Evidence map›Paper›PMID 35441904›Full record

ArticleJournal, genetic engineering & biotechnology2022

Immunoinformatics approach of epitope prediction for SARS-CoV-2.

Nourelislam Awad, Rania Hassan Mohamed, Nehal I Ghoneim, Ahmed O Elmehrath, Nagwa El-Badri

Erratum issuedOpen access · diamondAbstract read
In one paragraph

Article in Journal, genetic engineering & biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-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

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Nourelislam AwadCenter of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, Giza, Egypt.
Rania Hassan MohamedCenter of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, Giza, Egypt.
Nehal I GhoneimCenter of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, Giza, Egypt.
Ahmed O ElmehrathCenter of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, Giza, Egypt.
Nagwa El-BadriCenter of Excellence for Stem Cells and Regenerative Medicine (CESC), Zewail City of Science and Technology, Giza, Egypt. nelbadri@zewailcity.edu.eg.
Zewail City of Science and Technology · EGNile University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe novel coronavirus (SARS-CoV-2) caused lethal infections worldwide during an unprecedented pandemic. Identification of the candidate viral epitopes is the first step in the design of vaccines against the viral infection. Several immunoinformatic approaches were employed to identify the SARS-CoV-2 epitopes that bind specifically with the major histocompatibility molecules class I (MHC-I). We utilized immunoinformatic tools to analyze the whole viral protein sequences, to identify the SARS-CoV-2 epitopes responsible for binding to the most frequent human leukocyte antigen (HLA) alleles in the Egyptian population. These alleles were also found with high frequency in other populations worldwide.

resultsMolecular docking approach showed that using the co-crystallized MHC-I and T cell receptor (TCR) instead of using MHC-I structure only, significantly enhanced docking scores and stabilized the conformation, as well as the binding affinity of the identified SARS-CoV-2 epitopes. Our approach directly predicts 7 potential vaccine subunits from the available SARS-CoV-2 spike and ORF1ab protein sequence. This prediction has been confirmed by published experimentally validated and in silico predicted spike epitope. On the other hand, we predicted novel epitopes (RDLPQGFSA and FCLEASFNY) showing high docking scores and antigenicity response with both MHC-I and TCR. Moreover, antigenicity, allergenicity, toxicity, and physicochemical properties of the predicted SARS-CoV-2 epitopes were evaluated via state-of-the-art bioinformatic approaches, showing high efficacy of the proposed epitopes as a vaccine candidate.

conclusionOur predicted SARS-CoV-2 epitopes can facilitate vaccine development to enhance the immunogenicity against SARS-CoV-2 and provide supportive data for further experimental validation. Our proposed molecular docking approach of exploiting both MHC and TCR structures can be used to identify potential epitopes for most microbial pathogens, provided the crystal structure of MHC co-crystallized with TCR.

Indexed as

ImmunoinformaticsMHC class I epitopesORF1ab proteinSARS-CoV-2Spike protein

Identifiers

PMID35441904
PMCPMC9019534
OpenAlexW4224281762

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.