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Record W2148538644 · doi:10.1002/jmr.587

Inhibition of HIV protease by monoclonal antibodies

2002· review· en· W2148538644 on OpenAlexfundno aff
J. Brynda, Milan Fábry, M. Hořejší, R. Stouracova, Julien Lescar, V. Chitarra, Marie‐Madeleine Riottot, Juraj Sedláček, G.A. Bentley

Bibliographic record

VenueJournal of Molecular Recognition · 2002
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueEuropean Synchrotron Radiation FacilityAkademie Věd České RepublikyGrantová Agentura České RepublikyEuropean CommissionInstitut national de la recherche scientifique
KeywordsMonoclonal antibodyProteaseEpitopeAntibodyEnzymePeptideChemistryVirologyBiochemistryBiologyImmunology

Abstract

fetched live from OpenAlex

The protease of HIV plays a critical role in the maturation of the infectious particles of the virus. The enzyme has therefore been extensively studied with the objective of developing therapeutics that inhibit viral proliferation. We have produced monoclonal antibodies specific for the HIV-1 protease, and selected those that inhibit enzyme function for use as probes to study the enzyme's activity and as an eventual aid for the development of potential inhibitors targeted to regions other than the active site. We have characterized two such mAbs, F11.2.32 and 1696, which have inhibition constants in the low nanomolar range and which recognize epitopes from different regions of the protease. The crystal structures of the two antibodies, both in the free state as well as complexes with peptide fragments corresponding to their respective epitopes, have been solved. The structural analyses, taken together with other functional data on the antibodies, suggest mechanisms of protease inhibition by these 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 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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.041
GPT teacher head0.304
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2002
Admission routes1
Has abstractyes

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Same venueJournal of Molecular RecognitionSame topicHIV Research and TreatmentFrench-language works237,207