Evidence map›Paper›PMID 41301456›Full record

ArticleBiomolecules2025

Modeling the Mutational Effects on Biochemical Phenotypes of SARS-CoV-2 Using Molecular Fields.

Baifan Wang, Zhen Xi

Abstract read
In one paragraph

Article in Biomolecules, 2025. 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
–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

0 citing papers in PubMed.

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

2 authors.

Baifan WangState Key Laboratory of Elemento-Organic Chemistry, Department of Chemical Biology, National Pesticide Engineering Research Center (Tianjin), Nankai University, Tianjin 300071, China.ORCID 0000-0003-0887-5795
Zhen XiState Key Laboratory of Elemento-Organic Chemistry, Department of Chemical Biology, National Pesticide Engineering Research Center (Tianjin), Nankai University, Tianjin 300071, China.

Funding

the Ministry of Science and Technology of the People's Republic of China 2023YFD1700500
6 · The paper itself

Abstract

The ongoing evolution of SARS-CoV-2 has given rise to variants with enhanced transmissibility and pathogenicity, many of which harbor mutations in the receptor-binding domain (RBD) of the viral spike protein. These mutations often confer increased viral fitness and immune evasion by modulating interactions with the human ACE2 receptor (hACE2) and escaping neutralizing antibodies. Accurate prediction of the functional consequences of such mutations-particularly their effects on receptor binding and antibody escape-is critical for assessing the public health threat posed by emerging variants. In this study, we apply a Mutation-dependent Biomacromolecular Quantitative Structure-Activity Relationship (MB-QSAR) framework to quantitatively model the biochemical phenotypes of RBD variants. Trained on comprehensive deep mutational scanning (DMS) datasets, our models exhibit strong predictive performance, achieving correlation coefficients (r

Indexed as

COVID-19MutationSARS-CoV-2Spike Glycoprotein, CoronavirusAngiotensin-Converting Enzyme 2Antibodies, NeutralizingAntibodies, ViralHumansModels, MolecularPhenotypeProtein BindingQuantitative Structure-Activity RelationshipACE2 protein, humanAngiotensin-Converting Enzyme 2Antibodies, NeutralizingAntibodies, ViralSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2antibody escapehACE2 bindingMB-QSARmutational effectspredictive modelingprotein–protein interactionSARS-CoV-2

Identifiers

PMID41301456
PMCPMC12649838

What OpenQuestion holds

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LicenceCC BY
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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.