Evidence map›Paper›PMID 34043002›Full record

ArticleBioinformatics (Oxford, England)2021

Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and greater coronavirus family.

Chen Wang, Daniel M Konecki, David C Marciano, Harikumar Govindarajan, Amanda M Williams, Brigitta Wastuwidyaningtyas, Thomas Bourquard, Panagiotis Katsonis, Olivier Lichtarge

Open access · hybridAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 35% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
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  3. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Chen WangDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0001-5769-2077
Daniel M KoneckiQuantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA.
David C MarcianoDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Harikumar GovindarajanDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Amanda M WilliamsCancer and Cell Biology Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA.
Brigitta WastuwidyaningtyasDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Thomas BourquardDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Panagiotis KatsonisDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Olivier LichtargeDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
Baylor College of Medicine · US

Funding

Functional Determinnats in G Protein-coupled ReceptorsR01GM066099 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2003 to 2022
$7.5M
A Knowledge Map to Find Alzheimer's Disease DrugsR01AG061105 · NIA · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2018 to 2022
$4.6M
Comparative genomics of protein structure and functionR01GM079656 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2007 to 2019
$4.3M
NIA NIH HHS R01 AG061105NIGMS NIH HHS R01 GM066099NIGMS NIH HHS R01 GM079656
6 · The paper itself

Abstract

motivationSince the first recognized case of COVID-19, more than 100 million people have been infected worldwide. Global efforts in drug and vaccine development to fight the disease have yielded vaccines and drug candidates to cure COVID-19. However, the spread of SARS-CoV-2 variants threatens the continued efficacy of these treatments. In order to address this, we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify evolutionarily conserved functional sites that can inform the search for treatments with broader coverage across the coronavirus family.

resultsCombining coronavirus family sequence information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that may provide useful drug and vaccine targets and which are less likely to be compromised by the emergence of new virus strains. Several experimentally validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches stem from evolutionary principles that are agnostic to the organism or infective agent. AVAILABILITY AND IMPLEMENTATION: The results of this work are made interactively available at http://cov.lichtargelab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

COVID-19Viral VaccinesCOVID-19 VaccinesHumansProteomeSARS-CoV-2COVID-19 VaccinesProteomeViral Vaccines

Identifiers

PMID34043002
PMCPMC8243408
OpenAlexW3132545930

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.