Simple sequences are rare in the Protein Data Bank
Bibliographic record
Abstract
A simple sequence is abundant in the proteins that have been sequenced to date. But unusual protein features, such as a simple sequence, are not present in the same high frequency within structural databases. A subset of these simple sequences, a group with a highly repetitive nature has been shown to be abundant in eukaryotes but not in prokaryotes. In this study, an examination of the eukaryotic proteins in the Protein Data Bank (PDB) has revealed a large deficiency of low complexity, highly repetitive protein repeats. Through simulated databases of similar samples of eukaryotic proteins taken from the National Center for Biotechnology Information (NCBI) database, it is shown that the PDB contains a significantly less highly repetitive, simple sequence than artificial databases of similar composition randomly derived from NCBI. When the structural data for those few PDB sequences that did contain a highly repetitive simple sequence is examined in detail, it is found that in most cases the tertiary structure is unknown for the regions consisting of a simple sequence. This lack of a simple sequence both in the PDB database and in the structural information suggests that this type of simple sequence may produce disordered structures that make structural characterization difficult.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.021 |
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".