The Max homodimeric b‐HLH‐LZ significantly interferes with the specific heterodimerization between the c‐Myc and Max b‐HLH‐LZ in absence of DNA: a quantitative analysis
Bibliographic record
Abstract
Specific heterodimerization plays a crucial role in the regulation of the biology of the cell. For example, the specific heterodimerization between the b-HLH-LZ transcription factors c-Myc and Max is a prerequisite for c-Myc transcriptional activity that leads to cell growth, proliferation and tumorigenesis. On the other hand, the Mad proteins can compete with c-Myc for Max. The Mad/Max heterodimer antagonizes the effect of the c-Myc/Max heterodimer. In this contribution, we have focused on the specific heterodimerization between the b-HLH-LZ domains of c-Myc and Max using CD and NMR. While the c-Myc and Max b-HLH-LZ domains are found to preferentially form a heterodimer; we demonstrate for the first time that a significant population of the Max homodimeric b-HLH-LZ can also form and hence interferes significantly with the specific heterodimerization. This indicates that the Max/Max homodimer can also interfere with c-Myc/Max functions, therefore adding to the complexity of the regulation of transcription by the Myc/Max/Mad network. The demonstration of the existence of the homodimeric population was made possible by the application of numerical routines that enable the simulation of composite spectroscopic signal (e.g. CD) as a function of temperature and total concentration of proteins. From a systems biology perspective, our routines may be of general interest as they offer the opportunity to treat many competing equilibriums in order to predict the probability of existence of protein complexes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".