Evidence map›Paper›PMID 38495173›Full record

ArticleHeliyon2024

Unraveling the binding mechanisms of SARS-CoV-2 variants through molecular simulations.

Shin-Pon Ju, Yung-Cheng Yang, Hsing-Yin Chen

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.3field-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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

3 authors at 2 institutions in 1 country.

Shin-Pon JuDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
Yung-Cheng YangDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
Hsing-Yin ChenDepartment of Medicinal and Applied Chemistry, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
National Sun Yat-sen University · TWKaohsiung Medical University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of SARS-CoV-2 variants like Delta (AY.29) and Omicron (EG.5) poses continued challenges for vaccines and therapeutics. Mutations in the viral spike protein are key in altering infectivity and immune evasion. This study uses computational modeling to investigate the molecular binding mechanisms between spike protein variants and the ACE2 host receptor. Using the MARTNI force field, coarse-grained molecular dynamics (CGMD) simulations and nudged elastic band (NEB) calculations explore spike-ACE2 interactions for the wild type, Delta variant, and Omicron variant. The simulations reveal Omicron has the strongest binding affinity at -128.35 ± 10.91 kcal/mol, followed by Delta and wild type. Key mutations in Delta and Omicron, like Q493R and Q498R, optimize electrostatic contacts, enhancing ACE2 interactions. The wild-type spike has the highest transition state energy barrier at 17.87 kcal/mol, while Delta has the lowest barrier at 9.21 kcal/mol. Despite slightly higher dual barriers, Omicron's increased binding energy lowers its overall barrier to rapidly bind ACE2. These findings provide residue-level insights into mutation effects on SARS-CoV-2 infectivity. The computational modeling elucidates mechanisms underlying spike-ACE2 binding kinetics, aiding the development of vaccines and therapies targeting emerging viral strains.

Indexed as

ACE2 receptorBinding affinityCoare-grained molecular dynamicsSARS-CoV-2Spike proteinVariants

Identifiers

PMID38495173
PMCPMC10940921
OpenAlexW4392301812

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.