Methods for the Expression, Purification, Preparation, and Biophysical Characterization of Constructs of the c-Myc and Max b-HLH-LZs
Bibliographic record
Abstract
Specific heterodimerization and DNA binding by the b-HLH-LZ transcription factors c-Myc and Max is central to the activation and repression activities of c-Myc that lead to cell growth, proliferation, and tumorigenesis (Adhikary and Eilers, Nat Rev Mol Cell Biol 6:635-645, 2005; Eilers and Eisenman, Genes Dev 22:2755-2766, 2008; Grandori et al., Annu Rev Cell Dev Biol 16:653-699, 2000; Whitfield and Soucek, Cell Mol Life Sci 69:931-934, 2011). Although many c-Myc-interacting partner proteins are known to interact through their HLH domain (Adhikary and Eilers, Nat Rev Mol Cell Biol 6:635-645, 2005), current knowledge regarding the structure and the determinants of molecular recognition of these complexes is still very limited. Moreover, recent advances in the development and use of b-HLH-LZ dominant negatives (Soucek et al., Nature 455:679-683, 2008) and inhibitors of c-Myc interaction with its protein partners (Bidwell et al., J Control Release 135:2-10, 2009; Mustata et al., J Med Chem 52:1247-1250, 2009; Prochownik and Vogt, Genes Cancer 1:650-659, 2010) or DNA highlight the importance of efficient protocols to prepare such constructs and variants. Here, we provide methods to produce and purify high quantities of pure and untagged b-HLH-LZ constructs of c-Myc and Max as well as specific c-Myc/Max heterodimers for their biophysical and structural characterization by CD, NMR, or crystallography. Moreover, biochemical methods to analyze the homodimers and heterodimers as well as DNA binding of these constructs by native electrophoresis are presented. In addition to enable the investigation of the c-Myc/Max b-HLH-LZ complexes, the protocols described herein can be applied to the biochemical characterization of various mutants of either partner, as well as to ternary complexes with other partner proteins.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.032 | 0.030 |
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".