Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants

It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typicall...
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Autor*in:	Ye-Fan Hu [verfasserIn] Jing-Chu Hu [verfasserIn] Hua-Rui Gong [verfasserIn] Antoine Danchin [verfasserIn] Ren Sun [verfasserIn] Hin Chu [verfasserIn] Ivan Fan-Ngai Hung [verfasserIn] Kwok Yung Yuen [verfasserIn] Kelvin Kai-Wang To [verfasserIn] Bao-Zhong Zhang [verfasserIn] Thomas Yau [verfasserIn] Jian-Dong Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 13(2022)
Übergeordnetes Werk:	volume:13 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2022.861050

Katalog-ID:	DOAJ023384948

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520			\|a It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online.
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653		0	\|a Immunologic diseases. Allergy
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allfields	10.3389/fimmu.2022.861050 doi (DE-627)DOAJ023384948 (DE-599)DOAJf3cd4e1825a3418ca89f77f950a187d6 DE-627 ger DE-627 rakwb eng RC581-607 Ye-Fan Hu verfasserin aut Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online. SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy Ye-Fan Hu verfasserin aut Jing-Chu Hu verfasserin aut Hua-Rui Gong verfasserin aut Antoine Danchin verfasserin aut Antoine Danchin verfasserin aut Ren Sun verfasserin aut Hin Chu verfasserin aut Ivan Fan-Ngai Hung verfasserin aut Kwok Yung Yuen verfasserin aut Kelvin Kai-Wang To verfasserin aut Bao-Zhong Zhang verfasserin aut Thomas Yau verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.861050 kostenfrei https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
spelling	10.3389/fimmu.2022.861050 doi (DE-627)DOAJ023384948 (DE-599)DOAJf3cd4e1825a3418ca89f77f950a187d6 DE-627 ger DE-627 rakwb eng RC581-607 Ye-Fan Hu verfasserin aut Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online. SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy Ye-Fan Hu verfasserin aut Jing-Chu Hu verfasserin aut Hua-Rui Gong verfasserin aut Antoine Danchin verfasserin aut Antoine Danchin verfasserin aut Ren Sun verfasserin aut Hin Chu verfasserin aut Ivan Fan-Ngai Hung verfasserin aut Kwok Yung Yuen verfasserin aut Kelvin Kai-Wang To verfasserin aut Bao-Zhong Zhang verfasserin aut Thomas Yau verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.861050 kostenfrei https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfields_unstemmed	10.3389/fimmu.2022.861050 doi (DE-627)DOAJ023384948 (DE-599)DOAJf3cd4e1825a3418ca89f77f950a187d6 DE-627 ger DE-627 rakwb eng RC581-607 Ye-Fan Hu verfasserin aut Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online. SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy Ye-Fan Hu verfasserin aut Jing-Chu Hu verfasserin aut Hua-Rui Gong verfasserin aut Antoine Danchin verfasserin aut Antoine Danchin verfasserin aut Ren Sun verfasserin aut Hin Chu verfasserin aut Ivan Fan-Ngai Hung verfasserin aut Kwok Yung Yuen verfasserin aut Kelvin Kai-Wang To verfasserin aut Bao-Zhong Zhang verfasserin aut Thomas Yau verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.861050 kostenfrei https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsGer	10.3389/fimmu.2022.861050 doi (DE-627)DOAJ023384948 (DE-599)DOAJf3cd4e1825a3418ca89f77f950a187d6 DE-627 ger DE-627 rakwb eng RC581-607 Ye-Fan Hu verfasserin aut Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online. SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy Ye-Fan Hu verfasserin aut Jing-Chu Hu verfasserin aut Hua-Rui Gong verfasserin aut Antoine Danchin verfasserin aut Antoine Danchin verfasserin aut Ren Sun verfasserin aut Hin Chu verfasserin aut Ivan Fan-Ngai Hung verfasserin aut Kwok Yung Yuen verfasserin aut Kelvin Kai-Wang To verfasserin aut Bao-Zhong Zhang verfasserin aut Thomas Yau verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.861050 kostenfrei https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
allfieldsSound	10.3389/fimmu.2022.861050 doi (DE-627)DOAJ023384948 (DE-599)DOAJf3cd4e1825a3418ca89f77f950a187d6 DE-627 ger DE-627 rakwb eng RC581-607 Ye-Fan Hu verfasserin aut Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online. SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy Ye-Fan Hu verfasserin aut Jing-Chu Hu verfasserin aut Hua-Rui Gong verfasserin aut Antoine Danchin verfasserin aut Antoine Danchin verfasserin aut Ren Sun verfasserin aut Hin Chu verfasserin aut Ivan Fan-Ngai Hung verfasserin aut Kwok Yung Yuen verfasserin aut Kelvin Kai-Wang To verfasserin aut Bao-Zhong Zhang verfasserin aut Thomas Yau verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut Jian-Dong Huang verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 13(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:13 year:2022 https://doi.org/10.3389/fimmu.2022.861050 kostenfrei https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022
language	English
source	In Frontiers in Immunology 13(2022) volume:13 year:2022
sourceStr	In Frontiers in Immunology 13(2022) volume:13 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity Immunologic diseases. Allergy
isfreeaccess_bool	true
container_title	Frontiers in Immunology
authorswithroles_txt_mv	Ye-Fan Hu @@aut@@ Jing-Chu Hu @@aut@@ Hua-Rui Gong @@aut@@ Antoine Danchin @@aut@@ Ren Sun @@aut@@ Hin Chu @@aut@@ Ivan Fan-Ngai Hung @@aut@@ Kwok Yung Yuen @@aut@@ Kelvin Kai-Wang To @@aut@@ Bao-Zhong Zhang @@aut@@ Thomas Yau @@aut@@ Jian-Dong Huang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	657998354
id	DOAJ023384948
language_de	englisch
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callnumber-first	R - Medicine
author	Ye-Fan Hu
spellingShingle	Ye-Fan Hu misc RC581-607 misc SARS-CoV-2 misc variants of concern misc antigenicity prediction misc vaccine breakthrough variants misc computation of antigenicity misc Immunologic diseases. Allergy Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants
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topic_title	RC581-607 Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants SARS-CoV-2 variants of concern antigenicity prediction vaccine breakthrough variants computation of antigenicity
topic	misc RC581-607 misc SARS-CoV-2 misc variants of concern misc antigenicity prediction misc vaccine breakthrough variants misc computation of antigenicity misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc SARS-CoV-2 misc variants of concern misc antigenicity prediction misc vaccine breakthrough variants misc computation of antigenicity misc Immunologic diseases. Allergy
topic_browse	misc RC581-607 misc SARS-CoV-2 misc variants of concern misc antigenicity prediction misc vaccine breakthrough variants misc computation of antigenicity misc Immunologic diseases. Allergy
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title	Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants
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title_full	Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants
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author_browse	Ye-Fan Hu Jing-Chu Hu Hua-Rui Gong Antoine Danchin Ren Sun Hin Chu Ivan Fan-Ngai Hung Kwok Yung Yuen Kelvin Kai-Wang To Bao-Zhong Zhang Thomas Yau Jian-Dong Huang
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title_sort	computation of antigenicity predicts sars-cov-2 vaccine breakthrough variants
callnumber	RC581-607
title_auth	Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants
abstract	It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online.
abstractGer	It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online.
abstract_unstemmed	It has been reported that multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta can reduce neutralization by antibodies, resulting in vaccine breakthrough infections. Virus–antiserum neutralization assays are typically performed to monitor potential vaccine breakthrough strains. However, experiment-based methods took several weeks whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralization titers. Based on this correlation, we obtained a computational model for the receptor-binding domain (RBD) of the spike protein to predict the fold decrease in virus–antiserum neutralization titers with high accuracy (~0.79). Our predicted results were comparable to experimental neutralization titers of variants, including Alpha, Beta, Delta, Gamma, Epsilon, Iota, Kappa, and Lambda, as well as SARS-CoV. Here, we predicted the fold of decrease of Omicron as 17.4-fold less susceptible to neutralization. We visualized all 1,521 SARS-CoV-2 lineages to indicate variants including Mu, B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs Beta, Gamma, Delta, and Omicron. Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online.
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title_short	Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants
url	https://doi.org/10.3389/fimmu.2022.861050 https://doaj.org/article/f3cd4e1825a3418ca89f77f950a187d6 https://www.frontiersin.org/articles/10.3389/fimmu.2022.861050/full https://doaj.org/toc/1664-3224
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author2Str	Ye-Fan Hu Jing-Chu Hu Hua-Rui Gong Antoine Danchin Ren Sun Hin Chu Ivan Fan-Ngai Hung Kwok Yung Yuen Kelvin Kai-Wang To Bao-Zhong Zhang Thomas Yau Jian-Dong Huang
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up_date	2024-07-03T17:18:29.078Z
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