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Record W2117660120 · doi:10.1093/intimm/dxh338

Identification of core functional region of murine IL-4 using peptide phage display and molecular modeling

2005· article· en· W2117660120 on OpenAlexaff
Gang Yao, Weiyan Chen, Qunfeng Jiang, Zongxiang Xia, Lei Zang, Jianping Zuo, Xin Wei, Zhengjun Chen, Xu Shen, Dong Chen, Bing Sun

Bibliographic record

VenueInternational Immunology · 2005
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitute for Biological Sciences
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhage displayMutagenesisBinding sitePeptideCytokineChemistryMolecular biologyBiologyComputational biologyCell biologyBiochemistryGeneGeneticsMutation

Abstract

fetched live from OpenAlex

Murine IL-4 is a pleiotropic cytokine with undefined core functional region for eliciting downstream signaling. We used molecular modeling to predict the binding sites recognized by an anti-IL-4-neutralizing mAb (11B.11) and peptide phage display to delineate their makeup. The results of these approaches were confirmed by site-directed mutagenesis analysis. The results suggest that the amino acid residues spanning from 79 to 86 (QRLFRAFR) on IL-4 are of the major binding site for 11B.11. Furthermore, the functional experiments demonstrate that the residues R80, R83 and R86, which are located in the helix C of murine IL-4, play a crucial role in binding to the IL-4R alpha-chain. Taken together, a new core functional region of murine IL-4 is identified, which provides new insight into the interaction between IL-4 and IL-4Ralpha. In addition, the results demonstrate that 11B.11 binds to a core functional region of murine IL-4, which prevents this cytokine from interacting with its cognate receptor.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.340
Teacher spread0.287 · 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 designSimulation or modeling
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

Citations10
Published2005
Admission routes1
Has abstractyes

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