Mechanism for Recognition of Polyubiquitin Chains: Balancing Affinity through Interplay between Multivalent Binding and Dynamics
Bibliographic record
Abstract
RAP80 plays a key role in signal transduction in the DNA damage response by recruiting proteins to DNA damage foci by binding K63-polyubiquitin chains with two tandem ubiquitin-interacting motifs (tUIM). It is generally recognized that the typically weak interaction between ubiquitin (Ub) and various recognition motifs is intensified by themes such as tandem recognition motifs and Ub polymerization to achieve biological relevance. However, it remains an intricate problem to develop a detailed molecular mechanism to describe the process that leads to amplification of the Ub signal. A battery of solution-state NMR methods and molecular dynamics simulations were used to demonstrate that RAP80-tUIM employs mono- and multivalent interactions with polyUb chains to achieve enhanced affinity in comparison to monoUb interactions for signal amplification. The enhanced affinity is balanced by unfavorable entropic effects that include partial quenching of rapid reorientation between individual UIM domains and individual Ub domains in the bound state. For the RAP80-tUIM-polyUb interaction, increases in affinity with increasing chain length are a result of increased numbers of mono- and multivalent binding sites in the longer polyUb chains. The mono- and multivalent interactions are characterized by intrinsically weak binding and fast off-rates; these weak interactions with fast kinetics may be an important factor underlying the transient nature of protein-protein interactions that comprise DNA damage foci.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".