MétaCan
Menu
Back to cohort
Record W2753470422 · doi:10.1002/cbic.201700411

Synthetic Modular Antibody Construction by Using the SpyTag/SpyCatcher Protein‐Ligase System

2017· article· en· W2753470422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemBioChem · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Structural Characterization
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
FundersWestern Economic Diversification CanadaCanadian Cancer Society Research Institute
KeywordsRecombinant DNAAntibodyModular designComputational biologyDNA ligaseProtein engineeringComputer scienceBiologyChemistryBiochemistryGeneticsEnzymeGene

Abstract

fetched live from OpenAlex

Efforts to engineer recombinant antibodies for specific diagnostic and therapy applications are time consuming and expensive, as each new recombinant antibody needs to be optimized for expression, stability, bio-distribution, and pharmacokinetics. We have developed a new way to construct recombinant antibody-like "devices" by using a bottom-up approach to build them from well-behaved discrete recombinant antibody domains or "parts". Studies on antibody structure and function have identified antibody constant and variable domains with specific functions that can be expressed in isolation. We used the SpyTag/SpyCatcher protein ligase to join these parts together, thereby creating devices with desired properties based on summed properties of parts and in configurations that cannot be obtained by using genetic engineering. This strategy will create optimized recombinant antibody devices at reduced costs and with shortened development times.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

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

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 head0.247
Teacher spread0.238 · 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