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Record W4385442608 · doi:10.1021/acs.jproteome.3c00159

Determination of Protein Monoclonal–Antibody Epitopes by a Combination of Structural Proteomics Methods

2023· article· en· W4385442608 on OpenAlexafffund
Evgeniy V. Petrotchenko, Elisabete Nascimento, Jody Melton Witt, Christoph H. Borchers

Bibliographic record

VenueJournal of Proteome Research · 2023
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMcGill UniversityJewish General Hospital
FundersTerry Fox Research InstituteFondation De Famille Alvin SegalGenome CanadaWarren Y. Soper Charitable TrustMcGill University
KeywordsEpitopeMonoclonal antibodyProteomicsEpitope mappingAntigenComputational biologyProtein–protein interactionChemistryProtein structureAntibodyBiologyBiochemistryImmunology

Abstract

fetched live from OpenAlex

Structural proteomics techniques are useful for the determination of protein interaction interfaces. Each technique provides orthogonal structural information on the structure and the location of protein interaction sites. Here, we have characterized a monoclonal antibody epitope for a protein antigen by a combination of differential photoreactive surface modification (SM), cross-linking (CL), differential hydrogen-deuterium exchange (HDX), and epitope extraction/excision. We found that experimental data from different approaches agree with each other in determining the epitope of the monoclonal antibody on the protein antigens using the HIV-1 p24-mAb E complex as an illustrative example. A combination of these multiple structural proteomics approaches results in a detailed picture of the interaction of the proteins and increases confidence in the determination of the final structure of the protein interaction interface. Data are available via ProteomeXchange with identifier PXD040902.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.111
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.511
Teacher spread0.413 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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