. Solanki V, Tiwari M, Tiwari V (2019) Prioritization of potential vaccine targets using
comparative proteomics and designing of the
chimeric multi-epitope vaccine against Pseudomonas aeruginosa. Sci Rep 9:1. https://doi.
org/10.1038/s41598-019-41496-4
24. Dhanda SK, Usmani SS, Agrawal P, Nagpal G,
Gautam A, Raghava GPS (2017) Novel in
silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief
Bioinform 18:467–478
25. Sanchez-Trincado JL, Gomez-Perosanz M,
Reche PA (2017) Fundamentals and methods
for T- and B-cell epitope prediction. J Immunol Res 2017:2680160. https://doi.org/10.
1155/2017/2680160
26. Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X,
Peters B, Sette A (2017) The immune epitope
database and analysis resource in epitope
discovery and synthetic vaccine design. Front
Immunol 8:278. https://doi.org/10.3389/
fimmu.2017.00278
27. Vita R, Mahajan S, Overton JA, Dhanda SK,
Martini S, Cantrell JR, Wheeler DK, Sette A,
Peters B (2019) The immune epitope database (IEDB): 2018 update. Nucleic Acids Res
47:D339–D343
28. Cozzi R, Scarselli M, Ferlenghi I (2013)
Structural vaccinology: a three-dimensional
view for vaccine development. Curr Top
Med Chem 13:2629–2637
29. Anasir MI, Poh CL (2019) Structural vaccinology for viral vaccine design. Front Microbiol 10:738. https://doi.org/10.3389/
fmicb.2019.00738
30. Nuccitelli A, Rinaudo CD, Brogioni B et al
(2013) Understanding the molecular determinants driving the immunological specificity
of the protective pilus 2a backbone protein of
group B streptococcus. PLoS Comput Biol 9:
e1003115
31. Capelli R, Marchetti F, Tiana G, Colombo G
(2017) SAGE: a fast computational tool for
linear epitope grafting onto a foreign protein
scaffold. J Chem Inf Model 57:6–10
32. Raeven RHM, van Riet E, Meiring HD,
Metz B, Kersten GFA (2019) Systems vaccinology and big data in the vaccine development chain. Immunology 156:33–46
33. Oberg AL, Kennedy RB, Li P, Ovsyannikova
IG, Poland GA (2011) Systems biology
approaches to new vaccine development.
Curr Opin Immunol 23:436–443
34. De Groot AS, Ardito M, Moise L, Gustafson
EA, Spero D, Tejada G, Martin W (2011)
Immunogenic consensus sequence T helper
epitopes for a Pan-Burkholderia biodefense
vaccine. Immunome Res 7(2). https://doi.
org/10.4172/1745-7580.1000043
35. Goodswen SJ, Kennedy PJ, Ellis JT (2014)
Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide
conservation scores. PLoS One 9:e115745.
https://doi.org/10.1371/journal.pone.
0115745
36. Seib KL, Zhao X, Rappuoli R (2012) Developing vaccines in the era of genomics: a
decade of reverse vaccinology. Clin Microbiol
Infect 18(Suppl 5):109–116
37. Goodswen SJ, Kennedy PJ, Ellis JT (2018) A
gene-based positive selection detection
approach to identify vaccine candidates using
Toxoplasma gondii as a test case protozoan
pathogen. Front Genet 9:332. https://doi.
org/10.3389/fgene.2018.00332
Bacterial Pan-Proteome-Based Antigen Discovery
59
BPSL1626: reverse and structural vaccinology
reveal a novel candidate for vaccine design
against Burkholderia pseudomallei. Antibodies
7:26
17. Pajon R, Yero D, Niebla O et al (2009) Identification of new meningococcal serogroup B
surface antigens through a systematic analysis
of neisserial genomes. Vaccine 28:532–541
18. He Y, Xiang Z, Mobley HLT (2010) Vaxign:
the first web-based vaccine design program
for reverse vaccinology and applications for
vaccine development. J Biomed Biotechnol
2010:297505.
https://doi.org/10.1155/
2010/297505
19. Naz K, Naz A, Ashraf ST, Rizwan M,
Ahmad J, Baumbach J, Ali A (2019) PanRV:
pangenome-reverse vaccinology approach for
identifications of potential vaccine candidates
in microbial pangenome. BMC Bioinformatics
20:123.
https://doi.org/10.1186/
s12859-019-2713-9
20. Solanki V, Tiwari V (2018) Subtractive proteomics to identify novel drug targets and
reverse vaccinology for the development of
chimeric vaccine against Acinetobacter baumannii. Sci Rep 8:9044
21. Jaiswal V, Chanumolu SK, Gupta A, Chauhan
RS, Rout C (2013) Jenner-predict server: prediction of protein vaccine candidates (PVCs)
in bacteria based on host-pathogen interactions. BMC Bioinformatics 14:211
22. Zvi A, Rotem S, Bar-Haim E, Cohen O, Shafferman A (2011) Whole-genome immunoinformatic analysis of F. tularensis: predicted
CTL epitopes clustered in hotspots are prone
to elicit a T-cell response. PLoS One 6:
e20050
23. Solanki V, Tiwari M, Tiwari V (2019) Prioritization of potential vaccine targets using
comparative proteomics and designing of the
chimeric multi-epitope vaccine against Pseudomonas aeruginosa. Sci Rep 9:1. https://doi.
org/10.1038/s41598-019-41496-4
24. Dhanda SK, Usmani SS, Agrawal P, Nagpal G,
Gautam A, Raghava GPS (2017) Novel in
silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief
Bioinform 18:467–478
25. Sanchez-Trincado JL, Gomez-Perosanz M,
Reche PA (2017) Fundamentals and methods
for T- and B-cell epitope prediction. J Immunol Res 2017:2680160. https://doi.org/10.
1155/2017/2680160
26. Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X,
Peters B, Sette A (2017) The immune epitope
database and analysis resource in epitope
discovery and synthetic vaccine design. Front
Immunol 8:278. https://doi.org/10.3389/
fimmu.2017.00278
27. Vita R, Mahajan S, Overton JA, Dhanda SK,
Martini S, Cantrell JR, Wheeler DK, Sette A,
Peters B (2019) The immune epitope database (IEDB): 2018 update. Nucleic Acids Res
47:D339–D343
28. Cozzi R, Scarselli M, Ferlenghi I (2013)
Structural vaccinology: a three-dimensional
view for vaccine development. Curr Top
Med Chem 13:2629–2637
29. Anasir MI, Poh CL (2019) Structural vaccinology for viral vaccine design. Front Microbiol 10:738. https://doi.org/10.3389/
fmicb.2019.00738
30. Nuccitelli A, Rinaudo CD, Brogioni B et al
(2013) Understanding the molecular determinants driving the immunological specificity
of the protective pilus 2a backbone protein of
group B streptococcus. PLoS Comput Biol 9:
e1003115
31. Capelli R, Marchetti F, Tiana G, Colombo G
(2017) SAGE: a fast computational tool for
linear epitope grafting onto a foreign protein
scaffold. J Chem Inf Model 57:6–10
32. Raeven RHM, van Riet E, Meiring HD,
Metz B, Kersten GFA (2019) Systems vaccinology and big data in the vaccine development chain. Immunology 156:33–46
33. Oberg AL, Kennedy RB, Li P, Ovsyannikova
IG, Poland GA (2011) Systems biology
approaches to new vaccine development.
Curr Opin Immunol 23:436–443
34. De Groot AS, Ardito M, Moise L, Gustafson
EA, Spero D, Tejada G, Martin W (2011)
Immunogenic consensus sequence T helper
epitopes for a Pan-Burkholderia biodefense
vaccine. Immunome Res 7(2). https://doi.
org/10.4172/1745-7580.1000043
35. Goodswen SJ, Kennedy PJ, Ellis JT (2014)
Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide
conservation scores. PLoS One 9:e115745.
https://doi.org/10.1371/journal.pone.
0115745
36. Seib KL, Zhao X, Rappuoli R (2012) Developing vaccines in the era of genomics: a
decade of reverse vaccinology. Clin Microbiol
Infect 18(Suppl 5):109–116
37. Goodswen SJ, Kennedy PJ, Ellis JT (2018) A
gene-based positive selection detection
approach to identify vaccine candidates using
Toxoplasma gondii as a test case protozoan
pathogen. Front Genet 9:332. https://doi.
org/10.3389/fgene.2018.00332
Bacterial Pan-Proteome-Based Antigen Discovery
59