Implementing sequence-based antigenic distance calculation into immunological shape space model

In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational mode...

Full description

Saved in:
Bibliographic Details
Published inBMC bioinformatics Vol. 21; no. 1; pp. 256 - 13
Main Authors Anderson, Christopher S., Sangster, Mark Y., Yang, Hongmei, Mariani, Thomas J., Chaudhury, Sidhartha, Topham, David J.
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 19.06.2020
BioMed Central
BMC
Subjects
Online AccessGet full text
ISSN1471-2105
1471-2105
DOI10.1186/s12859-020-03594-3

Cover

Loading…
Abstract In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.
AbstractList Abstract Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza “swine flu” pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. Results We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual’s pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. Conclusion We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.
In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.
In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.
In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine.BACKGROUNDIn 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine.We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine.RESULTSWe found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine.We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.CONCLUSIONWe provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.
Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine. Results We found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine. Conclusion We provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories. Keywords: Gillespie algorithm, Shape space, Antigenic distance, Epitopes, Antigenic sites, Hemagglutinin, Influenza, Vaccines, Computational immunology, HA, Stalk, Stem, 2009 pandemic, H1N1, pH1N1, Artificial immune systems, Humoral immune system, Simulations
ArticleNumber 256
Audience Academic
Author Sangster, Mark Y.
Mariani, Thomas J.
Yang, Hongmei
Anderson, Christopher S.
Chaudhury, Sidhartha
Topham, David J.
Author_xml – sequence: 1
  givenname: Christopher S.
  orcidid: 0000-0002-8560-3438
  surname: Anderson
  fullname: Anderson, Christopher S.
– sequence: 2
  givenname: Mark Y.
  surname: Sangster
  fullname: Sangster, Mark Y.
– sequence: 3
  givenname: Hongmei
  surname: Yang
  fullname: Yang, Hongmei
– sequence: 4
  givenname: Thomas J.
  surname: Mariani
  fullname: Mariani, Thomas J.
– sequence: 5
  givenname: Sidhartha
  surname: Chaudhury
  fullname: Chaudhury, Sidhartha
– sequence: 6
  givenname: David J.
  surname: Topham
  fullname: Topham, David J.
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32560624$$D View this record in MEDLINE/PubMed
BookMark eNp9kltrFTEQxxep2It-AR9kwRd92JrbZpMXoRQvBwqCl-cwm8xuU7LJcbMr-u3N6WmlR0TykDDzmz8zk_9pdRRTxKp6Tsk5pUq-yZSpVjeEkYbwVouGP6pOqOhowyhpjx68j6vTnG8IoZ0i7ZPqmLNWEsnESWU20zbghHHxcawzfl8xWmx6yOhqKNERo7e183mBkqgtBLsGWHyKtY9Lqv00rTGFNPqSqvM1bLHOWyjolByGp9XjAULGZ3f3WfXt_buvlx-bq08fNpcXV41tpV4aFEor2XGmHGgpuVY9EKs6yTotYOCa9Y63RAuEnqmeMUV6J0EwzQehiORn1Wav6xLcmO3sJ5h_mQTe3AbSPBqYF28DGg6M9Z0bLKdC9EKAUjBQVBIdZ51qi9bbvdZ27Sd0tixnhnAgepiJ_tqM6YfpOOGa8yLw6k5gTmWheTGTzxZDgIhpzYYJ2pbOKekK-nKPjlBa83FIRdHucHNRhi8_2IrddOf_oMpxOHlbXDH4Ej8oeH1QUJgFfy4jrDmbzZfPh-yLh-P-mfPeJAVQe8DOKecZB2P9cmuB0oUPhhKz86PZ-9EUP5pbP5rdJthfpffq_yn6DRDH4Ng
CitedBy_id crossref_primary_10_31857_S0320972524050075
crossref_primary_10_1134_S0006297924050079
crossref_primary_10_3390_pathogens12020169
Cites_doi 10.1006/jtbi.1997.0495
10.1016/j.tcs.2008.02.011
10.1128/JVI.00169-19
10.1073/pnas.0903427106
10.1016/j.vaccine.2006.01.010
10.1371/journal.pone.0160510
10.1016/j.vaccine.2015.11.058
10.1016/j.vaccine.2008.07.078
10.1016/j.coviro.2016.12.004
10.1073/pnas.1118979109
10.1128/JVI.00098-09
10.1073/pnas.96.24.14001
10.1093/protein/gzq105
10.1006/bulm.1997.0035
10.1371/journal.pcbi.1000882
10.1038/ncomms1710
10.1186/s12859-018-2042-4
10.1126/scitranslmed.aad0522
10.4049/jimmunol.1401054
10.1126/science.1256427
10.1126/science.1176225
10.1084/jem.20130212
10.1038/nri700
10.1128/JVI.02184-09
10.1016/j.vaccine.2008.04.011
10.1038/s41598-017-14931-7
10.1128/JVI.00147-12
10.1093/nar/gkh340
10.1084/jem.20101352
10.1056/NEJMoa0906453
10.1111/imm.12594
10.1016/j.celrep.2015.06.005
10.1371/journal.ppat.1005692
10.1038/s41598-018-28706-1
10.1128/CVI.00735-12
10.1098/rsif.2015.0627
ContentType Journal Article
Copyright COPYRIGHT 2020 BioMed Central Ltd.
The Author(s) 2020
Copyright_xml – notice: COPYRIGHT 2020 BioMed Central Ltd.
– notice: The Author(s) 2020
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
ISR
7X8
5PM
DOA
DOI 10.1186/s12859-020-03594-3
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Gale In Context: Science
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList
MEDLINE

MEDLINE - Academic


Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 1471-2105
EndPage 13
ExternalDocumentID oai_doaj_org_article_3a22b7dfc3144b44a88af1e86ed32785
PMC7303933
A627147546
32560624
10_1186_s12859_020_03594_3
Genre Journal Article
GrantInformation_xml – fundername: NIAID NIH HHS
  grantid: HHSN272201400005C
– fundername: NHLBI NIH HHS
  grantid: T32-HL066988
– fundername: NHLBI NIH HHS
  grantid: T32 HL066988
– fundername: University of Rochester (US)
  grantid: OP211341
– fundername: NIEHS NIH HHS
  grantid: P30 ES001247
– fundername: ;
  grantid: OP211341
– fundername: ;
  grantid: T32-HL066988
– fundername: ;
  grantid: HHSN272201400005C
GroupedDBID ---
0R~
23N
2WC
53G
5VS
6J9
7X7
88E
8AO
8FE
8FG
8FH
8FI
8FJ
AAFWJ
AAJSJ
AAKPC
AASML
AAYXX
ABDBF
ABUWG
ACGFO
ACGFS
ACIHN
ACIWK
ACPRK
ACUHS
ADBBV
ADMLS
ADUKV
AEAQA
AENEX
AEUYN
AFKRA
AFPKN
AFRAH
AHBYD
AHMBA
AHYZX
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AMKLP
AMTXH
AOIJS
ARAPS
AZQEC
BAPOH
BAWUL
BBNVY
BCNDV
BENPR
BFQNJ
BGLVJ
BHPHI
BMC
BPHCQ
BVXVI
C6C
CCPQU
CITATION
CS3
DIK
DU5
DWQXO
E3Z
EAD
EAP
EAS
EBD
EBLON
EBS
EMB
EMK
EMOBN
ESX
F5P
FYUFA
GNUQQ
GROUPED_DOAJ
GX1
HCIFZ
HMCUK
HYE
IAO
ICD
IHR
INH
INR
ISR
ITC
K6V
K7-
KQ8
LK8
M1P
M48
M7P
MK~
ML0
M~E
O5R
O5S
OK1
OVT
P2P
P62
PGMZT
PHGZM
PHGZT
PIMPY
PQQKQ
PROAC
PSQYO
RBZ
RNS
ROL
RPM
RSV
SBL
SOJ
SV3
TR2
TUS
UKHRP
W2D
WOQ
WOW
XH6
XSB
CGR
CUY
CVF
ECM
EIF
NPM
PMFND
7X8
PJZUB
PPXIY
PQGLB
5PM
PUEGO
ID FETCH-LOGICAL-c569t-e489867328da966398ba0c8762794af392bd35094eab28b2280bd6a4293f48063
IEDL.DBID M48
ISSN 1471-2105
IngestDate Wed Aug 27 01:06:38 EDT 2025
Thu Aug 21 18:06:41 EDT 2025
Mon Jul 21 11:36:39 EDT 2025
Tue Jun 17 21:49:40 EDT 2025
Tue Jun 10 20:14:31 EDT 2025
Fri Jun 27 05:04:54 EDT 2025
Thu Apr 03 07:02:46 EDT 2025
Thu Apr 24 23:13:56 EDT 2025
Tue Jul 01 03:38:30 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords Shape space
Simulations
Hemagglutinin
Humoral immune system
Epitopes
Vaccines
H1N1
Gillespie algorithm
Stem
Computational immunology
Artificial immune systems
Influenza
2009 pandemic
pH1N1
Antigenic distance
Stalk
HA
Antigenic sites
Language English
License Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c569t-e489867328da966398ba0c8762794af392bd35094eab28b2280bd6a4293f48063
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0002-8560-3438
OpenAccessLink https://doaj.org/article/3a22b7dfc3144b44a88af1e86ed32785
PMID 32560624
PQID 2415293107
PQPubID 23479
PageCount 13
ParticipantIDs doaj_primary_oai_doaj_org_article_3a22b7dfc3144b44a88af1e86ed32785
pubmedcentral_primary_oai_pubmedcentral_nih_gov_7303933
proquest_miscellaneous_2415293107
gale_infotracmisc_A627147546
gale_infotracacademiconefile_A627147546
gale_incontextgauss_ISR_A627147546
pubmed_primary_32560624
crossref_citationtrail_10_1186_s12859_020_03594_3
crossref_primary_10_1186_s12859_020_03594_3
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2020-06-19
PublicationDateYYYYMMDD 2020-06-19
PublicationDate_xml – month: 06
  year: 2020
  text: 2020-06-19
  day: 19
PublicationDecade 2020
PublicationPlace England
PublicationPlace_xml – name: England
– name: London
PublicationTitle BMC bioinformatics
PublicationTitleAlternate BMC Bioinformatics
PublicationYear 2020
Publisher BioMed Central Ltd
BioMed Central
BMC
Publisher_xml – name: BioMed Central Ltd
– name: BioMed Central
– name: BMC
References BL Tesini (3594_CR9) 2019; 93
VI Zarnitsyna (3594_CR20) 2016; 12
DJ Smith (3594_CR41) 1997
V Gupta (3594_CR34) 2006; 24
W Ndifon (3594_CR37) 2015; 12
E Kirkpatrick (3594_CR8) 2018; 8
DJ Smith (3594_CR42) 1998; 60
FJ Thomas (3594_CR27) 2012
G-M Li (3594_CR10) 2012; 109
S Cobey (3594_CR36) 2017; 22
RC Edgar (3594_CR38) 2004; 32
Y Li (3594_CR13) 2013; 210
DJ Smith (3594_CR35) 1997; 189
MF Boni (3594_CR2) 2008; 26
CS Anderson (3594_CR18) 2016; 11
HY Lee (3594_CR22) 2009; 83
W Ndifon (3594_CR21) 2009; 106
AS Perelson (3594_CR19) 2002; 2
J Timmis (3594_CR40) 2008; 403
CA Russell (3594_CR31) 2008; 26
SF Andrews (3594_CR12) 2015; 7
DJ Smith (3594_CR28) 1999; 96
H Sun (3594_CR16) 2013; 4
K Hancock (3594_CR25) 2009; 361
S Chaudhury (3594_CR23) 2014; 193
J Wrammert (3594_CR32) 2011; 208
K Pan (3594_CR33) 2011; 24
AM Höpping (3594_CR30) 2016; 34
3594_CR24
BS Chambers (3594_CR3) 2015; 12
KM Murphy (3594_CR1) 2011
CD O'Donnell (3594_CR14) 2012; 86
JM Fonville (3594_CR29) 2014; 346
V Hunt (3594_CR39) 2010
ML DeDiego (3594_CR6) 2016; 148
MY Sangster (3594_CR7) 2013; 20
JC Krause (3594_CR26) 2010; 84
CS Anderson (3594_CR5) 2017; 7
CS Anderson (3594_CR4) 2018; 19
RJ Garten (3594_CR11) 2009; 325
A Wu (3594_CR15) 2010; 6
X Du (3594_CR17) 2012; 3
References_xml – volume: 189
  start-page: 141
  year: 1997
  ident: 3594_CR35
  publication-title: J Theor Biol
  doi: 10.1006/jtbi.1997.0495
– volume: 403
  start-page: 11
  year: 2008
  ident: 3594_CR40
  publication-title: Theor Comput Sci
  doi: 10.1016/j.tcs.2008.02.011
– volume: 93
  start-page: e00169
  year: 2019
  ident: 3594_CR9
  publication-title: J Virol
  doi: 10.1128/JVI.00169-19
– volume: 106
  start-page: 8701
  year: 2009
  ident: 3594_CR21
  publication-title: Proc Natl Acad Sci
  doi: 10.1073/pnas.0903427106
– volume: 24
  start-page: 3881
  year: 2006
  ident: 3594_CR34
  publication-title: Vaccine.
  doi: 10.1016/j.vaccine.2006.01.010
– start-page: 1
  volume-title: Influenza Research Database (IRD): a web-based resource for influenza virus data and analysis [Internet]
  year: 2010
  ident: 3594_CR39
– volume: 11
  start-page: e0160510
  year: 2016
  ident: 3594_CR18
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0160510
– volume: 34
  start-page: 540
  year: 2016
  ident: 3594_CR30
  publication-title: Vaccine.
  doi: 10.1016/j.vaccine.2015.11.058
– volume: 26
  start-page: D31
  issue: Suppl 4
  year: 2008
  ident: 3594_CR31
  publication-title: Vaccine.
  doi: 10.1016/j.vaccine.2008.07.078
– volume: 22
  start-page: 105
  year: 2017
  ident: 3594_CR36
  publication-title: Curr Opin Virol
  doi: 10.1016/j.coviro.2016.12.004
– volume: 109
  start-page: 9047
  year: 2012
  ident: 3594_CR10
  publication-title: Proc Natl Acad Sci U S A
  doi: 10.1073/pnas.1118979109
– volume: 83
  start-page: 7151
  year: 2009
  ident: 3594_CR22
  publication-title: J Virol
  doi: 10.1128/JVI.00098-09
– volume: 96
  start-page: 14001
  year: 1999
  ident: 3594_CR28
  publication-title: Proc Natl Acad Sci
  doi: 10.1073/pnas.96.24.14001
– volume: 24
  start-page: 291
  year: 2011
  ident: 3594_CR33
  publication-title: Protein Eng Des Sel
  doi: 10.1093/protein/gzq105
– volume: 60
  start-page: 647
  year: 1998
  ident: 3594_CR42
  publication-title: Bull Math Biol
  doi: 10.1006/bulm.1997.0035
– volume: 6
  start-page: e1000882
  year: 2010
  ident: 3594_CR15
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1000882
– volume: 3
  start-page: 709
  year: 2012
  ident: 3594_CR17
  publication-title: Nat Commun
  doi: 10.1038/ncomms1710
– volume: 19
  start-page: 51
  year: 2018
  ident: 3594_CR4
  publication-title: BMC Bioinformatics
  doi: 10.1186/s12859-018-2042-4
– start-page: 1
  volume-title: Smith thesis dissertation. Department of Computer Science. The University of New Mexico
  year: 1997
  ident: 3594_CR41
– volume: 7
  start-page: 316ra192
  year: 2015
  ident: 3594_CR12
  publication-title: Sci Transl Med
  doi: 10.1126/scitranslmed.aad0522
– volume: 193
  start-page: 2073
  year: 2014
  ident: 3594_CR23
  publication-title: J Immunol
  doi: 10.4049/jimmunol.1401054
– start-page: 1
  volume-title: On the doctrine of original antigenic sin
  year: 2012
  ident: 3594_CR27
– volume: 346
  start-page: 996
  year: 2014
  ident: 3594_CR29
  publication-title: Science.
  doi: 10.1126/science.1256427
– volume: 325
  start-page: 197
  year: 2009
  ident: 3594_CR11
  publication-title: Science
  doi: 10.1126/science.1176225
– volume: 210
  start-page: 1493
  year: 2013
  ident: 3594_CR13
  publication-title: J Exp Med
  doi: 10.1084/jem.20130212
– volume: 2
  start-page: 28
  year: 2002
  ident: 3594_CR19
  publication-title: Nat Rev Immunol
  doi: 10.1038/nri700
– volume: 84
  start-page: 3127
  year: 2010
  ident: 3594_CR26
  publication-title: J Virol
  doi: 10.1128/JVI.02184-09
– volume: 26
  start-page: C8
  year: 2008
  ident: 3594_CR2
  publication-title: Vaccine.
  doi: 10.1016/j.vaccine.2008.04.011
– volume: 7
  start-page: 14614
  year: 2017
  ident: 3594_CR5
  publication-title: Sci Rep
  doi: 10.1038/s41598-017-14931-7
– volume: 86
  start-page: 8625
  year: 2012
  ident: 3594_CR14
  publication-title: J Virol
  doi: 10.1128/JVI.00147-12
– volume: 32
  start-page: 1792
  year: 2004
  ident: 3594_CR38
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkh340
– volume: 208
  start-page: 181
  year: 2011
  ident: 3594_CR32
  publication-title: J Exp Med
  doi: 10.1084/jem.20101352
– volume: 4
  start-page: 1057
  year: 2013
  ident: 3594_CR16
  publication-title: MBio
– volume: 361
  start-page: 1945
  year: 2009
  ident: 3594_CR25
  publication-title: N Engl J Med
  doi: 10.1056/NEJMoa0906453
– volume-title: Janeway’s immunobiology
  year: 2011
  ident: 3594_CR1
– volume: 148
  start-page: 160
  issue: 2
  year: 2016
  ident: 3594_CR6
  publication-title: Immunology.
  doi: 10.1111/imm.12594
– volume: 12
  start-page: 1
  year: 2015
  ident: 3594_CR3
  publication-title: Cell Rep
  doi: 10.1016/j.celrep.2015.06.005
– volume: 12
  start-page: e1005692
  year: 2016
  ident: 3594_CR20
  publication-title: PLoS Pathog
  doi: 10.1371/journal.ppat.1005692
– volume: 8
  start-page: 10432
  year: 2018
  ident: 3594_CR8
  publication-title: Sci Rep
  doi: 10.1038/s41598-018-28706-1
– volume: 20
  start-page: 867
  year: 2013
  ident: 3594_CR7
  publication-title: Clin Vaccine Immunol
  doi: 10.1128/CVI.00735-12
– ident: 3594_CR24
– volume: 12
  start-page: 20150627
  year: 2015
  ident: 3594_CR37
  publication-title: J R Soc Interface
  doi: 10.1098/rsif.2015.0627
SSID ssj0017805
Score 2.3379261
Snippet In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine...
Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the...
Abstract Background In 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza “swine flu” pandemic. However, antibodies induced...
SourceID doaj
pubmedcentral
proquest
gale
pubmed
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 256
SubjectTerms Amino Acid Sequence
Antibodies, Viral - blood
Antibodies, Viral - immunology
Antigen-antibody reactions
Antigenic determinants
Antigenic distance
Antigenic sites
Antigens, Viral - chemistry
Antigens, Viral - immunology
B cells
Comparative analysis
Computer Simulation
Cross Reactions
Epidemics
Epitopes
Gillespie algorithm
Hemagglutinin
Hemagglutinin Glycoproteins, Influenza Virus - chemistry
Hemagglutinin Glycoproteins, Influenza Virus - immunology
Humans
Immunologic factors
Influenza A Virus, H1N1 Subtype - immunology
Influenza vaccines
Influenza Vaccines - immunology
Medical research
Models, Immunological
Shape space
Swine influenza
Vaccination
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrZ3daxQxEMCHUhD6In60ulolFqEPEnq7yWWTxyqWKrQPaqFvIV_bHpS94t499L_vTHb3uEXQF18vc3CZmWRmuMlvAD4mFYWUseFVMpqjUzTcSF_xJjk5w_pZx0APnC8u1fmV_H49v94a9UU9YT0euFfciXBV5evYBIGpv5fSae2aMmmVoqhqnemlGPPGYmr4_4BI_eMTGa1OupI4bZxKJULWSS4mYSjT-v-8k7eC0rRhcisCnT2Dp0PqyE77n_wcdlL7Ap70wyQfXoLNoN_c_dPesLFFmlOYigz1R9jNRWCREkZcYGicMMzuYot2tWQLeioy3oWsu3X3ieF9g6J5XM4-XJ19_fXlnA_jE3iYK7PiSWqjFcF4osOiRhjt3SzQ7Ydn0DWYGPkoiJ-XnK-0Jy6Oj8phgBKN1Ji6HMBuu2zTa2DRCU8sMyFnaILaYFk5C4TvSipI42IB5ahNGwa2OI24uLO5xtDK9hawaAGbLWBFAZ8237nvyRp_lf5MRtpIEhU7f4C-Ygdfsf_ylQKOyMSWuBctNdbcuHXX2W8_f9hT1Eop67lUBRwPQs0S9xDc8E4BNUGorInk4UQSD2aYLH8YPcnSEnWztWm57mzOmgwm1nUBr3rP2mxMUA6qKllAPfG5yc6nK-3iNnPB8bIWRog3_0NVb2GvysdF8dIcwu7q9zq9w_Rr5d_nk_YIoDkq0g
  priority: 102
  providerName: Directory of Open Access Journals
Title Implementing sequence-based antigenic distance calculation into immunological shape space model
URI https://www.ncbi.nlm.nih.gov/pubmed/32560624
https://www.proquest.com/docview/2415293107
https://pubmed.ncbi.nlm.nih.gov/PMC7303933
https://doaj.org/article/3a22b7dfc3144b44a88af1e86ed32785
Volume 21
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3da9swED_6wWAvY9912wVvDPYwtCWWLMsPY7SjWTdoGd0CeROyJKeB4nRxAut_vzvZzmpW-pKH6BKs-77k7ncAb710XAhXssTniqFSlCwXRcJKb8QQ62flLA04n53L04n4Pk2nW9CtO2oZWN9Z2tE-qcny6sOf3zef0eA_BYNX8mM9IhQ2RoUQAdIJxrdhFyNTRqsczsS_fxUIvz9MG2UjhqVO2g3R3PkdvUAV8Pz_99q3wla_pfJWjBo_hkdtchkfNdrwBLZ89RQeNOsmb56BDlDAoT-omsVdEzWjQOZi5DABc85t7CilxIMYxWfb7V7xvFot4jkNk3TeMq4vzbWP0SMhaVio8xwm45NfX05Zu2CB2VTmK-aFypUkuB5nsOzhuSrM0JJ_RCs1JaZOheOEsOdNkaiCkHMKJw2GMF4KhcnNC9ipFpXfg9gZXhDaGRfDQogsx8JzaAngy0srcuMiGHXc1LZFH6clGFc6VCFK6kYCGiWggwQ0j-D95jPXDfbGvdTHJKQNJeFmhzcWy5luzVBzkyRF5krLsZDEBzVKmXLklfSOJ5lKI3hDItaEjFFR683MrOtaf_t5oY-QK6g5qZARvGuJygXewZp2kgE5QWBaPcrDHiWaru0dv-40SdMR9btVfrGudcircky9swheNpq1uRinLFUmIoKsp3O9m_dPqvllQA5Hd85zzvfvf6wDeJgEQ5BslB_Czmq59q8w9VoVA9jOphm-qvHXAewen5z_uBiEnzEGwdL-Anp6K1E
linkProvider Scholars Portal
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Implementing+sequence-based+antigenic+distance+calculation+into+immunological+shape+space+model&rft.jtitle=BMC+bioinformatics&rft.au=Anderson%2C+Christopher+S&rft.au=Sangster%2C+Mark+Y&rft.au=Yang%2C+Hongmei&rft.au=Mariani%2C+Thomas+J&rft.date=2020-06-19&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=21&rft.issue=1&rft_id=info:doi/10.1186%2Fs12859-020-03594-3&rft.externalDocID=A627147546
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon