MétaCan
Menu
Back to cohort
Record W4401341384 · doi:10.1021/acs.accounts.4c00254

Protocellular Heme and Iron–Sulfur Clusters

2024· review· en· W4401341384 on OpenAlexafffund
Daniele Rossetto, Nemanja Cvjetan, Peter Walde, Sheref S. Mansy

Bibliographic record

VenueAccounts of Chemical Research · 2024
Typereview
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsUniversity of Alberta
FundersUniversità degli Studi di TrentoEidgenössische Technische Hochschule ZürichNatural Sciences and Engineering Research Council of CanadaEuropean CommissionAlfred P. Sloan FoundationUniversity of AlbertaGordon and Betty Moore FoundationSimons Foundation
KeywordsSulfurHemeChemistryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Conspectus Central to the quest of understanding the emergence of life is to uncover the role of metals, particularly iron, in shaping prebiotic chemistry. Iron, as the most abundant of the accessible transition metals on the prebiotic Earth, played a pivotal role in early biochemical processes and continues to be indispensable to modern biology. Here, we discuss our recent contributions to probing the plausibility of prebiotic complexes with iron, including heme and iron–sulfur clusters, in mediating chemistry beneficial to a protocell. Laboratory experiments and spectroscopic findings suggest plausible pathways, often facilitated by UV light, for the synthesis of heme and iron–sulfur clusters. Once formed, heme displays catalytic, peroxidase-like activity when complexed with amphiphiles. This activity could have been beneficial in two ways. First, heme could have catalytically removed a molecule (H 2 O 2 ) that could have had degradative effects on a protocell. Second, heme could have helped in the synthesis of the building blocks of life by coupling the reduction of H 2 O 2 with the oxidation of organic substrates. The necessity of amphiphiles to avoid the formation of inactive complexes of heme is telling, as the modern-day electron transport chain possesses heme embedded within a lipid membrane. Conversely, prebiotic iron–sulfur peptides have yet to be reported to partition into lipid membranes, nor have simple iron–sulfur peptides been found to be capable of participating in the synthesis of organic molecules. Instead, iron–sulfur peptides span a wide range of reduction potentials complementary to the reduction potentials of hemes. The reduction potential of iron–sulfur peptides can be tuned by the type of iron–sulfur cluster formed, e.g., [2Fe-2S] versus [4Fe-4S], or by the substitution of ligands to the metal center. Since iron–sulfur clusters easily form upon stochastic encounters between iron ions, hydrosulfide, and small organic molecules possessing a thiolate, including peptides, the likelihood of soluble iron–sulfur clusters seems to be high. What remains challenging to determine is if iron–sulfur peptides participated in early prebiotic chemistry or were recruited later when protocellular membranes evolved that were compatible with the exploitation of electron transfer for the storage of energy as a proton gradient. This problem mirrors in some ways the difficulty in deciphering the origins of metabolism as a whole. Chemistry that resembles some facets of extant metabolism must have transpired on the prebiotic Earth, but there are few clues as to how and when such chemistry was harnessed to support a (proto)cell. Ultimately, unraveling the roles of hemes and iron–sulfur clusters in prebiotic chemistry promises to deepen our understanding of the origins of life on Earth and aids the search for life elsewhere in the universe.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.133
GPT teacher head0.418
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAccounts of Chemical ResearchSame topicMetalloenzymes and iron-sulfur proteinsFrench-language works237,207