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Record W4410480159 · doi:10.1002/pro.70153

Molecular characterization of a synthetic neutralizing antibody targeting p67 of <i>Theileria parva</i>

2025· article· en· W4410480159 on OpenAlexafffund
Shane Miersch, Alex U. Singer, Chao Chen, Frédéric A. Fellouse, Anupriya Gopalsamy, Luciano J. Costa, Anna Lacasta, Hannah Wambui Chege, Naomi Chege, Vishvanath Nene, Sachdev S. Sidhu

Bibliographic record

VenueProtein Science · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicToxin Mechanisms and Immunotoxins
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoUniversity of Waterloo
FundersArgonne National LaboratoryInternational Development Research CentreBill and Melinda Gates Foundation
KeywordsEpitopeVirologyAntibodyNeutralizationNeutralizing antibodyTheileria parvaBiologyAntigenPhage displayPeptideEpitope mappingLinear epitopeMutagenesisPeptide libraryPeptide sequenceVirusImmunologyMutationGeneticsBiochemistryParasite hosting

Abstract

fetched live from OpenAlex

The Theileria parva sporozoite surface antigen p67 is a target of the bovine humoral immune response that generates antibodies capable of providing protection against subsequent infection. As a result, p67 has been the subject of efforts aimed at the development of an anti-sporozoite subunit vaccine. Previous studies have identified neutralizing epitopes in the N- and C-terminal regions of the full-length protein and shown that immunization with a C-terminal fragment of p67 (p67C) alone is capable of eliciting protection. To identify additional neutralizing epitopes in p67C, selections were conducted against it using a phage-displayed synthetic antibody library. An antibody that neutralized the sporozoite in vitro was identified, and the crystal structure of a Fab:peptide complex was elucidated. Mutagenesis studies aimed at validating and further characterizing the Fab:peptide interaction identified critical residues involved in binding and neutralization. This study also validates distinct epitopes for previously reported neutralizing antibodies.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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
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

Citations2
Published2025
Admission routes2
Has abstractyes

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