PT  - JOURNAL ARTICLE
AU  - Tariq Daouda
AU  - Maude Dumont-Lagacé
AU  - Albert Feghaly
AU  - Yahya Benslimane
AU  - Rébecca Panes
AU  - Mathieu Courcelles
AU  - Mohamed Benhammadi
AU  - Lea Harrington
AU  - Pierre Thibault
AU  - François Major
AU  - Yoshua Bengio
AU  - Étienne Gagnon
AU  - Sébastien Lemieux
AU  - Claude Perreault
TI  - Codon arrangement modulates MHC-I peptides presentation
AID  - 10.1101/2020.06.03.078824
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.03.078824
4099  - http://biorxiv.org/content/early/2020/06/04/2020.06.03.078824.short
4100  - http://biorxiv.org/content/early/2020/06/04/2020.06.03.078824.full
AB  - MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP coding regions, while excluding the MAP-coding codons per se. CAMAP predictions were significantly more accurate when using codon sequences than amino acid sequences. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules, and applied to several cell types and species. Combining MAP binding affinity, transcript expression level and CAMAP scores was particularly useful to ameliorate predictions of MAP derived from lowly expressed transcripts. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking MAP sequences (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome.Competing Interest StatementThe authors have declared no competing interest.