LPS-annotate: complete annotation of compositionally biased regions in the protein knowledgebase
Why this work is in the frame
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Bibliographic record
Abstract
Compositional bias (i.e. a skew in the composition of a biological sequence towards a subset of residue types) can occur at a wide variety of scales, from compositional biases of whole genomes, down to short regions in individual protein and gene-DNA sequences that are compositionally biased (CB regions). Such CB regions are made from a subset of residue types that are strewn along the length of the region in an irregular way. Here, we have developed the database server LPS-annotate, for the analysis of such CB regions, and protein disorder in protein sequences. The algorithm defines compositional bias through a thorough search for lowest-probability subsequences (LPSs) (i.e., the least likely sequence regions in terms of composition). Users can (i) initially annotate CB regions in input protein or nucleotide sequences of interest, and then (ii) query a database of greater than 1,500,000 pre-calculated protein-CB regions, for investigation of further functional hypotheses and inferences, about the specific CB regions that were discovered, and their protein disorder propensities. We demonstrate how a user can search for CB regions of similar compositional bias and protein disorder, with a worked example. We show that our annotations substantially augment the CB-region annotations that already exist in the UniProt database, with more comprehensive annotation of more complex CB regions. Our analysis indicates tens of thousands of CB regions that do not comprise globular domains or transmembrane domains, and that do not have a propensity to protein disorder, indicating a large cohort of protein-CB regions of biophysically uncharacterized types. This server and database is a conceptually novel addition to the workbench of tools now available to molecular biologists to generate hypotheses and inferences about the proteins that they are investigating. It can be accessed at http://libaio.biol.mcgill.ca/lps-annotate.html. Database URL: http://libaio.biol.mcgill.ca/lps-annotate.html.
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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 it