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Record W4306809517 · doi:10.1093/molbev/msac226

Early Nitrogenase Ancestors Encompassed Novel Active Site Diversity

2022· article· en· W4306809517 on OpenAlexfundno aff
Sarah L. Schwartz, Amanda K. Garcia, Betül Kaçar, Gregory P. Fournier

Bibliographic record

VenueMolecular Biology and Evolution · 2022
Typearticle
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsnot available
FundersNational Defense Science and Engineering GraduateNational Aeronautics and Space AdministrationIndian Council of Agricultural ResearchScience Mission DirectorateCanada's Michael Smith Genome Sciences CentreSimons FoundationUniversity of Wisconsin-Madison
KeywordsBiologyNitrogenaseExtant taxonEvolutionary biologySequence (biology)Diversity (politics)GeneticsNitrogen fixation

Abstract

fetched live from OpenAlex

Ancestral sequence reconstruction (ASR) infers predicted ancestral states for sites within sequences and can constrain the functions and properties of ancestors of extant protein families. Here, we compare the likely sequences of inferred nitrogenase ancestors to extant nitrogenase sequence diversity. We show that the most-likely combinations of ancestral states for key substrate channel residues are not represented in extant sequence space, and rarely found within a more broadly defined physiochemical space-supporting that the earliest ancestors of extant nitrogenases likely had alternative substrate channel composition. These differences may indicate differing environmental selection pressures acting on nitrogenase substrate specificity in ancient environments. These results highlight ASR's potential as an in silico tool for developing hypotheses about ancestral enzyme functions, as well as improving hypothesis testing through more targeted in vitro and in vivo experiments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations13
Published2022
Admission routes1
Has abstractyes

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