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Record W2414301904 · doi:10.2174/1386207023330273

Rationally Designed Allosteric Variants of Hammerhead Ribozymes Responsive to the HIV-1 Tat Protein

2002· article· en· W2414301904 on OpenAlexaff
Dennis Y. Wang, Dipankar Sen

Bibliographic record

VenueCombinatorial Chemistry & High Throughput Screening · 2002
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAllosteric regulationRibozymeRational designEffectorComputational biologyChemistryBiochemistryBiologyStereochemistryRNAEnzymeGeneticsGene

Abstract

fetched live from OpenAlex

Hammerhead ribozymes that are subject to allosteric control by small molecule and oligonucleotide effectors have been reported recently. Rational design has been an effective strategy for the creation of these ribozymes, which incorporate structurally interdependent hammerhead motifs and effector-binding sequences. In this paper we report the rational design of the first protein-responsive allosteric ribozymes that are regulated by the HIV-1 Tat. The TAR-Tat interaction of HIV-1 has the interesting feature that both Tat and arginine are able to bind to and bring about comparable conformational changes in the TAR loop. Here we describe the construction of two classes of TAR-modified hammerhead ribozymes and their response to Tat protein and to its derivatives. Instances of both allosteric activation and inhibition were found. Interestingly, the activation response was stimulated by both Tat and argininamide while the inhibitory response was stimulated by Tat and by its derivative peptide, ADP1, but not by argininamide. Overall, the extent of allosteric response in our ribozymes was modest relative to those reported for ribozymes with small molecule effectors. Future work utilizing combinatorial approaches along with elements of rational design should reveal the means by which highly efficient, protein-mediated allostery of ribozymes may be achieved.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.280
Teacher spread0.245 · 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.

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

Citations28
Published2002
Admission routes1
Has abstractyes

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