COVID-19 Vaccine Candidates: 174 Predicted SARS-CoV-2 Epitopes

A dataset of SARS-CoV-2 peptides predicted to bind to 11 HLA allotypes, used in the paper "COVID-19 vaccine candidates: Prediction and validation of 174 novel SARS-CoV-2 epitopes". It includes predictions from multiple computational tools and results from a stability assay, revealing potential vaccine targets. The data was contributed by Marek Prachař from the University of Copenhagen.

Use Cases

• Training machine learning models for epitope prediction based on peptide sequences and HLA binding affinity.
• Validating computational immunology tools by comparing predictions from multiple tested tools.
• Identifying promising vaccine targets by analyzing peptides measured in a stability assay.

Strengths

• Contains 174 novel SARS-CoV-2 epitopes predicted for vaccine development.
• Includes predictions for binding to 11 distinct HLA allotypes.
• Integrates computational predictions with experimental stability assay results.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description notes that not all prediction tools use the same measure, complicating direct comparison.

Provenance

Source

University of Copenhagen

Collection Method

Computational prediction and experimental validation as described in the associated research paper.