Evidence map›Paper›PMID 33568759›Full record

ArticleScientific reports2021

Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus).

Thanh Thi Nguyen, Pubudu N Pathirana, Thin Nguyen, Quoc Viet Hung Nguyen, Asim Bhatti, Dinh C Nguyen, Dung Tien Nguyen, Ngoc Duy Nguyen, Douglas Creighton, Mohamed Abdelrazek

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
9.8field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 114 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Thanh Thi NguyenSchool of Information Technology, Deakin University, Victoria, Australia. thanh.nguyen@deakin.edu.au.ORCID 0000-0001-9709-1663
Pubudu N PathiranaSchool of Engineering, Deakin University, Victoria, Australia.
Thin NguyenApplied Artificial Intelligence Institute (A2I2), Deakin University, Victoria, Australia.
Quoc Viet Hung NguyenSchool of Information and Communication Technology, Griffith University, Queensland, Australia.ORCID 0000-0002-9687-1315
Asim BhattiInstitute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia.ORCID 0000-0001-6876-1437
Dinh C NguyenSchool of Engineering, Deakin University, Victoria, Australia.
Dung Tien NguyenSchool of Information Technology, Deakin University, Victoria, Australia.
Ngoc Duy NguyenInstitute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia.
Douglas CreightonInstitute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia.
Mohamed AbdelrazekSchool of Information Technology, Deakin University, Victoria, Australia.
Deakin University · AUGriffith University · AU

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 highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

Indexed as

Genome, ViralMutationProtein Structure, SecondaryCOVID-19DNA, ViralGenomicsHumansSARS-CoV-2Spike Glycoprotein, CoronavirusDNA, ViralSpike Glycoprotein, Coronavirus

Identifiers

PMID33568759
PMCPMC7876117
OpenAlexW3127652512

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.