Evaluation and classification of RING-finger domains encoded by the Arabidopsis genome
Bibliographic record
Abstract
BACKGROUND: In computational analysis, the RING-finger domain is one of the most frequently detected domains in the Arabidopsis proteome. In fact, it is more abundant in Arabidopsis than in other eukaryotic genomes. However, computational analysis might classify ambiguous domains of the closely related PHD and LIM motifs as RING domains by mistake. Thus, we set out to define an ordered set of Arabidopsis RING domains by evaluating predicted domains on the basis of recent structural data. RESULTS: Inspection of the proteome with a current InterPro release predicts 446 RING domains. We evaluated each detected domain and as a result eliminated 59 false positives. The remaining 387 domains were grouped by cluster analysis and according to their metal-ligand arrangement. We further defined novel patterns for additional computational analyses of the proteome. They were based on recent structural data that enable discrimination between the related RING, PHD and LIM domains. These patterns allow us to predict with different degrees of certainty whether a particular domain is indeed likely to form a RING finger. CONCLUSIONS: In summary, 387 domains have a significant potential to form a RING-type cross-brace structure. Many of these RING domains overlap with predicted PHD domains; however, the RING domain signature mostly prevails. Thus, the abundance of PHD domains in Arabidopsis has been significantly overestimated. Cluster analysis of the RING domains defines groups of proteins, which frequently show significant similarity outside the RING domain. These groups document a common evolutionary origin of their members and potentially represent genes of overlapping functionality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".