Evidence map›Paper›PMID 37258931›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Immunoinformatics Approaches in Designing Vaccines Against COVID-19.

Ankita Chakraborty, Jagadeesh Bayry, Suprabhat Mukherjee

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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
7.5field-weighted citation impact, top 2% 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, 9 citations in OpenAlex.

  1. Article
  2. Frontiers in immunology · 2025
    Article
  3. Article
  4. Article
  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

3 authors at 2 institutions in 1 country.

Ankita ChakrabortyIntegrative Biochemistry and Immunology Laboratory, Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India.
Jagadeesh BayryDepartment of Biological Sciences & Engineering, Indian Institute of Technology Palakkad, Palakkad, Kerala, India. bayry@iitpkd.ac.in.ORCID https://orcid.org/0000-0003-0498-9808
Suprabhat MukherjeeIntegrative Biochemistry and Immunology Laboratory, Department of Animal Science, Kazi Nazrul University, Asansol, West Bengal, India. suprabhat.mukherjee@knu.ac.in.ORCID https://orcid.org/0000-0002-5709-9190
Kazi Nazrul University · INIndian Institute of Technology Palakkad · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the onset of the COVID-19 pandemic, a number of approaches have been adopted by the scientific communities for developing efficient vaccine candidate against SARS-CoV-2. Conventional approaches of developing a vaccine require a long time and a series of trials and errors which indeed limit the feasibility of such approaches for developing a dependable vaccine in an emergency situation like the COVID-19 pandemic. Hitherto, most of the available vaccines have been developed against a particular antigen of SARS-CoV, spike protein in most of the cases, and intriguingly, these vaccines are not effective against all the pathogenic coronaviruses. In this context, immunoinformatics-based reverse vaccinology approaches enable a robust design of efficacious peptide-based vaccines against all the infectious strains of coronaviruses within a short frame of time. In this chapter, we enumerate the methodological trajectory of developing a universal anti-SARS-CoV-2 vaccine, namely, "AbhiSCoVac," through advanced computational biology-based immunoinformatics approach and its in-silico validation using molecular dynamics simulations.

Indexed as

COVID-19Viral VaccinesComputational BiologyCOVID-19 VaccinesEpitopes, B-LymphocyteEpitopes, T-LymphocyteHumansMolecular Docking SimulationPandemicsSARS-CoV-2Vaccines, SubunitCOVID-19 VaccinesEpitopes, B-LymphocyteEpitopes, T-LymphocyteVaccines, SubunitViral VaccinesImmunoinformaticsMolecular dynamics simulationReverse vaccinologySARS-CoV-2Vaccine

Identifiers

PMID37258931
OpenAlexW4378801452

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.