Evidence map›Paper›PMID 33351863›Full record

ArticlePloS one2020

Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.

Muhammad Tahir Ul Qamar, Abdur Rehman, Kishver Tusleem, Usman Ali Ashfaq, Muhammad Qasim, Xitong Zhu, Israr Fatima, Farah Shahid, Ling-Ling Chen

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers.

0numbers the graph read from it
0cells of the map it votes in
111citing 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

111 citing papers in PubMed.

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

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

9 authors.

Muhammad Tahir Ul QamarCollege of Life Science and Technology, Guangxi University, Nanning, P. R. China.ORCID 0000-0003-4832-4250
Abdur RehmanDepartment of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.
Kishver TusleemFatima Jinnah Medical University, Lahore, Pakistan.
Usman Ali AshfaqDepartment of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.
Muhammad QasimDepartment of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.
Xitong ZhuHubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.
Israr FatimaDepartment of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.
Farah ShahidDepartment of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.
Ling-Ling ChenCollege of Life Science and Technology, Guangxi University, Nanning, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Indexed as

Amino Acid SequenceComputational BiologyCOVID-19COVID-19 VaccinesEpitopes, B-LymphocyteEpitopes, T-LymphocyteHumansMolecular Docking SimulationMolecular Dynamics SimulationSARS-CoV-2Spike Glycoprotein, CoronavirusViral Envelope ProteinsCOVID-19 VaccinesEpitopes, B-LymphocyteEpitopes, T-LymphocyteSpike Glycoprotein, CoronavirusViral Envelope Proteins

Identifiers

PMID33351863
PMCPMC7755200

What OpenQuestion holds

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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.