MétaCan
Menu
Back to cohort
Record W4281620403 · doi:10.1002/cbic.202200202

Histidine Ligated Iron‐Sulfur Peptides

2022· article· en· W4281620403 on OpenAlexafffund
Luca Valer, Daniele Rossetto, Taylor Parkkila, Lorenzo Sebastianelli, Graziano Guella, Amber L. Hendricks, J. A. Cowan, Lingzi Sang, Sheref S. Mansy

Bibliographic record

VenueChemBioChem · 2022
Typearticle
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSimons Foundation
KeywordsHistidineCysteineChemistrySulfurCofactorIron–sulfur clusterPeptideBiochemistryMetalloproteinRedoxExtant taxonCombinatorial chemistryAmino acidOrganic chemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

Abstract Iron‐sulfur clusters are thought to be ancient cofactors that could have played a role in early protometabolic systems. Thus far, redox active, prebiotically plausible iron‐sulfur clusters have always contained cysteine ligands to the cluster. However, extant iron‐sulfur proteins can be found to exploit other modes of binding, including ligation by histidine residues, as seen with [2Fe‐2S] Rieske and MitoNEET proteins. Here, we investigated the ability of cysteine‐ and histidine‐containing peptides to coordinate a mononuclear Fe 2+ center and a [2Fe‐2S] cluster and compare their properties with purified iron‐sulfur proteins. The iron‐sulfur peptides were characterized by UV‐vis, circular dichroism, and paramagnetic NMR spectroscopies and cyclic voltammetry. Small (≤6 amino acids) peptides can coordinate [2Fe‐2S] clusters through a combination of cysteine and histidine residues with similar reduction potentials as their corresponding proteins. Such complexes may have been important for early cell‐like systems.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.302
Threshold uncertainty score1.000

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)0.0080.000

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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueChemBioChemSame topicMetalloenzymes and iron-sulfur proteinsFrench-language works237,207