Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Peptide Sets
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Abstract
Subunit vaccines induce immunity to a pathogen by presenting a component of the pathogen and thus inherently limit the representation of pathogen peptides for cellular immunity based memory. We find that SARS-CoV-2 subunit peptides may not be robustly displayed by the Major Histocompatibility Complex (MHC) molecules in certain individuals. We introduce an augmentation strategy for subunit vaccines that adds a small number of SARS-CoV-2 peptides to a vaccine to improve the population coverage of pathogen peptide display. Our population coverage estimates integrate clinical data on peptide immunogenicity in convalescent COVID-19 patients and machine learning predictions. We evaluate the population coverage of 9 different subunits of SARS-CoV-2, including 5 functional domains and 4 full proteins, and augment each of them to fill a predicted coverage gap.
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SciScore for 10.1101/2020.08.04.200691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Quantification and Statistical Analysis: Classification performance of peptide-MHC scoring models was calculated using scikit-learn (Pedregosa et al., 2011) in Python using the sklearn.metrics.roc auc score (AUROC), sklearn.metrics.average_precision_score (Average Precision), sklearn.metrics.accuracy_score (Accuracy), sklearn.metrics.precision recall fscore support (Precision, Recall and F1 score), and sklearn.metrics.classification_report (Sensitivity and Specificity) functions. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)Pearson r correlation was computed using scipy (Virtanen … SciScore for 10.1101/2020.08.04.200691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Quantification and Statistical Analysis: Classification performance of peptide-MHC scoring models was calculated using scikit-learn (Pedregosa et al., 2011) in Python using the sklearn.metrics.roc auc score (AUROC), sklearn.metrics.average_precision_score (Average Precision), sklearn.metrics.accuracy_score (Accuracy), sklearn.metrics.precision recall fscore support (Precision, Recall and F1 score), and sklearn.metrics.classification_report (Sensitivity and Specificity) functions. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)Pearson r correlation was computed using scipy (Virtanen et al., 2020) in Python using the scipy.stats.pearsonr function. Pythonsuggested: (IPython, RRID:SCR_001658)scipysuggested: (SciPy, RRID:SCR_008058)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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