Structure-function analysis of the retinoblastoma tumor suppressor protein – is the whole a sum of its parts?
Bibliographic record
Abstract
Biochemical analysis of the retinoblastoma protein's function has received considerable attention since it was cloned just over 20 years ago. During this time pRB has emerged as a key regulator of the cell division cycle and its ability to block proliferation is disrupted in the vast majority of human cancers. Much has been learned about the regulation of E2F transcription factors by pRB in the cell cycle. However, many questions remain unresolved and researchers continue to explore this multifunctional protein. In particular, understanding how its biochemical functions contribute to its role as a tumor suppressor remains to be determined. Since pRB has been shown to function as an adaptor molecule that links different proteins together, or to particular promoters, analyzing pRB by disrupting individual protein interactions holds tremendous promise in unraveling the intricacies of its function. Recently, crystal structures have reported how pRB interacts with some of its molecular partners. This information has created the possibility of rationally separating pRB functions by studying mutants that disrupt individual binding sites. This review will focus on literature that investigates pRB by isolating functions based on binding sites within the pocket domain. This article will also discuss the prospects for using this approach to further explore the unknown functions of pRB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".