Evidence map›Paper›PMID 37920812›Full record

ArticleComputational and structural biotechnology journal2023

Collaborative effects of 2019-nCoV-Spike mutants on viral infectivity.

Senbiao Fang, Chuqi Lei, Meng Li, Yongfan Ming, Liren Liu, Xuming Zhou, Min Li

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2023. 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
0.4field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

7 authors at 2 institutions in 2 countries.

Senbiao FangHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Chuqi LeiHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Meng LiKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
Yongfan MingHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Liren LiuDepartment of Molecular Pharmacology, Tianjin Medical University Cancer Institute & Hospital; National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Xuming ZhouKey Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
Min LiHunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Central South University · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The emerging mutants of the 2019-nCoV coronavirus are posing unprecedented challenges to the pandemic prevention. A thorough, understanding of the mutational characterization responsible for the pathogenic mechanisms of mutations in 2019-nCoV-Spike is indispensable for developing effective drugs and new vaccines. Methods: We employed computational methods and viral infection assays to examine the interaction pattern and binding affinity between ACE2 and both single- and multi-mutants of the Spike proteins. Results: Using data from the CNCB-NGDC databank and analysis of the 2019-nCoV-Spike/ACE2 interface crystal structure, we identified 31 amino acids that may significantly contribute to viral infectivity. Subsequently, we performed molecular dynamics simulations for 589 single-mutants that emerged from the nonsynonymous substitutions of the aforementioned 31 residues. Ultimately, we discovered 8 single-mutants that exhibited significantly higher binding affinities (<-65.00 kcal/mol) to ACE2 compared with the wild-type Spike protein (-55.07 kcal/mol). The random combination of these 8 single-mutants yielded 184 multi-mutants, of which 60 multi-mutants exhibit markedly enhanced binding affinities (<-65.00 kcal/mol). Moreover, the binding free energy analyses of all 773 mutants (including 589 single- and 184 multi-mutants) revealed that Y449R and S494R had a synergistic effect on the binding affinity with other mutants, which were confirmed by virus infection assays of six randomly selected multi-mutants. More importantly, the findings of virus infection assay further validated a strong association between the binding free energy of Spike/ACE2 complex and the viral infectivity. Conclusions: These findings will greatly contribute to the future surveillance of viruses and rational design of therapeutics.

Indexed as

COVID-19Infection assayMolecular dynamics simulationsMulti-mutantSpike

Identifiers

PMID37920812
PMCPMC10618117
OpenAlexW4387709968

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.