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Research Article
David C. Marciano
Baylor College of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5237-5144
Olivier Lichtarge
Baylor College of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4057-7122
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Since the first recognized case of COVID-19, more than 30 million people have been infected worldwide. Despite global efforts in drug and vaccine development to fight the disease, there is currently no vaccine or drug cure for COVID-19, though some drugs reduce severity and hasten recovery. Here we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify functional sites that can inform the search for treatments. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that are useful drug targets. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches are based upon evolutionary principles and are agnostic to organism or infective agent.
Keywords
SARS-CoV-2, COVID-19, coronavirus, epitopes, sequence analysis
Figure 1
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This is a list of supplementary files associated with this preprint. Click to download.
- SupportingTable3.SCWZscores.xlsx
Dataset S3. SCW Z-scores.
- SupportingTable1.AlignmentSequenceCountsEachFilteringStep.xlsx
Dataset S1. Alignment Sequence Counts Each Filtering Step.
- SupportingTable4.StructuralEpitopeswith5AProximityCutoff.xlsx
Dataset S4. Structural Sites with 5A Proximity Cutoff.
- SupplementaryMaterials.pdf
Supporting information for: Identification of evolutionarily stable sites across the SARS-CoV-2 proteome
- SupportingTable2.ETRankingsandUniqueVariantCounts.xlsx
Dataset S2. ET Rankings and Unique Variant Counts.
- SupportingTable6.ComparisonoflinearETsitesandstructuralETsites.xlsx
Dataset S6. Comparison of Linear ET Sites and Structural ET Sites.
- SupportingTable7.CrossreactiveTCellEpitopesTrainingSet.xlsx
Dataset S7. Cross-reactive T Cell Epitopes Training Set.
- SupportingTable5.Linearsites.xlsx
Dataset S5. Linear Sites.
- SupportingTable8.CrossreactiveTCellEpitopesPredictionSet.xlsx
Dataset S8. Cross-reactive T cell Epitopes Prediction Set.
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
David C. Marciano
Baylor College of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5237-5144
Olivier Lichtarge
Baylor College of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0003-4057-7122
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
The most recent version of this article is available here.
No community comments so far
Version 2
Posted 18 Dec, 2020
No comments provided
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
The most recent version of this article is available here.
Since the first recognized case of COVID-19, more than 30 million people have been infected worldwide. Despite global efforts in drug and vaccine development to fight the disease, there is currently no vaccine or drug cure for COVID-19, though some drugs reduce severity and hasten recovery. Here we interrogate the evolutionary history of the entire SARS-CoV-2 proteome to identify functional sites that can inform the search for treatments. Combining this information with the mutations observed in the current COVID-19 outbreak, we systematically and comprehensively define evolutionarily stable sites that are useful drug targets. Several experimentally-validated effective drugs interact with these proposed target sites. In addition, the same evolutionary information can prioritize cross reactive antigens that are useful in directing multi-epitope vaccine strategies to illicit broadly neutralizing immune responses to the betacoronavirus family. Although the results are focused on SARS-CoV-2, these approaches are based upon evolutionary principles and are agnostic to organism or infective agent.
Figure 1
Figure 1
Figure 1
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
- SupportingTable3.SCWZscores.xlsx
Dataset S3. SCW Z-scores.
- SupportingTable1.AlignmentSequenceCountsEachFilteringStep.xlsx
Dataset S1. Alignment Sequence Counts Each Filtering Step.
- SupportingTable4.StructuralEpitopeswith5AProximityCutoff.xlsx
Dataset S4. Structural Sites with 5A Proximity Cutoff.
- SupplementaryMaterials.pdf
Supporting information for: Identification of evolutionarily stable sites across the SARS-CoV-2 proteome
- SupportingTable2.ETRankingsandUniqueVariantCounts.xlsx
Dataset S2. ET Rankings and Unique Variant Counts.
- SupportingTable6.ComparisonoflinearETsitesandstructuralETsites.xlsx
Dataset S6. Comparison of Linear ET Sites and Structural ET Sites.
- SupportingTable7.CrossreactiveTCellEpitopesTrainingSet.xlsx
Dataset S7. Cross-reactive T Cell Epitopes Training Set.
- SupportingTable5.Linearsites.xlsx
Dataset S5. Linear Sites.
- SupportingTable8.CrossreactiveTCellEpitopesPredictionSet.xlsx
Dataset S8. Cross-reactive T cell Epitopes Prediction Set.
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