Evidence map›Paper›PMID 41359941›Full record

ArticleJMIR bioinformatics and biotechnology2025

Structural and Functional Impacts of SARS-CoV-2 Spike Protein Mutations: Insights From Predictive Modeling and Analytics.

Edem K Netsey, Samuel M Naandam, Joseph Asante Jnr, Kuukua E Abraham, Aayire C Yadem, Gabriel Owusu, Jeffrey G Shaffer, Sudesh K Srivastav, Seydou Doumbia, Ellis Owusu-Dabo and 6 more

Abstract read
In one paragraph

Article in JMIR bioinformatics and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

16 authors.

Edem K Netsey *Department of Mathematics and Information Communication Technology, School of Physical Sciences, Dambai College of Education, Dambai, Ghana.ORCID http://orcid.org/0009-0000-6723-9460
Samuel M NaandamDepartment of Mathematics, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana.ORCID http://orcid.org/0009-0003-7229-8184
Joseph Asante JnrDepartment of Geriatrics, School of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States.ORCID http://orcid.org/0009-0000-4618-5133
Kuukua E AbrahamDepartment of Mathematics, Memphis Shelby County Schools, Memphis, TN, United States.ORCID http://orcid.org/0009-0002-9269-2536
Aayire C YademResearch and Development, CytoAstra LLC, Little Rock, AR, United States.ORCID http://orcid.org/0000-0002-4923-7792
Gabriel OwusuOffice of Research, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, New Orleans, LA, United States.ORCID http://orcid.org/0000-0003-0330-1979
Jeffrey G ShafferDepartment of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, 1440 Canal Street, New Orleans, LA, 70112, United States, 1 5049882475.ORCID http://orcid.org/0000-0002-3941-3772
Sudesh K SrivastavDepartment of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, 1440 Canal Street, New Orleans, LA, 70112, United States, 1 5049882475.ORCID http://orcid.org/0000-0002-1225-5874
Seydou DoumbiaDepartment of Public Health, Faculty of Medicine, Malaria Research and Training Center, University of Sciences, Techniques and Technologies of Bamako, Bamako, Mali.ORCID http://orcid.org/0000-0003-1637-5600
Ellis Owusu-DaboSchool of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.ORCID http://orcid.org/0000-0003-4232-4292
Chris E MorkleDepartment of Mathematics, Suhum Senior High School, Suhum, Ghana.ORCID http://orcid.org/0009-0006-1110-0519
Desmond YemehInterdisciplinary Aging Studies, Tulane Center for Aging, School of Medicine, Tulane University, New Orleans, LA, United States.ORCID http://orcid.org/0009-0009-7810-4495
Stephen ManorteyDepartment of Community Health, Ensign Global University, Kpong, Ghana.ORCID http://orcid.org/0000-0001-5783-6595
Ernest YanksonDepartment of Mathematics, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana.ORCID http://orcid.org/0000-0002-5621-1048
Mamadou SangareDepartment of National Institute of Allergy and Infection Diseases (NIAID), 14 Laboratory of Malaria and Vector Research (LMVR), National Institute of Allergy and Infectious Diseases (NIAID), Rockville, MD, United States.ORCID http://orcid.org/0000-0002-8300-8205
Samuel Kakraba *Department of Biostatistics and Data Science, Celia Scott Weatherhead School of Public Health and Tropical Medicine at Tulane University, 1440 Canal Street, New Orleans, LA, 70112, United States, 1 5049882475.ORCID http://orcid.org/0000-0002-6362-5126

Funding

West African Center of Excellence for Global Health Bioinformatics Research TrainingU2RTW010673 · FIC · UNIV OF SCIENCES, TECH & TECH OF BAMAKO · PI DOUMBIA, SEYDOU, LI, JIAN · 2017 to 2021
$1.2M
West Africa Center of Excellence for Data Science Research EducationUE5TW012526 · FIC · UNIV OF SCIENCES, TECH & TECH OF BAMAKO · PI DOUMBIA, SEYDOU, SHAFFER, JEFFREY G · 2023 to 2025
$600k
FIC NIH HHS U2R TW010673FIC NIH HHS UE5 TW012526
6 · The paper itself

Abstract

Background: The COVID-19 pandemic requires a deep understanding of SARS-CoV-2, particularly how mutations in the spike receptor-binding domain (RBD) chain E affect its structure and function. Current methods lack comprehensive analysis of these mutations at different structural levels. Objective: This study aims to analyze the impact of specific COVID-19-associated point mutations (N501Y, L452R, N440K, K417N, and E484A) on the SARS-CoV-2 spike RBD structure and function using predictive modeling, including a graph-theoretic model, protein modeling techniques, and molecular dynamics simulations. Methods: The study used a multitiered graph-theoretic framework to represent protein structure across 3 interconnected levels. This model incorporated 19 top-level vertices, connected to intermediate graphs based on 6-angstrom proximity within the protein's 3D structure. Graph-theoretic molecular descriptors or invariants were applied to weigh vertices and edges at all levels. The study also used Iterative Threading Assembly Refinement (I-TASSER) to model mutated sequences and molecular dynamics simulation tools to evaluate changes in protein folding and stability compared to the wildtype. Results: A total of 3 distinct predictive modeling and analytical approaches successfully identified structural and functional changes in the SARS-CoV-2 spike RBD (chain E) resulting from point mutations. The novel graph-theoretic model detected notable structural changes, with N501Y and L452R showing the most pronounced effects on conformation and stability compared to the wildtype. K147N and E484A mutations demonstrated less significant impacts compared to the severe mutations, N501Y and L452R. Ab initio modeling and molecular simulation dynamics findings corroborated the results from graph-theoretic analysis. The multilevel analytical approach provided a comprehensive visualization of mutation effects, deepening our understanding of their functional consequences. Conclusions: This study advanced our understanding of SARS-CoV-2 spike RBD mutations and their implications. The multifaceted approach characterized the effects of various mutations, identifying N501Y and L452R as having the most substantial impact on RBD conformation and stability. The findings have important implications for vaccine development, therapeutic design, and variant monitoring. Our research underscores the power of combining multiple predictive analytical approaches in virology, contributing valuable knowledge to ongoing efforts against the COVID-19 pandemic and providing a framework for future studies on viral mutations and their impacts on protein structure and function.

Indexed as

6M0JCOVID-19E484Agraph-theoretic modelingmolecular dynamics simulationspredictive modelingSARS-CoV-2 spike mutations (N501Y, E484A, L452R, N440K, K417N)unsupervised machine learning

Identifiers

PMID41359941
PMCPMC12685290

What OpenQuestion holds

Textmetadata
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.