Low Complexity Regions in Proteins and DNA are Poorly Correlated
Bibliographic record
Abstract
Low complexity sequences (LCRs) are well known within coding as well as non-coding sequences. A low complexity region within a protein must be encoded by the underlying DNA sequence. Here, we examine the relationship between the entropy of the protein sequence and that of the DNA sequence which encodes it. We show that they are poorly correlated whether starting with a low complexity region within the protein and comparing it to the corresponding sequence in the DNA or by finding a low complexity region within coding DNA and comparing it to the corresponding sequence in the protein. We show this is the case within the proteomes of five model organisms: Homo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana. We also report a significant bias against mononucleic codons in LCR encoding sequences. By comparison with simulated proteomes, we show that highly repetitive LCRs may be explained by neutral, slippage-based evolution, but compositionally biased LCRs with cryptic repeats are not. We demonstrate that other biological biases and forces must be acting to create and maintain these LCRs. Uncovering these forces will improve our understanding of protein LCR evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".