Iterative Optimization of DNA Duplexes for Crystallization of SeqA-DNA Complexes
Bibliographic record
Abstract
Escherichia coli SeqA is a negative regulator of DNA replication that prevents premature reinitiation events by sequestering hemimethylated GATC clusters within the origin of replication. Beyond the origin, SeqA is found at the replication forks, where it organizes newly replicated DNA into higher ordered structures. SeqA associates only weakly with single GATC sequences, but it forms high affinity complexes with DNA duplexes containing multiple GATC sites. The minimal functional and structural unit of SeqA is a dimer, thereby explaining the requirement of at least two GATC sequences to form a high-affinity complex with hemimethylated DNA. Additionally, the SeqA architecture, with the oligomerization and DNA-binding domains separated by a flexible linker, allows binding to GATC repeats separated by up to three helical turns. Therefore, understanding the function of SeqA at a molecular level requires the structural analysis of SeqA bound to multiple GATC sequences. In protein-DNA crystallization, DNA can have none to an exceptional effect on the packing interactions depending on the relative sizes and architecture of the protein and the DNA. If the protein is larger than the DNA or footprints most of the DNA, the crystal packing is primarily mediated by protein-protein interactions. Conversely, when the protein is the same size or smaller than the DNA or it only covers a fraction of the DNA, DNA-DNA and DNA-protein interactions dominate crystal packing. Therefore, crystallization of protein-DNA complexes requires the systematic screening of DNA length and DNA ends (blunt or overhang). In this report, we describe how to design, optimize, purify and crystallize hemimethylated DNA duplexes containing tandem GATC repeats in complex with a dimeric variant of SeqA (SeqAΔ(41-59)-A25R) to obtain crystals suitable for structure determination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".