In Silico Identification of Vaccine Targets for 2019-nCoV

28 peptides identical to SARS-CoV and 48 peptides similar to known immunogenic peptides were identified from the 2019-nCoV proteome. A de novo search found 63 additional 9-mer peptides with high immunogenicity potential. This dataset, produced by Chloe H. Lee at the University of Oxford, provides a shortlist of candidates for experimental vaccine validation.

Use Cases

• Prioritizing experimental vaccine candidates based on predicted immunogenic peptides.
• Analyzing peptide similarity to known SARS-CoV epitopes for cross-reactive immunity studies.
• Predicting HLA binding affinity for common alleles in Chinese and European populations.
• Validating immunogenicity prediction models like iPred and NetMHCpan 4.0.

Strengths

• Identifies 28 peptides with direct experimental evidence from SARS-CoV T cell assays.
• Lists 48 peptides with high similarity to immunogenic entries in the Immune Epitope Database.
• Predicts binding for 63 de novo peptides to common HLA alleles using established algorithms.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Oxford

Collection Method

Computational alignment of the 2019-nCoV proteome to viral immunogenic peptide databases, with predictions from NetMHCpan 4.0 and iPred.