Evidence map›Paper›PMID 34123321›Full record

ArticleChemical science2021

Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies.

Jiahui Chen, Kaifu Gao, Rui Wang, Guo-Wei Wei

Abstract read
In one paragraph

Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Antivirals in COVID-19: A Focus on Pediatric Cardiac Patients.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Topological deep learning based deep mutational scanning.Computers in biology and medicine · 2023
    Article
  14. Persistent topological Laplacian analysis of SARS-CoV-2 variants.Journal of computational biophysics and chemistry · 2023
    Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Review

7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Jiahui ChenDepartment of Mathematics, Michigan State University MI 48824 USA weig@msu.edu.
Kaifu GaoDepartment of Mathematics, Michigan State University MI 48824 USA weig@msu.edu.
Rui WangDepartment of Mathematics, Michigan State University MI 48824 USA weig@msu.edu.ORCID https://orcid.org/0000-0002-7402-6372
Guo-Wei WeiDepartment of Mathematics, Michigan State University MI 48824 USA weig@msu.edu.ORCID https://orcid.org/0000-0001-8132-5998

Funding

AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodiesR01AI164266 · NIAID · UNIVERSITY OF GEORGIA · PI Guowei Wei, YONG-HUI ZHENG · 2022 to 2026
$2.7M
Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impactsR01GM126189 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WEI, GUOWEI · 2018 to 2021
$1.4M
NIAID NIH HHS R01 AI164266NIGMS NIH HHS R01 GM126189
6 · The paper itself

Abstract

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.

Identifiers

PMID34123321
PMCPMC8153213

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.