In Situ Proteolysis to Generate Crystals for Structure Determination: An Update
Bibliographic record
Abstract
For every 100 purified proteins that enter crystallization trials, an average of 30 form crystals, and among these only 13-15 crystallize in a form that enables structure determination. In 2007, Dong et al reported that the addition of trace amounts of protease to crystallization trials--in situ proteolysis--significantly increased the number of proteins in a given set that produce diffraction quality crystals. 69 proteins that had previously resisted structure determination were subjected to crystallization with in situ proteolysis and ten crystallized in a form that led to structure determination (14.5% success rate). Here we apply in situ proteolysis to over 270 new soluble proteins that had failed in the past to produce crystals suitable for structure determination. These proteins had produced no crystals, crystals that diffracted poorly, or produced twinned and/or unmanageable diffraction data. The new set includes yeast and prokaryotic proteins, enzymes essential to protozoan parasites, and human proteins such as GTPases, chromatin remodeling proteins, and tyrosine kinases. 34 proteins yielded deposited crystal structures of 2.8 A resolution or better, for an overall 12.6% success rate, and at least ten more yielded well-diffracting crystals presently in refinement. The success rate among proteins that had previously crystallized was double that of those that had never before yielded crystals. The overall success rate is similar to that observed in the smaller study, and appears to be higher than any other method reported to rescue stalled protein crystallography projects.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".