Evidence map›Paper›PMID 35899933›Full record

ArticleSoft matter2022

Nanomechanical analysis of SARS-CoV-2 variants and predictions of infectiousness and lethality.

Yiwen Hu, Markus J Buehler

Open access · hybridAbstract read
In one paragraph

Article in Soft matter, 2022. 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
0.5field-weighted citation impact, top 36% 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, 6 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

2 authors at 1 institution in 1 country.

Yiwen HuLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA. mbuehler@mit.edu.
Markus J BuehlerLaboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA. mbuehler@mit.edu.ORCID http://orcid.org/0000-0002-4173-9659
Massachusetts Institute of Technology · US

Funding

Models to Predict Protein Biomaterial PerformanceU01EB014976 · NIBIB · TUFTS UNIVERSITY MEDFORD · PI BUEHLER, MARKUS J., KAPLAN, DAVID L. · 2012 to 2020
$5.2M
NIBIB NIH HHS U01 EB014976
6 · The paper itself

Abstract

As variants of the pathogen that causes COVID-19 spread around the world, estimates of infectiousness and lethality of newly emerging strains are important. Here we report a predictive model that associates molecular motions and vibrational patterns of the virus spike protein with infectiousness and lethality. The key finding is that most SARS-CoV-2 variants are predicted to be more infectious and less lethal compared to the original spike protein. However, lineage B.1.351 (Beta variant) is predicted to be less infectious and more lethal, and lineage B.1.1.7 (Alpha variant) is predicted to have both higher infectivity and lethality, showing the potential of the virus to mutate towards different performance regimes. The relatively more recent lineage B.1.617.2 (Delta variant), although contains a few key spike mutations other than D614G, behaves quite similar to the single D614G mutation in both vibrational and predicted epidemiological aspects, which might explain its rapid circulation given the prevalence of D614G. This work may provide a tool to estimate the epidemiological effects of new variants, and offer a pathway to screen mutations against high threat levels. Moreover, the nanomechanical approach, as a novel tool to predict virus-cell interactions, may further open up the door towards better understanding other viruses.

Indexed as

COVID-19SARS-CoV-2HumansMutationSpike Glycoprotein, CoronavirusSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2

Identifiers

PMID35899933
PMCPMC9364333
OpenAlexW4286375390

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.