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Record W4412704844 · doi:10.2196/73637

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

2025· article· en· W4412704844 on OpenAlexvenueno aff
Edem K. Netsey, Samuel M. Naandam, Joseph Asante, Abraham E. Kuukua, Aayire C. Yadem, Gabriel Owusu, Jeffrey G. Shaffer, Sudesh Srivastav, Ellis Owusu‐Dabo, Chris E. Morkle, Desmond Yemeh, Stephen Manortey, Ernest Yankson, Mamadou Sangaré, Samuel Kakraba

Bibliographic record

VenueJMIR Bioinformatics and Biotechnology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsnot available
FundersFogarty International Center
KeywordsPreprintSpike (software development)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Spike ProteinAnalyticsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakPredictive analyticsComputer scienceComputational biologyData scienceBiologyVirologyMedicineWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.248
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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