Molecular Metamorphosis in Transcriptional Regulation
Bibliographic record
Abstract
The foundation of all life is the interaction of molecules. Molecular interaction occurs in the tightly packed cytoplasm. In this crowded environment, the molecules need to be able to establish stable interactions with specific interaction partners, and with specific ones only. Selectivity is essential for transcriptional regulation where DNA-binding proteins, more specifically transcription factors, need to reach their respective target DNA, such as an operator sequence, quickly. The transcription factor LacI for example reaches its DNA operator within minutes and establishes a tight interaction to it that will block transcriptions of the genes it regulates. With the other millions of sequences in the bacterial chromosome, it interacts only fleetingly. This is possible because a small part of the protein is disordered during the transient interaction with non-operator DNA. Only with specific DNA will the folding of the disordered region into a rigid helix be favored. When the disordered region folds into a helix, the transcription factor changes its structure and, with it, its function from a weak DNA binder to a strong one. Thinking about molecular structures as dynamic entities that react to their environment and understanding the origins of structural metamorphosis allow us to predict changes in the protein sequence that will affect their behavior in the cellular context. This is demonstrated in this thesis where I study the structural dynamics of LacI with molecular dynamic simulations and use the insights from these simulations to design protein variants with changed binding stability and selectivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".