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Record W2474716288 · doi:10.4049/jimmunol.1500654

ERAAP Shapes the Peptidome Associated with Classical and Nonclassical MHC Class I Molecules

2016· article· en· W2474716288 on OpenAlexaff
Niranjana Nagarajan, Danielle A. de Verteuil, Dev Sriranganadane, Wafaa Yahyaoui, Pierre Thibault, Claude Perreault, Nilabh Shastri

Bibliographic record

VenueThe Journal of Immunology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersNational Institute of Allergy and Infectious Diseases
KeywordsClass (philosophy)MHC class IComputational biologyMajor histocompatibility complexBiologyGeneticsComputer scienceArtificial intelligenceGene

Abstract

fetched live from OpenAlex

The peptide repertoire presented by classical as well as nonclassical MHC class I (MHC I) molecules is altered in the absence of the endoplasmic reticulum aminopeptidase associated with Ag processing (ERAAP). To characterize the extent of these changes, peptides from cells lacking ERAAP were eluted from the cell surface and analyzed by high-throughput mass spectrometry. We found that most peptides found in wild-type (WT) cells were retained in the absence of ERAAP. In contrast, a subset of "ERAAP-edited" peptides was lost in WT cells, and ERAAP-deficient cells presented a unique "unedited" repertoire. A substantial fraction of MHC-associated peptides from ERAAP-deficient cells contained N-terminal extensions and had a different molecular composition than did those from WT cells. We found that the number and immunogenicity of peptides associated with nonclassical MHC I was increased in the absence of ERAAP. Conversely, only peptides presented by classical MHC I were immunogenic in ERAAP-sufficient cells. Finally, MHC I peptides were also derived from different intracellular sources in ERAAP-deficient cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.214
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2016
Admission routes1
Has abstractyes

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