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Record W2095519917 · doi:10.3389/fimmu.2013.00133

Advances in T-Cell Epitope Engineering

2013· article· en· W2095519917 on OpenAlexaff
Johanne M. Pentier, Andrew K. Sewell, John J. Miles

Bibliographic record

VenueFrontiers in Immunology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicvaccines and immunoinformatics approaches
Canadian institutionsInstitute of Infection and Immunity
FundersBiotechnology and Biological Sciences Research Council
KeywordsEpitopeT cellComputational biologyCell biologyBiologyImmunologyAntigenImmune system

Abstract

fetched live from OpenAlex

T-cells recognize small peptide fragments (p) cradled in multiple major histocompatibility complex (MHC) molecules, termed human leukocyte antigens (HLA) in humans. These membrane-integral pMHC molecules are present on surface of all nucleated cells and allow T-cells to detect aberrant intracellular activity, be this infection with microorganisms or abnormal host biochemistry such as neoplastic division. Scanning of pMHC molecules occurs via the αβ T-cell receptor (TCR), a clonotypic, heterodimeric, and membrane-integral molecule on the T-cell surface (Miles et al., 2011a). TCRs engage pMHC molecules via six highly flexible complementarity determining region (CDR) loops and, upon productive docking with a pMHC molecule, the TCR triggers a myriad of intracellular T-cell signaling cascades (Bridgeman et al., 2012). The binding strength (or affinity) between a TCR and a cognate pMHC is relatively weak across known biological systems with monomeric “dwell times” (or half-lives) typically measured in seconds or microseconds at physiological temperatures (Miles et al., 2010; Bridgeman et al., 2012; Smith et al., 2012). This is in contrast to numerous other biological interactions such as antibodies (van der Merwe and Davis, 2003), interleukins (Morton et al., 1994), lipoproteins (Misra et al., 2001), and structural membrane proteins (Matte et al., 2012) which have half-lives measured in hours-to-days. Overall, TCR/pMHC interactions are fleeting even by the dynamic standards of cell surface interactions (van der Merwe and Davis, 2003). The evolutionary rationale for this striking functional divide can only be speculated upon but likely pertains to the primary function of T-cells. T-cells must scan large numbers of pMHC on multiple cells in series in order to identify and eliminate threats quickly. Effective immunity requires that TCR scanning time must be minimal and antigen coverage maximal. Theoretical arguments dictate that maximal immune cover of possible foreign pMHC requires each TCR to recognize huge numbers of different peptides (Mason, 1998; Sewell, 2012). This theory is now supported up by direct experimental evidence that shows a single TCR can cross-recognize millions of pMHC molecules as well or better than the native antigen (Sewell, 2012; Wooldridge et al., 2012; Ekeruche-Makinde et al., 2013). Curiously, this extensive T-cell cross-reactivity is strictly compartmentalized based on peptide length (Ekeruche-Makinde et al., 2013).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.003
GPT teacher head0.179
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations12
Published2013
Admission routes1
Has abstractyes

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