Prebiotic Environments with Mg<sup>2+</sup> and Thiophilic Metal Ions Increase the Thermal Stability of Cysteine and Non-cysteine Peptides
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wet–dry cycles driven by heating to high temperatures are frequently invoked for the prebiotic synthesis of peptides. Similarly, iron–sulfur clusters are often cited as an example of an ancient catalyst that helped prune early chemical systems into metabolic-like pathways. Because extant iron–sulfur clusters are metallocofactors of protein enzymes and nearly ubiquitous across biology, a reasonable hypothesis is that prebiotic iron–sulfur peptides formed on the early Earth. However, iron–sulfur clusters are coordinated by multiple cysteine residues, and the stability of cysteines to the heat steps of wet–dry cycles has not been determined. It, therefore, has remained unclear if the peptides needed to stabilize the formation of iron–sulfur clusters could have formed. If not, then iron–sulfur-dependent activity may have emerged later, when milder, more biological-like peptide synthesis machinery took hold. Here, we report the thermal stability of cysteine-containing peptides. We show that temperatures of 150 °C lead to the rapid degradation of cysteinyl peptides. However, the presence of Mg 2+ at environmentally reasonable concentrations leads to significant protection. Thiophilic metal ions also protect against degradation at 150 °C but require concentrations not frequently observed in the environment. Nevertheless, cysteine-containing peptides are stable at lower, prebiotically plausible temperatures in seawater, carbonate lake, and ferrous lake conditions. The data are consistent with the persistence of cysteine-containing peptides on the early Earth in environments rich in metal ions. High concentrations of Mg 2+ are common intra- and extra-cellularly, suggesting that the protection afforded by Mg 2+ may reflect conditions that were present on the prebiotic Earth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".