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Record W2972303634 · doi:10.1093/molbev/msz200

Can the RNA World Still Function without Cytidine?

2019· article· en· W2972303634 on OpenAlexafffund
Andrew S. Tupper, Ralph E. Pudritz, Paul G. Higgs

Bibliographic record

VenueMolecular Biology and Evolution · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsBiologyRNAGeneticsRNA world hypothesisSequence (biology)CytosineRibozymeBase pairComputational biologyAlphabetPairingDNAGenePhysics

Abstract

fetched live from OpenAlex

Most scenarios for the origin of life assume that RNA played a key role in both catalysis and information storage. The A, U, G, and C nucleobases in modern RNA all participate in secondary structure formation and replication. However, the rapid deamination of C to U and the absence of C in meteorite samples suggest that prebiotic RNA may have been deficient in cytosine. Here, we assess the ability of RNA sequences formed from a three-letter AUG alphabet to perform both structural and genetic roles in comparison to sequences formed from the AUGC alphabet. Despite forming less thermodynamically stable helices, the AUG alphabet can find a broad range of structures and thus appears sufficient for catalysis in the RNA World. However, in the AUG case, longer sequences are required to form structures with an equivalent complexity. Replication in the AUG alphabet requires GU pairing. Sequence fidelity in the AUG alphabet is low whenever G's are present in the sequence. We find that AUG sequences evolve to AU sequences if GU pairing is rare, and to RU sequences if GU pairing is common (R denotes A or G). It is not possible to conserve a G at a specific site in either case. These problems do not rule out the possibility of an RNA World based on AUG, but they show that it wouldbe significantly more difficult than with a four-base alphabet.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.302

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 head0.005
GPT teacher head0.231
Teacher spread0.227 · 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 designObservational
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

Citations3
Published2019
Admission routes2
Has abstractyes

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