Properties of MHC Class I Presented Peptides That Enhance Immunogenicity
By J. Calis, Matt Maybeno, J. Greenbaum, D. Weiskopf, Aruna D. De Silva, Alessandro Sette, Can Kesmir, Bjoern Peters. PLoS Comput. Biol., 2013
836 citations recorded by the source corpus.
T-cells have to recognize peptides presented on MHC molecules to be activated and elicit their effector functions. Several studies demonstrate that some peptides are more immunogenic than others and therefore more likely to be T-cell epitopes. We set out to determine which properties cause such differences in immunogenicity. To this end, we collected and analyzed a large set of data describing the immunogenicity of peptides presented on various MHC-I molecules. Two main conclusions could be drawn from this analysis: First, in line with previous observations, we showed that positions P4–6 of a presented peptide are more important for immunogenicity. Second, some amino acids, especially those with large and aromatic side chains, are associated with immunogenicity. This information was combined into a simple model that was used to demonstrate that immunogenicity is, to a certain extent, predictable. This model (made available at http://tools.iedb.org/immunogenicity/) was validated with data from two independent epitope discovery studies. Interestingly, with this model we could show that T-cells are equipped to better recognize viral than human (self) peptides. After the past successful elucidation of different steps in the MHC-I presentation pathway, the identification of variables that influence immunogenicity will be an important next step in the investigation of T-cell epitopes and our understanding of cellular immune responses.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5be7fa082a4b38b8ecbc7b937f5cfd7caaceb5d7
How researchers see this paper
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In Immunology
- Bjoern Peters
- Sinu Paul
- Massimo Andreatta
- Morten Nielsen
- Alessandro Sette
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- Paolo Marcatili
In Molecular Biology
- Bjoern Peters
- Morten Nielsen
- Massimo Andreatta
- Alessandro Sette
- Sinu Paul
- Paolo Marcatili
- Swapnil Mahajan
- Sandeep Kumar Dhanda
Citations in the corpus
- The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “We used the enrichment and depletions score to generate propensity scales than can be used to evaluate a peptide directly (39).”
- IEDB-AR: immune epitope database—analysis resource in 2019 (2019, background): “org/immunogenicity) is intended to classify peptides that bind to MHC class I (pMHC) into two categories: epitopes and non-epitopes (8).”