Effects of Temperature on the p53-DNA Binding Interactions and Their Dynamical Behavior: Comparing the Wild Type to the R248Q Mutant
Bibliographic record
Abstract
BACKGROUND: The protein p53 plays an active role in the regulation of cell cycle. In about half of human cancers, the protein is inactivated by mutations located primarily in its DNA-binding domain. Interestingly, a number of these mutations possess temperature-induced DNA-binding characteristics. A striking example is the mutation of Arg248 into glutamine or tryptophan. These mutants are defective for binding to DNA at 310 K although they have been shown to bind specifically to several p53 response elements at sub-physiological temperatures (298-306 K). METHODOLOGY/PRINCIPAL FINDINGS: This important experimental finding motivated us to examine the effects of temperature on the structure and configuration of R248Q mutant and compare it to the wild type protein. Our aim is to determine how and where structural changes of mutant variants take place due to temperature changes. To answer these questions, we compared the mutant to the wild-type proteins from two different aspects. First, we investigated the systems at the atomistic level through their DNA-binding affinity, hydrogen bond networks and spatial distribution of water molecules. Next, we assessed changes in their long-lived conformational motions at the coarse-grained level through the collective dynamics of their side-chain and backbone atoms separately. CONCLUSIONS: The experimentally observed effect of temperature on the DNA-binding properties of p53 is reproduced. Analysis of atomistic and coarse-grained data reveal that changes in binding are determined by a few key residues and provide a rationale for the mutant-loss of binding at physiological temperatures. The findings can potentially enable a rescue strategy for the mutant structure.
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 | 0.000 |
| 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 | 0.000 |
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".