Homopeptide and homocodon levels across fungi are coupled to GC/AT-bias and intrinsic disorder, with unique behaviours for some amino acids
Bibliographic record
Abstract
Homopeptides (runs of one amino-acid type) are evolutionarily important since they are prone to expand/contract during DNA replication, recombination and repair. To gain insight into the genomic/proteomic traits driving their variation, we analyzed how homopeptides and homocodons (which are pure codon repeats) vary across 405 Dikarya, and probed their linkage to genome GC/AT bias and other factors. We find that amino-acid homopeptide frequencies vary diversely between clades, with the AT-rich Saccharomycotina trending distinctly. As organisms evolve, homocodon and homopeptide numbers are majorly coupled to GC/AT-bias, exhibiting a bi-furcated correlation with degree of AT- or GC-bias. Mid-GC/AT genomes tend to have markedly fewer simply because they are mid-GC/AT. Despite these trends, homopeptides tend to be GC-biased relative to other parts of coding sequences, even in AT-rich organisms, indicating they absorb AT bias less or are inherently more GC-rich. The most frequent and most variable homopeptide amino acids favour intrinsic disorder, and there are an opposing correlation and anti-correlation versus homopeptide levels for intrinsic disorder and structured-domain content respectively. Specific homopeptides show unique behaviours that we suggest are linked to inherent slippage probabilities during DNA replication and recombination, such as poly-glutamine, which is an evolutionarily very variable homopeptide with a codon repertoire unbiased for GC/AT, and poly-lysine whose homocodons are overwhelmingly made from the codon AAG.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".