Homopeptide and Homocodon Levels are Coupled to GC/AT Bias Levels, Intrinsic Disorder Propensity and other Factors Across Diverse Fungi
Bibliographic record
Abstract
Abstract Homopeptides (consecutive runs of one amino-acid type) are suggested to play important roles in proteome evolution, since they are prone to expand/contract during DNA replication, recombination and repair. It is currently not clear how homopeptide frequencies vary as organisms evolve, and which genomic/proteomic traits drive variation. Thus, to gain insight, we analyzed how homopeptides and homocodons (which are pure codon repeats) vary across 405 Dikarya, and probed how this variation is linked to GC/AT bias amongst other factors. We observe that amino-acid homopeptide frequencies vary diversely between clades (even close relatives), with the AT-rich Saccharomycotina trending distinctly. As organisms evolve, homocodon and homopeptide numbers are majorly coupled to GC/AT-bias, with medium GC/AT genomes having markedly fewer. Despite this, homopeptides tend to be more GC-rich than other proteome areas, even in AT-rich organisms, indicating they absorb AT bias less or are inherently more GC-rich. Furthermore, the purity of homopeptides (i.e., the degree one codon type predominates in them) varies least for amino acids with GC/AT-balanced codon repertoires, with most variation for arginine since it has only one AT-rich codon (out of six). The most frequent and most variable homopeptide amino acids have greater intrinsic disorder propensity, and annotated intrinsic disorder fractions are strongly correlated with homopeptide levels (unlike structured domain fractions, which are anti-correlated). Poly-glutamine uniquely behaves as an evolutionarily very variable homopeptide with a codon repertoire unbiased for GC/AT. In summary, homopeptide/homocodon levels are coupled to or influenced by several factors, including GC/AT bias and amino-acid intrinsic disorder propensity.
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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 | 0.001 |
| 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".