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Isolation of Monoclonal Antibody Fragments from Phage Display Libraries

2009· article· en· W49499134 on OpenAlexaff
Mehdi Arbabi‐Ghahroudi, Jamshid Tanha, Roger MacKenzie

Bibliographic record

VenueMethods in molecular biology · 2009
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitute for Biological Sciences
Fundersnot available
KeywordsPanning (audio)Phage displayPhagemidMonoclonal antibodyAntibodyBiologyRecombinant DNAPeptide libraryAntigenMolecular biologyImmunoglobulin light chainEscherichia coliVirologyGeneBacteriophagePeptide sequenceGenetics

Abstract

fetched live from OpenAlex

Techniques developed over the past 20 years for the display of foreign peptides and proteins on the surfaces of filamentous bacteriophages have been a major driving force in the rapid development of recombinant antibody technology in recent years. With phage display of antibodies as one of its key components, recombinant antibody technology has led to the development of an increasing number of therapeutic monoclonal antibodies. Antibody gene libraries are fused to a gene encoding a phage coat protein. Recombinant phage expressing the resulting antibody libraries in fusion with the coat protein are propagated in Escherichia coli. Phage displaying monoclonal antibodies with specificities for target antigens are isolated from the libraries by a process called panning. The genes encoding the desired antibodies selected from the libraries are packaged within the phage particles, linking genotype and phenotype. Here, we describe the application of this technology to the construction of a phage-displayed single-domain antibody (sdAb) library based on the heavy chain antibody repertoire of a llama, the panning of the library against a peptide antigen and the expression, purification, and characterization of sdAbs isolated by panning.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.268
Threshold uncertainty score0.506

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 studies0.0000.000
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 head0.030
GPT teacher head0.446
Teacher spread0.416 · 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
GenreMethods

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

Citations31
Published2009
Admission routes1
Has abstractyes

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