Evolving fitness and immune escape: a retrospective analysis of SARS-CoV-2 spike protein (2020-2024) using protein language model Article Swipe
Sihua Peng
,
Leke Lyu
,
Ludy Registre Carmola
,
Sachin Subedi
,
M. H. M. Mubassir
,
Mohamed A Bakheet
,
Justin Bahl
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1576414
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1576414
Introduction The COVID-19 pandemic posed global health challenges. Understanding SARS-CoV-2’s evolutionary dynamics, especially fitness and immune escape, is vital for public health. This study uses protein language models to assess how genetic variations affect viral adaptability and immunity. Methods We applied the CoVFit model to predict Fitness and Immune Escape Index (IEI), validated by a null model based on neutral evolution. We analyzed 2,504,278 SARS-CoV-2 spike sequences, including 160,892 variants, tracking evolution from 2020 to May 2024, comparing real and random mutants’ Fitness and IEI. Results Our analysis revealed an increase in Fitness (mean rising from 0.227 in 2020 to 0.930 in 2024) and IEI (mean increasing from 0.171 to 0.555) for North American samples. Globally, the comparison of Fitness and IEI between real and random mutants (generated by the null model) revealed statistically significant differences (real mutant Fitness 0.3849 vs. random mutant 0.2046, p < 0.001, KS test; real mutant IEI 0.2894 vs. random mutant 0.1895, p < 0.001, KS test), indicating strong selective pressure; the JN.1 lineage dominated (94% of sequences by April 2024), underscoring its evolutionary advantage. Conclusions CoVFit offers key insights into SARS-CoV-2 evolution, aiding vaccine design. Persistent viral adaptation despite interventions highlights the need for surveillance and adaptive strategies using tools like CoVFit for preparedness.
Related Topics
Concepts
Spike Protein
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Coronavirus disease 2019 (COVID-19)
Spike (software development)
2019-20 coronavirus outbreak
Immune system
Coronavirus
Immune escape
Computational biology
Virology
Biology
Medicine
Computer science
Immunology
Infectious disease (medical specialty)
Disease
Pathology
Software engineering
Outbreak
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2025.1576414
- https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1576414/pdf
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411397155
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411397155Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fimmu.2025.1576414Digital Object Identifier
- Title
-
Evolving fitness and immune escape: a retrospective analysis of SARS-CoV-2 spike protein (2020-2024) using protein language modelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-18Full publication date if available
- Authors
-
Sihua Peng, Leke Lyu, Ludy Registre Carmola, Sachin Subedi, M. H. M. Mubassir, Mohamed A Bakheet, Justin BahlList of authors in order
- Landing page
-
https://doi.org/10.3389/fimmu.2025.1576414Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1576414/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1576414/pdfDirect OA link when available
- Concepts
-
Spike Protein, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19), Spike (software development), 2019-20 coronavirus outbreak, Immune system, Coronavirus, Immune escape, Computational biology, Virology, Biology, Medicine, Computer science, Immunology, Infectious disease (medical specialty), Disease, Pathology, Software engineering, OutbreakTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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53Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mutants’ | 81 |
| abstract_inverted_index.sequences, | 66 |
| abstract_inverted_index.strategies | 203 |
| abstract_inverted_index.variations | 32 |
| abstract_inverted_index.Conclusions | 180 |
| abstract_inverted_index.challenges. | 7 |
| abstract_inverted_index.differences | 135 |
| abstract_inverted_index.significant | 134 |
| abstract_inverted_index.Introduction | 0 |
| abstract_inverted_index.adaptability | 35 |
| abstract_inverted_index.evolutionary | 10, 178 |
| abstract_inverted_index.surveillance | 200 |
| abstract_inverted_index.underscoring | 176 |
| abstract_inverted_index.Understanding | 8 |
| abstract_inverted_index.interventions | 195 |
| abstract_inverted_index.preparedness. | 209 |
| abstract_inverted_index.statistically | 133 |
| abstract_inverted_index.SARS-CoV-2’s | 9 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.28705976 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |