Evidence map›Paper›PMID 33892639›Full record

ArticleBMC molecular and cell biology2021

Online biophysical predictions for SARS-CoV-2 proteins.

Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K Anton Feenstra, Wim F Vranken

Abstract read
In one paragraph

Article in BMC molecular and cell biology, 2021. 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
–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

1 citing paper in PubMed.

  1. Identification of Potential Inhibitors of MurD Enzyme ofMolecules (Basel, Switzerland) · 2021
    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

6 authors.

Luciano KagamiInteruniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium.
Joel Roca-MartínezInteruniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium.
Jose Gavaldá-GarcíaInteruniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium.
Pathmanaban RamasamyInteruniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium.
K Anton FeenstraIBIVU - Center for Integrative Bioinformatics, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands.
Wim F VrankenInteruniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium. wim.vranken@vub.be.ORCID http://orcid.org/0000-0001-7470-4324

Funding

Fonds Wetenschappelijk Onderzoek G.0328.16NH2020 European Research Council Marie Sklodowska-Curie 813239
6 · The paper itself

Abstract

backgroundThe SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. MAIN: We present a website ( https://bio2byte.be/sars2/ ) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour.

conclusionThe https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action.

Indexed as

Databases, ProteinHumansInternet AccessSARS-CoV-2Sequence AlignmentSequence Analysis, ProteinSoftwareViral ProteinsViral ProteinsBiophysical featuresCOVID-19ProteinsSARS-CoV-2Single sequence based predictions

Identifiers

PMID33892639
PMCPMC8062939

What OpenQuestion holds

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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.