TraG-edy to Triumph: How Challenges in Crystallisation Efforts of TraG Led to Success
Bibliographic record
Abstract
For many projects involving the structural solution of a protein, a high-resolution crystal structure is a desirable goal for completing the project. However, protein crystallisation is a bottleneck to success; what do you do if a protein never crystallizes, and you don't have access to cryo-EM? TraG is a desirable structural target; it is a protein found in F-like Type IV Secretion Systems (T4SS) for the transmission of mobile DNA elements in gram-negative bacteria, serving as a major contributor to antibiotic resistance (Figure 1)1. TraG is essential in preventing redundant DNA transfer through a process termed entry exclusion, and the protein has no homologs with solved structures. Structural studies of TraG revealed the presence of a dynamic region between the N- and C-terminal domains of the protein; thermofluor, circular dichroism, collision induced unfolding mass spectrometry and SEC-MALS-SAXS experiments guided the design of mutants to lower flexibility and promote protein crystallisation. Despite years of effort and a variety of crystallisation techniques employed, a diffraction-quality crystal was not obtained. However, this led to similar examinations of other proteins in the F-like T4SS, which were then found to have dynamic regions as well and provided context to how the conjugative T4SS operates as a complex (Figure 2).
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".