Insights on the interaction of SARS-CoV-2 variant B.1.617.2 with antibody CR3022 and analysis of antibody resistance.

CR3022 Delta Homology modelling Molecular docking Molecular dynamics SARS-CoV-2

Journal

Journal, genetic engineering & biotechnology
ISSN: 2090-5920
Titre abrégé: J Genet Eng Biotechnol
Pays: Netherlands
ID NLM: 101317150

Informations de publication

Date de publication:
20 Mar 2023
Historique:
received: 11 10 2022
accepted: 12 03 2023
entrez: 20 3 2023
pubmed: 21 3 2023
medline: 21 3 2023
Statut: epublish

Résumé

The existence of mutated Delta (B.1.617.2) variants of SARS-CoV-2 causes rapid transmissibility, increase in virulence, and decrease in the effectiveness of public health. Majority of mutations are seen in the surface spike, and they are considered as antigenicity and immunogenicity of the virus. Hence, finding suitable cross antibody or natural antibody and understanding its biomolecular recognition for neutralizing surface spike are crucial for developing many clinically approved COVID-19 vaccines. Here, we aim to design SARS-CoV-2 variant and hence, to understand its mechanism, binding affinity and neutralization potential with several antibodies. In this study, we modelled six feasible spike protein (S1) configurations for Delta SARS-CoV-2 (B.1.617.2) and identified the best structure to interact with human antibodies. Initially, the impact of mutations at the receptor-binding domain (RBD) of B.1.617.2 was tested, and it is found that all mutations increase the stability of proteins (ΔΔG) and decrease the entropies. An exceptional case is noted for the mutation of G614D variant for which the vibration entropy change is found to be within the range of 0.133-0.004 kcal/mol/K. Temperature-dependent free energy change values (ΔG) for wild type is found to be - 0.1 kcal/mol, whereas all other cases exhibit values within the range of - 5.1 to - 5.5 kcal/mol. Mutation on spike increases the interaction with the glycoprotein antibody CR3022 and the binding affinity (CLUSpro energy =  - 99.7 kcal/mol). The docked Delta variant with the following antibodies, etesevimab, bebtelovimab, BD-368-2, imdevimab, bamlanivimab, and casirivimab, exhibit a substantially decreased docking score (- 61.7 to - 112.0 kcal/mol) and the disappearance of several hydrogen bond interactions. Characterization of antibody resistance for Delta variant with respect to the wild type gives understanding regarding why Delta variant endures the resistance boosted through several trademark vaccines. Several interactions with CR3022 have appeared compared to Wild for Delta variant, and hence, it is suggested that modification on the CR3022 antibody could further improve for the prevention of viral spread. Antibody resistance decreased significantly due to numerous hydrogen bond interactions which clearly indicate that these marketed/launched vaccines (etesevimab) will be effective for Delta variants.

Sections du résumé

BACKGROUND BACKGROUND
The existence of mutated Delta (B.1.617.2) variants of SARS-CoV-2 causes rapid transmissibility, increase in virulence, and decrease in the effectiveness of public health. Majority of mutations are seen in the surface spike, and they are considered as antigenicity and immunogenicity of the virus. Hence, finding suitable cross antibody or natural antibody and understanding its biomolecular recognition for neutralizing surface spike are crucial for developing many clinically approved COVID-19 vaccines. Here, we aim to design SARS-CoV-2 variant and hence, to understand its mechanism, binding affinity and neutralization potential with several antibodies.
RESULTS RESULTS
In this study, we modelled six feasible spike protein (S1) configurations for Delta SARS-CoV-2 (B.1.617.2) and identified the best structure to interact with human antibodies. Initially, the impact of mutations at the receptor-binding domain (RBD) of B.1.617.2 was tested, and it is found that all mutations increase the stability of proteins (ΔΔG) and decrease the entropies. An exceptional case is noted for the mutation of G614D variant for which the vibration entropy change is found to be within the range of 0.133-0.004 kcal/mol/K. Temperature-dependent free energy change values (ΔG) for wild type is found to be - 0.1 kcal/mol, whereas all other cases exhibit values within the range of - 5.1 to - 5.5 kcal/mol. Mutation on spike increases the interaction with the glycoprotein antibody CR3022 and the binding affinity (CLUSpro energy =  - 99.7 kcal/mol). The docked Delta variant with the following antibodies, etesevimab, bebtelovimab, BD-368-2, imdevimab, bamlanivimab, and casirivimab, exhibit a substantially decreased docking score (- 61.7 to - 112.0 kcal/mol) and the disappearance of several hydrogen bond interactions.
CONCLUSION CONCLUSIONS
Characterization of antibody resistance for Delta variant with respect to the wild type gives understanding regarding why Delta variant endures the resistance boosted through several trademark vaccines. Several interactions with CR3022 have appeared compared to Wild for Delta variant, and hence, it is suggested that modification on the CR3022 antibody could further improve for the prevention of viral spread. Antibody resistance decreased significantly due to numerous hydrogen bond interactions which clearly indicate that these marketed/launched vaccines (etesevimab) will be effective for Delta variants.

Identifiants

pubmed: 36940010
doi: 10.1186/s43141-023-00492-y
pii: 10.1186/s43141-023-00492-y
pmc: PMC10026237
doi:

Types de publication

Journal Article

Langues

eng

Pagination

35

Subventions

Organisme : SERB-NPDF
ID : PDF/2020/001703

Informations de copyright

© 2023. The Author(s).

Références

Nucleic Acids Res. 2018 Jul 2;46(W1):W350-W355
pubmed: 29718330
Turk J Med Sci. 2020 Apr 15;50(SI-1):549-556
pubmed: 32293832
Antimicrob Agents Chemother. 2021 Jun 17;65(7):e0009721
pubmed: 33903110
Euro Surveill. 2021 Jul;26(26):
pubmed: 34212838
Nat Med. 2021 Jul;27(7):1131-1133
pubmed: 34045737
Front Cell Infect Microbiol. 2020 Nov 25;10:587269
pubmed: 33324574
Nucleic Acids Res. 2018 Jul 2;46(W1):W296-W303
pubmed: 29788355
Bioinformatics. 2012 Oct 15;28(20):2608-14
pubmed: 23053206
Vaccines (Basel). 2021 Nov 30;9(12):
pubmed: 34960156
Emerg Microbes Infect. 2020 Dec;9(1):382-385
pubmed: 32065055
J Biomol Struct Dyn. 2023 Jan;41(1):81-90
pubmed: 34796779
Science. 2021 Feb 19;371(6531):850-854
pubmed: 33495308
Biochem Biophys Res Commun. 2021 Jan 29;538:108-115
pubmed: 33220921
J Mol Biol. 1999 Sep 17;292(2):195-202
pubmed: 10493868
J Biomol Struct Dyn. 2021 Oct;39(17):6761-6771
pubmed: 32762537
Comput Biol Med. 2021 Aug;135:104654
pubmed: 34346317
Bioinformatics. 2017 Nov 01;33(21):3415-3422
pubmed: 29036273
Virology. 2021 Sep;561:107-116
pubmed: 34217923
J Mol Biol. 1984 Sep 5;178(1):63-89
pubmed: 6548264
Nat Commun. 2020 Aug 21;11(1):4198
pubmed: 32826914
Science. 2020 May 8;368(6491):630-633
pubmed: 32245784
Cell Host Microbe. 2020 Sep 9;28(3):445-454.e6
pubmed: 32585135
Phys Chem Chem Phys. 2019 May 15;21(19):10135-10145
pubmed: 31062799
Cell Host Microbe. 2021 May 12;29(5):747-751.e4
pubmed: 33887205
Ann N Y Acad Sci. 2020 Aug;1473(1):3-10
pubmed: 32396683
Sci Rep. 2021 Nov 5;11(1):21735
pubmed: 34741079
Comb Chem High Throughput Screen. 2021;24(7):1069-1082
pubmed: 33106140
Infect Genet Evol. 2020 Nov;85:104517
pubmed: 32882432
J Infect Dis. 2021 Sep 17;224(6):989-994
pubmed: 34260717
JAMA. 2021 Apr 6;325(13):1261-1262
pubmed: 33571363
Int J Infect Dis. 2021 Feb;103:611-616
pubmed: 33075532
Brief Bioinform. 2022 Jan 17;23(1):
pubmed: 34553217
Methods Mol Biol. 2015;1282:1-23
pubmed: 25720466
J Chem Inf Model. 2021 Oct 25;61(10):5133-5140
pubmed: 34648284
Nature. 2020 May;581(7807):215-220
pubmed: 32225176
Nature. 2021 May;593(7857):130-135
pubmed: 33684923

Auteurs

Sandhya Ks (S)

Department of Computational Biology and Bioinformatics, University of Kerala, Kerala, Thiruvananthapuram, India. drkssandhya@gmail.com.
Malankara Catholic College, Mariagiri, Kaliakkavilai, Kanyakumari, 629153, Tamil Nadu, India. drkssandhya@gmail.com.

Achuthsankar S Nair (AS)

Department of Computational Biology and Bioinformatics, University of Kerala, Kerala, Thiruvananthapuram, India.

Classifications MeSH