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Record W4295078755 · doi:10.1007/s12104-022-10109-z

Backbone and side-chain resonance assignments of the NISTmAb-scFv and antigen-binding study

2022· article· en· W4295078755 on OpenAlexafffund
Houman Ghasriani, Sara Ahmadi, Derek J. Hodgson, Yves Aubin

Bibliographic record

VenueBiomolecular NMR Assignments · 2022
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsCarleton UniversityHealth Canada
FundersHealth Canada
KeywordsAntigenImmunoglobulin light chainAntibodyMonoclonal antibodySingle-chain variable fragmentPeptideChemistryComputational biologyMolecular biologyBiologyBiochemistryImmunology

Abstract

fetched live from OpenAlex

Monoclonal antibodies (mAbs) therapeutics are the largest and fastest growing class of biologic drugs, amongst which, the vast majority are immunoglobulin G1 (IgG1). Their antigen binding abilities are used for the treatment of immunologic diseases, cancer therapy, reversal of drug effects, and targeting viruses and bacteria. The high importance of therapeutic mAbs and their derivatives has called for the generation of well-characterized standards for method development and calibration. One such standard, the NISTmAb RM 8621 based on the antibody motavizumab, has been developed by the National Institute of Standards and Technologies (NIST) in the US. Here, we present the resonance assignment of the single chain variable fragment, NISTmAb-scFv, that was engineered by linking the variable domains of the heavy and light chains of the NISTmAb. Also, addition of a peptide, corresponding to the target antigen of motavizumab, to samples of NISTmAb-scFv has induced chemical shift perturbations on residues lining the antigen binding interface thereby indicating proper folding of the NISTmAb-scFv.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.298
Teacher spread0.276 · 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 designObservational
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

Citations9
Published2022
Admission routes2
Has abstractyes

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