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Record W2095491500 · doi:10.1021/cb200450w

Cyclic Peptide Inhibitors of HIV-1 Capsid-Human Lysyl-tRNA Synthetase Interaction

2012· article· en· W2095491500 on OpenAlexaff
Varun Dewan, Tao Liu, Kuan‐Ming Chen, Ziqing Qian, Yong Xiao, Lawrence Kleiman, Kiran V. Mahasenan, Chenglong Li, Hiroshi Matsuo, Dehua Pei, Karin Musier‐Forsyth

Bibliographic record

VenueACS Chemical Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsJewish General Hospital
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Institutes of Health
KeywordsCapsidPeptideBinding siteBiochemistryBiologyCTDProtein–protein interactionAminoacyl tRNA synthetaseTransfer RNAPeptide sequencePlasma protein bindingMolecular biologyRNAGene

Abstract

fetched live from OpenAlex

The human immunodeficiency virus type 1 (HIV-1) capsid protein (CA) plays a critical role in the viral life cycle. The C-terminal domain (CTD) of CA binds to human lysyl-tRNA synthetase (hLysRS), and this interaction facilitates packaging of host cell tRNA(Lys,3), which serves as the primer for reverse transcription. Here, we report the library synthesis, high-throughput screening, and identification of cyclic peptides (CPs) that bind HIV-1 CA. Scrambling or single-residue changes of the selected peptide sequences eliminated binding, suggesting a sequence-specific mode of interaction. Two peptides (CP2 and CP4) subjected to detailed analysis also inhibited hLysRS/CA interaction in vitro. Nuclear magnetic resonance spectroscopy and mutagenesis studies revealed that both CPs bind to a site proximal to helix 4 of the CA-CTD, which is the known site of hLysRS interaction. These results extend the current repertoire of CA-binding molecules to a new class of peptides targeting a novel site with potential for development into novel antiviral agents.

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)0.000
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 score0.024
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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

Citations35
Published2012
Admission routes1
Has abstractyes

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