Evidence map›Paper›PMID 34273397›Full record

ArticleJournal of molecular biology2021

Revealing the Threat of Emerging SARS-CoV-2 Mutations to Antibody Therapies.

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

Abstract read
In one paragraph

Article in Journal of molecular biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 1 pooled it
–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

56 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Incipient functional SARS-CoV-2 diversification identified through neural network haplotype maps.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  11. Article
  12. Review
  13. Persistent topological Laplacian analysis of SARS-CoV-2 variants.Journal of computational biophysics and chemistry · 2023
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. The Omicron variant of concern: Diversification and convergent evolution in spike protein, and escape from anti-Spike monoclonal antibodies.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2022
    Review
  20. Article
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.
Kaifu GaoDepartment of Mathematics, Michigan State University, MI 48824, USA.
Rui WangDepartment of Mathematics, Michigan State University, MI 48824, USA.
Guo-Wei WeiDepartment of Mathematics, Michigan State University, MI 48824, USA; Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA; Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA. Electronic address: weig@msu.edu.

Funding

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
NIGMS NIH HHS R01 GM126189
6 · The paper itself

Abstract

The ongoing massive vaccination and the development of effective intervention offer the long-awaited hope to end the global rage of the COVID-19 pandemic. However, the rapidly growing SARS-CoV-2 variants might compromise existing vaccines and monoclonal antibody (mAb) therapies. Although there are valuable experimental studies about the potential threats from emerging variants, the results are limited to a handful of mutations and Eli Lilly and Regeneron mAbs. The potential threats from frequently occurring mutations on the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) to many mAbs in clinical trials are largely unknown. We fill the gap by developing a topology-based deep learning strategy that is validated with tens of thousands of experimental data points. We analyze 796,759 genome isolates from patients to identify 606 non-degenerate RBD mutations and investigate their impacts on 16 mAbs in clinical trials. Our findings, which are highly consistent with existing experimental results about Alpha, Beta, Gamma, Delta, Epsilon, and Kappa variants shed light on potential threats of 100 most observed mutations to mAbs not only from Eli Lilly and Regeneron but also from Celltrion and Rockefeller University that are in clinical trials. We unveil, for the first time, that high-frequency mutations R346K/S, N439K, G446V, L455F, V483F/A, F486L, F490L/S, Q493L, and S494P might compromise some of mAbs in clinical trials. Our study gives rise to a general perspective about how mutations will affect current vaccines.

Indexed as

COVID-19 Drug TreatmentAntibodies, MonoclonalAntibodies, NeutralizingAntibodies, ViralCOVID-19HumansMutationPandemicsProtein BindingSARS-CoV-2Spike Glycoprotein, CoronavirusAntibodies, MonoclonalAntibodies, NeutralizingAntibodies, ViralSpike Glycoprotein, Coronavirusantibodyclinical trialdeep learningmutationvariant

Identifiers

PMID34273397
PMCPMC8277955

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.