Dataset Information

Footprints of antigen processing boost MHC class II natural ligand predictions.

ABSTRACT:

Background

Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.

Methods

We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets.

Results

We show that integrated prediction models incorporate identifiable rules of antigen processing. In fact, we observed detectable signals of protease cleavage at defined positions of the ligands. We also hypothesize a role of the length of the terminal ligand protrusions for trimming the peptide to the MHC presented ligand.

Conclusions

The results of integrating binding affinity and eluted ligand data in a combined model demonstrate improved performance for the prediction of MHC-II ligands and T cell epitopes and foreshadow a new generation of improved peptide to MHC-II prediction tools accounting for the plurality of factors that determine natural presentation of antigens.

SUBMITTER: Barra C

PROVIDER: S-EPMC6240193 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Footprints of antigen processing boost MHC class II natural ligand predictions.

Barra Carolina C Alvarez Bruno B Paul Sinu S Sette Alessandro A Peters Bjoern B Andreatta Massimo M Buus Søren S Nielsen Morten M

Genome medicine 20181116 1

<h4>Background</h4>Major histocompatibility complex class II (MHC-II) molecules present peptide fragments to T cells for immune recognition. Current predictors for peptide to MHC-II binding are trained on binding affinity data, generated in vitro and therefore lacking information about antigen processing.<h4>Methods</h4>We generate prediction models of peptide to MHC-II binding trained with naturally eluted ligands derived from mass spectrometry in addition to peptide binding affinity data sets. ...[more]

PMID: 30446001

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Project description:The well described conventional antigen-processing pathway is accountable for most peptides that end up in MHC class I molecules at the cell surface. These peptides experienced liberation by the proteasome and transport by the peptide transporter TAP. However, there are multiple roads that lead to Rome, illustrated by the increasing number of alternative processing pathways that have been reported during last years. Interestingly, TAP-deficient individuals do not succumb to viral infections, suggesting that CD8 T cell immunity is sufficiently supported by alternative TAP-independent processing pathways. To date, a diversity of viral and endogenous TAP-independent peptides have been identified in the grooves of different MHC class I alleles. Some of these peptides are not displayed by normal TAP-positive cells and we therefore called them TEIPP, for "T-cell epitopes associated with impaired peptide processing." TEIPPs are hidden self-antigens, are derived from normal housekeeping proteins, and are processed via unconventional processing pathways. Per definition, TEIPPs are presented via TAP-independent pathways, but recent data suggest that part of this repertoire still depend on proteasome and metalloprotease activity. An exception is the C-terminal peptide of the endoplasmic reticulum (ER)-membrane-spanning ceramide synthase Trh4 that is surprisingly liberated by the signal peptide peptidase (SPP), the proteolytic enzyme involved in cleaving leader sequences. The intramembrane cleaving SPP is thereby an important contributor of TAP-independent peptides. Its family members, like the Alzheimer's related presenilins, might contribute as well, according to our preliminary data. Finally, alternative peptide routing is an emerging field and includes processes like the unfolded protein response, the ER-associated degradation, and autophagy-associated vesicular pathways. These data convince us that there is a world to be discovered in the field of unconventional antigen processing.

Dataset Information

Footprints of antigen processing boost MHC class II natural ligand predictions.

Background

Methods

Results

Conclusions

Publications

Footprints of antigen processing boost MHC class II natural ligand predictions.

Similar Datasets

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