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Record W3093545770 · doi:10.1002/1873-3468.13962

Structural plasticity of substrate selection by activation‐induced cytidine deaminase as a regulator of its genome‐wide mutagenic activity

2020· article· en· W3093545770 on OpenAlexafffund
Justin J. King, Mani Larijani

Bibliographic record

VenueFEBS Letters · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsSimon Fraser UniversityMemorial University of Newfoundland
FundersInstitute of Infection and ImmunityNatural Sciences and Engineering Research Council of Canada
KeywordsCytidine deaminaseRegulatorActivation-induced (cytidine) deaminaseCytidineSubstrate specificitySubstrate (aquarium)ChemistryGenomeSelection (genetic algorithm)BiologyBiochemistryGeneticsEnzymeCell biologyComputational biologyBiophysicsGeneSomatic hypermutationComputer science

Abstract

fetched live from OpenAlex

Activation-induced cytidine deaminase (AID) mediates somatic hypermutation and class-switch recombination of antibodies. Computational-biochemical and crystallography analyses of AID have identified three surface grooves for binding single-stranded DNA (ssDNA). Functional studies have also found evidence for RNA-binding motifs on AID. Although AID and the related apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) enzymes share a conserved core, AID uniquely features multiple substrate-binding motifs on its surface. Here we suggest that combinatorial deployment of AID's multiple ssDNA- or RNA-binding motifs yields many substrate-binding modes that can accommodate ssDNA, RNA, or DNA/RNA substrates of diverse structures. We also suggest that AID oligomerization generates yet additional novel substrate-binding modes. We propose that this plasticity in substrate choice is an evolved aspect of AID's structure that contributes to the regulation of its differential mutagenic activity at various loci.

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 score0.030
Threshold uncertainty score0.575

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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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