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Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies
Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic. They, however, are prone to over 5000 mutations on the spike (S) protein uncovered by a Mutation Tracker based on over 200 000 genome isolates. It is imperative to understand how mutations will impact v...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153213/ https://www.ncbi.nlm.nih.gov/pubmed/34123321 http://dx.doi.org/10.1039/d1sc01203g |
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author | Chen, Jiahui Gao, Kaifu Wang, Rui Wei, Guo-Wei |
author_facet | Chen, Jiahui Gao, Kaifu Wang, Rui Wei, Guo-Wei |
author_sort | Chen, Jiahui |
collection | PubMed |
description | Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic. They, however, are prone to over 5000 mutations on the spike (S) protein uncovered by a Mutation Tracker based on over 200 000 genome isolates. It is imperative to understand how mutations will impact vaccines and antibodies in development. In this work, we first study the mechanism, frequency, and ratio of mutations on the S protein which is the common target of most COVID-19 vaccines and antibody therapies. Additionally, we build a library of 56 antibody structures and analyze their 2D and 3D characteristics. Moreover, we predict the mutation-induced binding free energy (BFE) changes for the complexes of S protein and antibodies or ACE2. By integrating genetics, biophysics, deep learning, and algebraic topology, we reveal that most of the 462 mutations on the receptor-binding domain (RBD) will weaken the binding of S protein and antibodies and disrupt the efficacy and reliability of antibody therapies and vaccines. A list of 31 antibody disrupting mutants is identified, while many other disruptive mutations are detailed as well. We also unveil that about 65% of the existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, will strengthen the binding between the S protein and human angiotensin-converting enzyme 2 (ACE2), resulting in more infectious COVID-19 variants. We discover the disparity between the extreme values of RBD mutation-induced BFE strengthening and weakening of the bindings with antibodies and angiotensin-converting enzyme 2 (ACE2), suggesting that SARS-CoV-2 is at an advanced stage of evolution for human infection, while the human immune system is able to produce optimized antibodies. This discovery, unfortunately, implies the vulnerability of current vaccines and antibody drugs to new mutations. Our predictions were validated by comparison with more than 1400 deep mutations on the S protein RBD. Our results show the urgent need to develop new mutation-resistant vaccines and antibodies and to prepare for seasonal vaccinations. |
format | Online Article Text |
id | pubmed-8153213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-81532132021-06-11 Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies Chen, Jiahui Gao, Kaifu Wang, Rui Wei, Guo-Wei Chem Sci Chemistry Antibody therapeutics and vaccines are among our last resort to end the raging COVID-19 pandemic. They, however, are prone to over 5000 mutations on the spike (S) protein uncovered by a Mutation Tracker based on over 200 000 genome isolates. It is imperative to understand how mutations will impact vaccines and antibodies in development. In this work, we first study the mechanism, frequency, and ratio of mutations on the S protein which is the common target of most COVID-19 vaccines and antibody therapies. Additionally, we build a library of 56 antibody structures and analyze their 2D and 3D characteristics. Moreover, we predict the mutation-induced binding free energy (BFE) changes for the complexes of S protein and antibodies or ACE2. By integrating genetics, biophysics, deep learning, and algebraic topology, we reveal that most of the 462 mutations on the receptor-binding domain (RBD) will weaken the binding of S protein and antibodies and disrupt the efficacy and reliability of antibody therapies and vaccines. A list of 31 antibody disrupting mutants is identified, while many other disruptive mutations are detailed as well. We also unveil that about 65% of the existing RBD mutations, including those variants recently found in the United Kingdom (UK) and South Africa, will strengthen the binding between the S protein and human angiotensin-converting enzyme 2 (ACE2), resulting in more infectious COVID-19 variants. We discover the disparity between the extreme values of RBD mutation-induced BFE strengthening and weakening of the bindings with antibodies and angiotensin-converting enzyme 2 (ACE2), suggesting that SARS-CoV-2 is at an advanced stage of evolution for human infection, while the human immune system is able to produce optimized antibodies. This discovery, unfortunately, implies the vulnerability of current vaccines and antibody drugs to new mutations. Our predictions were validated by comparison with more than 1400 deep mutations on the S protein RBD. Our results show the urgent need to develop new mutation-resistant vaccines and antibodies and to prepare for seasonal vaccinations. The Royal Society of Chemistry 2021-04-13 /pmc/articles/PMC8153213/ /pubmed/34123321 http://dx.doi.org/10.1039/d1sc01203g Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by-nc/3.0/ |
spellingShingle | Chemistry Chen, Jiahui Gao, Kaifu Wang, Rui Wei, Guo-Wei Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title | Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title_full | Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title_fullStr | Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title_full_unstemmed | Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title_short | Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies |
title_sort | prediction and mitigation of mutation threats to covid-19 vaccines and antibody therapies |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8153213/ https://www.ncbi.nlm.nih.gov/pubmed/34123321 http://dx.doi.org/10.1039/d1sc01203g |
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