Evidence map›Paper›PMID 36008961›Full record

ArticleBiomolecules2022

CoVM

Hongjun Chen, Xiaotian Hu, Yanshi Hu, Jiawen Zhou, Ming Chen

Open access · goldAbstract read
In one paragraph

Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

5 authors at 2 institutions in 1 country.

Hongjun ChenDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-0936-2599
Xiaotian HuDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0003-2215-8792
Yanshi HuDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0003-3219-3778
Jiawen ZhouChu Kochen Honors College, Zhejiang University, Hangzhou 310058, China.
Ming ChenDepartment of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-9677-1699
Zhejiang University · CNFirst Affiliated Hospital Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2-human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2-human protein-protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale.

Indexed as

COVID-19SARS-CoV-2HumansMutationPandemicsViral ProteinsViral Proteinsbinding affinitydata integrationmolecular biologyprotein structure dockingSARS-CoV-2virus–host interactions

Identifiers

PMID36008961
PMCPMC9405999
OpenAlexW4289523493

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.