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Record W1970173052 · doi:10.1038/icb.2010.21

COMBODY: one‐domain antibody multimer with improved avidity

2010· article· en· W1970173052 on OpenAlexaff
Xuekai Zhu, Lei Wang, Rongzhi Liu, Barry Flutter, Shenghua Li, Jie Ding, Hua Tao, Changzhen Liu, Meiyi Sun, Bin Gao

Bibliographic record

VenueImmunology and Cell Biology · 2010
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsNational Research Council CanadaInstitute for Biological Sciences
Fundersnot available
KeywordsAviditySingle-domain antibodyAntibodyPeptideChemistryIn vitroT-cell receptorComputational biologyMolecular biologyCell biologyT cellBiologyImmune systemImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Antibodies (Abs) have been engineered into small antigen-binding fragments and rebuilt into multivalent high-avidity molecules for improving in vivo pharmacokinetics and efficacy in clinical use. To increase the avidity of a T-cell receptor-like single-domain Ab (sdAb) specific for HLA-A2 complex, we fused the sdAb to a coiled-coil peptide derived from human cartilage oligomeric matrix protein (COMP48) to make an sdAb multimer, termed combody. The combody improved the binding avidity of sdAb significantly, whereas the specificity for the targeted cells was retained. The strategy was also expanded to create a bispecific combody by fusing an sdAb to the N-terminal and an anti-CD3 single-chain variable fragment to the C-terminal of COMP48. The dual-specific combody was able to efficiently mediate cytotoxicity against the target cells in vitro. Taken together, the strategy to make combody could be widely adopted to increase the avidity of Ab fragment for further application.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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 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

Citations38
Published2010
Admission routes1
Has abstractyes

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