Molecular dynamics studies on the DNA-binding process of ERG
Bibliographic record
Abstract
The ETS family of transcription factors regulate gene targets by binding to a core GGAA DNA-sequence. The ETS factor ERG is required for homeostasis and lineage-specific functions in endothelial cells, some subset of haemopoietic cells and chondrocytes; its ectopic expression is linked to oncogenesis in multiple tissues. To date details of the DNA-binding process of ERG including DNA-sequence recognition outside the core GGAA-sequence are largely unknown. We combined available structural and experimental data to perform molecular dynamics simulations to study the DNA-binding process of ERG. In particular we were able to reproduce the ERG DNA-complex with a DNA-binding simulation starting in an unbound configuration with a final root-mean-square-deviation (RMSD) of 2.1 Å to the core ETS domain DNA-complex crystal structure. This allowed us to elucidate the relevance of amino acids involved in the formation of the ERG DNA-complex and to identify Arg385 as a novel key residue in the DNA-binding process. Moreover we were able to show that water-mediated hydrogen bonds are present between ERG and DNA in our simulations and that those interactions have the potential to achieve sequence recognition outside the GGAA core DNA-sequence. The methodology employed in this study shows the promising capabilities of modern molecular dynamics simulations in the field of protein DNA-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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".