Evidence map›Paper›PMID 34761257›Full record

ArticleNucleic acids research2022

SCoV2-MD: a database for the dynamics of the SARS-CoV-2 proteome and variant impact predictions.

Mariona Torrens-Fontanals, Alejandro Peralta-García, Carmine Talarico, Ramon Guixà-González, Toni Giorgino, Jana Selent

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

22 citing papers in PubMed, 29 citations in OpenAlex.

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  14. Applying the digital data and the bioinformatics tools in SARS-CoV-2 research.Computational and structural biotechnology journal · 2023
    Review
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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 at 4 institutions in 3 countries.

Mariona Torrens-FontanalsResearch Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute-Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona 08003, Spain.ORCID 0000-0001-6233-8945
Alejandro Peralta-GarcíaResearch Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute-Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona 08003, Spain.
Carmine TalaricoEXSCALATE, Dompé Farmaceutici S.p.A., Via Tommaso De Amicis, 95, Napoli, 80131, Italy.
Ramon Guixà-GonzálezLaboratory of Biomolecular Research, Paul Scherrer Institute, CH-5232 Villigen PSI, Switzerland.
Toni GiorginoBiophysics Institute (CNR-IBF), National Research Council of Italy, Milan 20133, Italy.ORCID 0000-0001-6449-0596
Jana SelentResearch Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute-Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona 08003, Spain.ORCID 0000-0002-1844-4449
Universitat Pompeu Fabra · ESDompé (Italy) · ITPaul Scherrer Institute · CHUniversity of Milan · IT

Funding

CINECA GSNSDr3EU H2020 101003551Government of Catalonia DGRISMinistry of Science, Innovation and Universities FPU16/01209Swiss National Science Foundation 192780
6 · The paper itself

Abstract

SCoV2-MD (www.scov2-md.org) is a new online resource that systematically organizes atomistic simulations of the SARS-CoV-2 proteome. The database includes simulations produced by leading groups using molecular dynamics (MD) methods to investigate the structure-dynamics-function relationships of viral proteins. SCoV2-MD cross-references the molecular data with the pandemic evolution by tracking all available variants sequenced during the pandemic and deposited in the GISAID resource. SCoV2-MD enables the interactive analysis of the deposited trajectories through a web interface, which enables users to search by viral protein, isolate, phylogenetic attributes, or specific point mutation. Each mutation can then be analyzed interactively combining static (e.g. a variety of amino acid substitution penalties) and dynamic (time-dependent data derived from the dynamics of the local geometry) scores. Dynamic scores can be computed on the basis of nine non-covalent interaction types, including steric properties, solvent accessibility, hydrogen bonding, and other types of chemical interactions. Where available, experimental data such as antibody escape and change in binding affinities from deep mutational scanning experiments are also made available. All metrics can be combined to build predefined or custom scores to interrogate the impact of evolving variants on protein structure and function.

Indexed as

Databases, GeneticMolecular Dynamics SimulationSoftwareCOVID-19Evolution, MolecularGene Expression Regulation, ViralGenome, ViralHumansHydrogen BondingInternetModels, MolecularPhylogenyPoint MutationProtein BindingProtein Interaction MappingSARS-CoV-2Viral Proteins

Identifiers

PMID34761257
PMCPMC8689960
OpenAlexW3213803608

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.