Evidence map›Paper›PMID 35339763›Full record

ArticleComputational biology and chemistry2022

Glycosylation is key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2.

Georcki Ropón-Palacios, Jhon Pérez-Silva, Ricardo Rojas-Humpire, Gustavo E Olivos-Ramírez, Manuel Chenet-Zuta, Victor Cornejo-Villanueva, Sheyla Carmen-Sifuentes, Kewin Otazu, Yaritza L Ramirez-Díaz, Karolyn Vega Chozo and 1 more

Open access · greenAbstract read
In one paragraph

Article in Computational biology and chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

11 authors at 3 institutions in 3 countries.

Georcki Ropón-PalaciosLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil. Electronic address: groponp@gmail.com.
Jhon Pérez-SilvaLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Ricardo Rojas-HumpireLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Gustavo E Olivos-RamírezLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Manuel Chenet-ZutaEscuela de Posgrado, Universidad San Ignacio de Loyola, Peru.
Victor Cornejo-VillanuevaEscuela de Genética y Biotecnología, Universidad Nacional Mayor de San Marcos, Peru.
Sheyla Carmen-SifuentesLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Kewin OtazuLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Yaritza L Ramirez-DíazLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Karolyn Vega ChozoLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil.
Ihosvany CampsLaboratório de Modelagem Computacional, Instituto de Ciências Exatas, Universidade Federal de Alfenas, Brazil; High Performance & Quantum Computing Labs, Waterloo, Canada. Electronic address: icamps@unifal-mg.edu.br.
Universidade Federal de Alfenas · BRNational University of San Marcos · PEUniversidad San Ignacio de Loyola · PE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of COVID-19 caused by SARS-CoV-2 and its spread since 2019 represents the major public health problem worldwide nowadays, which has generated a high number of infections and deaths. The spike protein (S protein) is the most studied protein of SARS-CoV-2, and key to host-cell entry through ACE2 receptor. This protein presents a large pattern of glycosylations with important roles in immunity and infection mechanisms. Therefore, understanding key aspects of the molecular mechanisms of these structures, during drug recognition in SARS-CoV-2, may contribute to therapeutic alternatives. In this work, we explored the impact of glycosylations on the drug recognition on two domains of the S protein, the receptor-binding domain (RBD) and the N-terminal domain (NTD) through molecular dynamics simulations and computational biophysics analysis. Our results show that glycosylations in the S protein induce structural stability and changes in rigidity/flexibility related to the number of glycosylations in the structure. These structural changes are important for its biological activity as well as the correct interaction of ligands in the RBD and NTD regions. Additionally, we evidenced a roto-translation phenomenon in the interaction of the ligand with RBD in the absence of glycosylation, which disappears due to the influence of glycosylation and the convergence of metastable states in RBM. Similarly, glycosylations in NTD promote an induced fit phenomenon, which is not observed in the absence of glycosylations; this process is decisive for the activity of the ligand at the cryptic site. Altogether, these results provide an explanation of glycosylation relevance in biophysical properties and drug recognition to S protein of SARS-CoV-2, which must be considered in the rational drug development and virtual screening targeting S protein.

Indexed as

COVID-19SARS-CoV-2Angiotensin-Converting Enzyme 2GlycoproteinsGlycosylationHumansLigandsMolecular Dynamics SimulationProtein BindingSpike Glycoprotein, CoronavirusAngiotensin-Converting Enzyme 2GlycoproteinsLigandsSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2COVID-19Cryptic pocketFree Energy LandscapeInduced-Fit bindingMolecular dynamicsN- and O-glycosylationRoto-translation phenomenon

Identifiers

PMID35339763
PMCPMC8941845
OpenAlexW4220928358

What OpenQuestion holds

Textmetadata
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.