TopFIND 2.0--linking protein termini with proteolytic processing and modifications altering protein function
Bibliographic record
Abstract
Protein termini provide critical insights into the functional state of individual proteins. With recent advances in specific proteomics approaches to enrich for N- and C-terminomes, the global analysis of whole terminomes at a proteome-wide scale is now possible. Information on the actual N- and C-termini of proteins in vivo and any post-translational modifications, including their generation by proteolytic processing, is rapidly accumulating. To access this information we present version 2.0 of TopFIND (http://clipserve.clip.ubc.ca/topfind), a knowledgebase for protein termini, terminus modifications and underlying proteolytic processing. Built on a protein-centric framework TopFIND covers five species: Homo sapiens, Mus musculus, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli and incorporates information from curated community submissions, publications, UniProtKB and MEROPS. Emphasis is placed on the detailed description and classification of evidence supporting the reported identification of each cleavage site, terminus and modification. A suite of filters can be applied to select supporting evidence. A dynamic network representation of the relationship between proteases, their substrates and inhibitors as well as visualization of protease cleavage site specificities complements the information displayed. Hence, TopFIND supports in depth investigation of protein termini information to spark new hypotheses on protein function by correlating cleavage events and termini with protein domains and mutations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".