Computational analysis of the stability of SCF ligases employing domain information
Bibliographic record
Abstract
Because of the unequivocally fundamental role of SCF ubiquitin ligase in many biological functions within a living cell including regulating DNA repair, cell cycle progression, and inflammation, we have analyzed the role of domain interactions in determining particular types of protein-protein interactions (PPIs) that are known or predicted to occur involving subunit components of the SCF-ligase complex. We focus on the prediction and analysis of obligate and non-obligate SCF-ligase complexes by using sequence domains from the Pfam database. After extracting different types of feature vectors, the prediction is performed via a support vector machine (SVM). The numerical results demonstrate that most of the interactions of SCF-ligase complexes are mediated by at least one domain. Moreover, domain-domain interactions dominate in obligate complexes whereas non-obligate complexes exhibit more domain-peptide chain interactions. Also, the computational results show that the best prediction accuracy of 80.46% is achieved using the combination of feature vectors of domain-domain type, domain-peptide chain type and no-domain interactions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".