A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family.
Group feature
Host specificity
Iridoviridae
Linear epitope
Propensity scales
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
01 May 2019
01 May 2019
Historique:
entrez:
11
5
2019
pubmed:
11
5
2019
medline:
6
7
2019
Statut:
epublish
Résumé
The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.
Sections du résumé
BACKGROUND
BACKGROUND
The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.
Identifiants
pubmed: 31074372
doi: 10.1186/s12859-019-2736-2
pii: 10.1186/s12859-019-2736-2
pmc: PMC6509842
doi:
Substances chimiques
Epitopes
0
Viral Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
192Références
Dis Aquat Organ. 1999 Apr 15;36(1):73-5
pubmed: 10349554
J Hum Virol. 2000 Mar-Apr;3(2):63-76
pubmed: 10850891
J Mol Biol. 2000 Sep 8;302(1):205-17
pubmed: 10964570
Protein Eng. 2001 Aug;14(8):529-32
pubmed: 11579220
Arch Virol. 2005 Feb;150(2):351-9
pubmed: 15549489
Fish Shellfish Immunol. 2006 Apr;20(4):597-609
pubmed: 16213752
Proteins. 2006 Oct 1;65(1):40-8
pubmed: 16894596
J Mol Recognit. 2007 Mar-Apr;20(2):75-82
pubmed: 17205610
J Mol Recognit. 2008 Jul-Aug;21(4):243-55
pubmed: 18496882
Curr Top Microbiol Immunol. 2009;328:123-70
pubmed: 19216437
Microbiol Immunol. 2010 Mar;54(3):135-42
pubmed: 20236423
Nat Protoc. 2010 Apr;5(4):725-38
pubmed: 20360767
Nat Rev Immunol. 2010 Jul;10(7):527-35
pubmed: 20577269
J Biomed Biotechnol. 2011;2011:432830
pubmed: 21876642
Nucleic Acids Res. 2012 Jan;40(Database issue):D130-5
pubmed: 22121212
Mol Immunol. 2013 Jan;53(1-2):24-34
pubmed: 22784991
Dev Comp Immunol. 2012 Oct;38(2):254-61
pubmed: 22885634
Fish Shellfish Immunol. 2012 Oct;33(4):880-5
pubmed: 22971336
Fish Shellfish Immunol. 2012 Nov;33(5):1192-8
pubmed: 22986024
PLoS One. 2013 May 07;8(5):e62216
pubmed: 23667458
Hum Vaccin Immunother. 2014;10(5):1274-83
pubmed: 24633335
J Virol Methods. 2014 Sep 1;205:31-7
pubmed: 24814400
Protein Eng Des Sel. 2014 Oct;27(10):325-30
pubmed: 25301959
Nucleic Acids Res. 2015 Jan;43(Database issue):D204-12
pubmed: 25348405
J Biomed Inform. 2015 Feb;53:405-14
pubmed: 25464113
Vet Microbiol. 2014 Dec 5;174(3-4):382-390
pubmed: 25465180
Mol Phylogenet Evol. 2015 Mar;84:44-52
pubmed: 25562178
PLoS One. 2015 Mar 27;10(3):e0121673
pubmed: 25816293
Nat Protoc. 2015 Jun;10(6):845-58
pubmed: 25950237
Vaccine. 2015 Oct 13;33(42):5662-5669
pubmed: 26303874
Int J Mol Sci. 2015 Dec 02;16(12):28647-56
pubmed: 26633384
Comput Biol Med. 2016 Sep 1;76:24-9
pubmed: 27393958
Nucleic Acids Res. 2017 Jul 3;45(W1):W24-W29
pubmed: 28472356
PLoS One. 2017 Jun 5;12(6):e0178009
pubmed: 28582388
Int J Nanomedicine. 2017 Jul 04;12:4747-4762
pubmed: 28740382
Fish Shellfish Immunol. 2017 Dec;71:264-274
pubmed: 28939532
Vaccine. 2018 Feb 1;36(6):802-810
pubmed: 29325821