ProteoConnections: A bioinformatics platform to facilitate proteome and phosphoproteome analyses
Bibliographic record
Abstract
Novel and improved computational tools are required to transform large-scale proteomics data into valuable information of biological relevance. To this end, we developed ProteoConnections, a bioinformatics platform tailored to address the pressing needs of proteomics analyses. The primary focus of this platform is to organize peptide and protein identifications, evaluate the quality of the acquired data set, profile abundance changes, and accelerate data interpretation. Peptide and protein identifications are stored into a relational database to facilitate data mining and to evaluate the quality of data sets using graphical reports. We integrated databases of known PTMs and other bioinformatics tools to facilitate the analysis of phosphoproteomics data sets and to provide insights for subsequent biological validation experiments. Phosphorylation sites are also annotated according to kinase consensus motifs, contextual environment, protein domains, binding motifs, and evolutionary conservation across different species. The practical application of ProteoConnections is further demonstrated for the analysis of the phosphoproteomics data sets from rat intestinal IEC-6 cells where we identified 9615 phosphorylation sites on 2108 phosphoproteins. Combined proteomics and bioinformatics analyses revealed valuable biological insights on the regulation of phosphoprotein functions via the introduction of new binding sites on scaffold proteins or the modulation of protein-protein, protein-DNA, or protein-RNA interactions. Quantitative proteomics data can be integrated into ProteoConnections to determine the changes in protein phosphorylation under different cell stimulation conditions or kinase inhibitors, as demonstrated here for the MEK inhibitor PD184352.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".