Evidence map›Paper›PMID 28356762›Full record

ArticleAdvances and applications in bioinformatics and chemistry : AABC2017

In silico-based vaccine design against Ebola virus glycoprotein.

Raju Dash, Rasel Das, Md Junaid, Md Forhad Chowdhury Akash, Ashekul Islam, Sm Zahid Hosen

Abstract read
In one paragraph

Article in Advances and applications in bioinformatics and chemistry : AABC, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

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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

38 citing papers in PubMed.

  1. Review
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  9. EnhancedJournal of virology · 2022
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  19. Computational Insights into the Deleterious Impacts of Missense Variants onInternational journal of molecular sciences · 2021
    Article
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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

6 authors.

Raju DashMolecular Modeling and Drug Design Laboratory (MMDDL), Pharmacology Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR), Chittagong, Bangladesh.
Rasel DasNanotechnology and Catalysis Research Center, University of Malaya, Kuala Lumpur, Malaysia.
Md JunaidDepartment of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh.
Md Forhad Chowdhury AkashDepartment of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh.
Ashekul IslamDepartment of Biochemistry and Molecular Biology, University of Chittagong, Chittagong, Bangladesh.
Sm Zahid HosenMolecular Modeling and Drug Design Laboratory (MMDDL), Pharmacology Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR), Chittagong, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ebola virus (EBOV) is one of the lethal viruses, causing more than 24 epidemic outbreaks to date. Despite having available molecular knowledge of this virus, no definite vaccine or other remedial agents have been developed yet for the management and avoidance of EBOV infections in humans. Disclosing this, the present study described an epitope-based peptide vaccine against EBOV, using a combination of B-cell and T-cell epitope predictions, followed by molecular docking and molecular dynamics simulation approach. Here, protein sequences of all glycoproteins of EBOV were collected and examined via in silico methods to determine the most immunogenic protein. From the identified antigenic protein, the peptide region ranging from 186 to 220 and the sequence HKEGAFFLY from the positions of 154-162 were considered the most potential B-cell and T-cell epitopes, correspondingly. Moreover, this peptide (HKEGAFFLY) interacted with HLA-A*32:15 with the highest binding energy and stability, and also a good conservancy of 83.85% with maximum population coverage. The results imply that the designed epitopes could manifest vigorous enduring defensive immunity against EBOV.

Indexed as

Ebola virusepitopeglycoproteinvaccine design

Identifiers

PMID28356762
PMCPMC5367765

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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.