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Phage Display Technology for Human Monoclonal Antibodies

2013· article· en· W124230207 on OpenAlexfundno aff
Cecilia Deantonio, Diego Cotella, Paolo Macor, Claudio Santoro, Daniele Sblattero

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
FundersTufts University School of MedicineCentro Singular de Investigación de GaliciaUppsala UniversitetTechnische Universität BraunschweigJapan Society for the Promotion of ScienceNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of TokyoUniversità degli Studi del Piemonte OrientaleHebrew University of JerusalemEidgenössische Technische Hochschule ZürichUniversity of TorontoChugai Pharmaceutical
KeywordsPhage displayMonoclonal antibodyPhagemidSelection (genetic algorithm)AntibodyBiologyComputational biologyEffectorFunction (biology)Negative selectionHybridoma technologyGeneGeneticsBacteriophageComputer scienceImmunologyEscherichia coliArtificial intelligenceGenome

Abstract

fetched live from OpenAlex

During the last 15 years in vitro technologies opened powerful routes to combine the generation of large libraries together with fast selection procedures to identify lead candidates. One of the commonest methods is based on the use filamentous phages. Antibodies (Abs) can be displayed successfully on the surface of phage by fusing the coding sequence of the antibody variable (V) regions to the phage minor coat protein pIII. By creating large libraries, antibodies with affinities comparable to those obtained using traditional hybridomas technology can be selected by a series of cycles of selection on antigen. As in this system antibody genes are cloned simultaneously with selection they can be easily further engineered for example by increasing their affinity (to levels unobtainable in the immune system), modulating their specificity or their effector function (by recloning into a full-length immunoglobulin scaffold). This chapter describes the basic protocols for antibody library construction, handling, and selection.

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)0.001
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 score0.523
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.473
Teacher spread0.433 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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