MétaCan
Menu
Back to cohort
Record W2592243077 · doi:10.1088/1478-3975/aa64a4

An evolution-based strategy for engineering allosteric regulation

2017· article· en· W2592243077 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsHillsborough Hospital
FundersNIH Office of the DirectorNational Institutes of HealthGordon and Betty Moore Foundation
KeywordsAllosteric regulationSynthetic biologyProtein engineeringComputational biologyRational designComputer scienceBiologyEnzymeBiochemistryGenetics

Abstract

fetched live from OpenAlex

Allosteric regulation provides a way to control protein activity at the time scale of milliseconds to seconds inside the cell. An ability to engineer synthetic allosteric systems would be of practical utility for the development of novel biosensors, creation of synthetic cell signaling pathways, and design of small molecule pharmaceuticals with regulatory impact. To this end, we outline a general approach-termed rational engineering of allostery at conserved hotspots (REACH)-to introduce novel regulation into a protein of interest by exploiting latent allostery that has been hard-wired by evolution into its structure. REACH entails the use of statistical coupling analysis (SCA) to identify 'allosteric hotspots' on protein surfaces, the development and implementation of experimental assays to test hotspots for functionality, and a toolkit of allosteric modulators to impinge on endogenous cellular circuitry. REACH can be broadly applied to rewire cellular processes to respond to novel inputs.

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.

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 score0.211
Threshold uncertainty score0.350

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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