Evidence map›Paper›PMID 36156540›Full record

ArticlePLoS computational biology2022

Design of immunogens for eliciting antibody responses that may protect against SARS-CoV-2 variants.

Eric Wang, Arup K Chakraborty

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 27% of its field
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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Bioinformatics advances · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Eric WangInstitute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0003-0330-0597
Arup K ChakrabortyInstitute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.ORCID 0000-0003-1268-9602
Massachusetts Institute of Technology · USRagon Institute of MGH, MIT and Harvard · US

Funding

Combinatorial and computational design of bnAb mRNA vaccines for HIVR61AI161805 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI ANDERSON, DANIEL G · 2021 to 2023
$2.1M
NIAID NIH HHS R61 AI161805
6 · The paper itself

Abstract

The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2' cleavage site and fusion peptide as potential pan-coronavirus vaccine targets.

Indexed as

COVID-19SARS-CoV-2Angiotensin-Converting Enzyme 2Antibodies, NeutralizingAntibodies, ViralAntibody FormationCOVID-19 VaccinesHumansSpike Glycoprotein, CoronavirusAngiotensin-Converting Enzyme 2Antibodies, NeutralizingAntibodies, ViralCOVID-19 VaccinesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2

Identifiers

PMID36156540
PMCPMC9536555
OpenAlexW4297265059

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.