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Production and Characterization of Polyclonal Generic Phosphotyrosine‐specific Antibodies

2016· article· en· W3175620938 on OpenAlexaff
Steven Pelech, Lambert Yue, Shenshen Lai, Dirk Winkler, Jane Shi, Hong Zhang

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsKinexus Bioinformatics Corporation (Canada)University of British Columbia
Fundersnot available
KeywordsPolyclonal antibodiesMonoclonal antibodyMolecular biologyAntibodyAffinity chromatographyPhosphoserinePrimary and secondary antibodiesBiologyBiochemistryPhosphopeptideChemistryPeptidePhosphorylationEnzymeImmunology

Abstract

fetched live from OpenAlex

Reversible protein‐tyrosine phosphorylation plays critical roles in cell regulation under normal and pathological conditions, which has made generic phosphotyrosine (pY) antibodies invaluable tools for biomedical research. A recent study that compared the specificities of three widely‐used monoclonal pY antibodies has raised concerns about strong sequence motif selectivity and low overall coverage rates. These issues can potentially introduce significant bias in pY site identification and quantification. In this study we describe a novel strategy of generating a pool of polyclonal antibodies from a large set of physiological pY‐site sequences. Over 400 peptides spanning 7 to 20 amino acids in length, each featuring 1 to 7 phosphorylation sites and representing over 1000 phosphosites, were used to immunize over 100 rabbits. The sera from these rabbits were subjected to ammonium sulphate fractionation, pooled, and then affinity‐purified on pY‐agarose columns to produce the PYK antibody preparations. The specificity of the purified PYK antibody was assessed with phosphopeptide microarrays, including the Jerini Peptide Technologies Phosphatase Peptide Microarray, which features 6,099 peptides representing diverse human pY‐sites. The rabbit polyclonal PYK phosphotyrosine antibody proved to perform better than the well known 4G10, PY20 and PY100 mouse monoclonal antibodies. PYK was extremely stable to repeated freeze thaw, and found to be 4‐fold or more sensitive for detection of phosphopeptides on arrays and for proteins following Western blotting of EGF‐treated A431 cell lysates than the monoclonal antibodies. Like the other monoclonal pY antibodies, PYK reactivities with phosphothreonine and phosphoserine were over 200‐ and 300‐fold less, respectively, when compared to phosphotyrosine on phosphopeptide microarrays. PYK also detected a larger number of diverse pY‐containing peptides than 4G10, PY20 and PY100 on phosphopeptide microarrays, and unlike these other antibodies, acidic amino acid residues surrounding the pY‐sites were not negative determinants for antibody recognition. Since acidic amino acids are important positive determinants for substrate recognition for most protein‐tyrosine kinases and flank most protein‐tyrosine phosphosites, our findings indicate that the PYK antibody may be more useful for enrichment and analysis of physiological pY‐sites than the commonly used mouse monoclonal antibodies for such purposes. Further purification of the PYK antibody over an IgG‐agarose column also allowed development of preparation of a reporter antibody that was suitable for use in sandwich antibody microarrays to assess changes in the tyrosine phosphorylation status of over 500 signaling proteins simultaneously with as little as 50 μg of crude lysate protein from cells and tissues. Support or Funding Information Supported by Kinexus Bioinformatics Corporation.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.271
Teacher spread0.241 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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