Variation among species in proteomic sulphur content is related to environmental conditions
Bibliographic record
Abstract
The elemental composition of proteins influences the quantities of different elements required by organisms. Here, we considered variation in the sulphur content of whole proteomes among 19 Archaea, 122 Eubacteria and 10 eukaryotes whose genomes have been fully sequenced. We found that different species vary greatly in the sulphur content of their proteins, and that average sulphur content of proteomes and genome base composition are related. Forces contributing to variation in proteomic sulphur content appear to operate quite uniformly across the proteins of different species. In particular, the sulphur content of orthologous proteins was frequently correlated with mean proteomic sulphur contents. Among prokaryotes, proteomic sulphur content tended to be greater in anaerobes, relative to non-anaerobes. Thermophiles tended to have lower proteomic sulphur content than non-thermophiles, consistent with the thermolability of cysteine and methionine residues. This work suggests that persistent environmental growth conditions can influence the evolution of elemental composition of whole proteomes in a manner that may have important implications for the amount of sulphur used by living organisms to build proteins. It extends previous studies that demonstrated links between transient changes in environmental conditions and the elemental composition of subsets of proteins expressed under these conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".