Proteome TopFIND 3.0 with TopFINDer and PathFINDer: database and analysis tools for the association of protein termini to pre- and post-translational events
Bibliographic record
Abstract
The knowledgebase TopFIND is an analysis platform focussed on protein termini, their origin, modification and hence their role on protein structure and function. Here, we present a major update to TopFIND, version 3, which includes a 70% increase in the underlying data to now cover a 90,696 proteins, 165,044 N-termini, 130,182 C-termini, 14,382 cleavage sites and 33,209 substrate cleavages in H. sapiens, M. musculus, A. thaliana, S. cerevisiae and E. coli. New features include the mapping of protein termini and cleavage entries across protein isoforms and significantly, the mapping of protein termini originating from alternative transcription and alternative translation start sites. Furthermore, two analysis tools for complex data analysis based on the TopFIND resource are now available online: TopFINDer, the TopFIND ExploRer, characterizes and annotates proteomics-derived N- or C-termini sets for their origin, sequence context and implications for protein structure and function. Neo-termini are also linked to associated proteases. PathFINDer identifies indirect connections between a protease and list of substrates or termini thus supporting the evaluation of complex proteolytic processes in vivo. To demonstrate the utility of the tools, a recent N-terminomics data set of inflamed murine skin has been re-analyzed. In re-capitulating the major findings originally performed manually, this validates the utility of these new resources. The point of entry for the resource is http://clipserve.clip.ubc.ca/topfind from where the graphical interface, all application programming interfaces (API) and the analysis tools are freely accessible.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.029 |
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".