Evidence map›Paper›PMID 33786418›Full record

ArticleAdvances in cell and gene therapy2021

An in silico analysis of effective siRNAs against COVID-19 by targeting the leader sequence of SARS-CoV-2.

Anand Kumar Pandey, Shalja Verma

Abstract read
In one paragraph

Article in Advances in cell and gene therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Potential of siRNA in COVID-19 therapy: Emphasis onFrontiers in bioengineering and biotechnology · 2023
    Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. 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

2 authors.

Anand Kumar PandeyDepartment of Biotechnology Engineering Institute of Engineering and Technology Bundelkhand University Jhansi India.ORCID https://orcid.org/0000-0003-3256-4663
Shalja VermaDepartment of Biotechnology Engineering Institute of Engineering and Technology Bundelkhand University Jhansi India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a retrovirus having genome size of around 30 kb. Its genome contains a highly conserved leader sequence at its 5' end, which is added to all subgenomic mRNAs at their 5' terminus by a discontinuous transcription mechanism and regulates their translation. Targeting the leader sequence by RNA interference can be an effective approach to inhibit the viral replication. In the present study an in-silico prediction of highly effective siRNAs was performed to target the leader sequence using the online software siDirect version 2.0. Low seed-duplex stability, exact complementarity with target, at least three mismatches with any off-target and least number of off-targets, were considered as effective criteria for highly specific siRNA. Further validation of siRNA affinity for the target was accomplished by molecular docking by HNADOCK online server. Our results revealed four potential siRNAs, of which siRNA having guide strand sequence 5'GUUUAGAGAACAGAUCUACAA3' met almost all specificity criteria with no off-targets for guide strand. Molecular docking of all predicted siRNAs (guide strand) with the target leader sequence depicted highest binding score of -327.45 for above-mentioned siRNA. Furthermore, molecular docking of the passenger strand of the best candidate with off-target sequences gave significantly low binding scores. Hence, 5'GUUUAGAGAACAGAUCUACAA3' siRNA possess great potential to silence the leader sequence of SARS-CoV-2 with least off-target effect. Present study provides great scope for development of gene therapy against the prevailing COVID-19 disease, thus further research in this concern is urgently demanded.

Indexed as

leader sequenceoff‐target effectRNAi therapySARS‐CoV‐2siRNA

Identifiers

PMID33786418
PMCPMC7995175

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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.