The Biological Crystallization Resource: Facilitating Knowledge-Based Protein Crystallizations
Bibliographic record
Abstract
The Biological Crystallization Resource (BCR) is a fully standardized, searchable, and comprehensive database of known crystallization conditions of biological macromolecular structures determined by X-ray crystallographic techniques, with a current total of over 18 000 entries. It was created to facilitate the discovery of the relationships between the properties of biological molecules and their optimal crystallization conditions. Construction and maintenance of the database make use of advanced data mining and manipulation techniques, with the associated software being capable of executing single or multiple parameter searches of database entries to determine optimal crystallization conditions for new targets of structural studies. It is a knowledge-based approach to deriving crystallization conditions designed to improve upon the very limited success rates observed for the random sparse matrix based screening methods currently widely employed in the field. Test results clearly demonstrate the predictive ability of BCR-derived knowledge-based crystallization screens, which not only deliver a more focused set of trial conditions but also the expectation of much higher crystallization success rates. The full BCR database, a comprehensive manual, and example demonstration are available at the following Web site: http://www.growacrystal.com .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".