LigASite a database of biologically relevant binding sites in proteins with known apo-structures
Bibliographic record
Abstract
Better characterization of binding sites in proteins and the ability to accurately predict their location and energetic properties are major challenges which, if addressed, would have many valuable practical applications. Unfortunately, reliable benchmark datasets of binding sites in proteins are still sorely lacking. Here, we present LigASite ('LIGand Attachment SITE'), a gold-standard dataset of binding sites in 550 proteins of known structures. LigASite consists exclusively of biologically relevant binding sites in proteins for which at least one apo- and one holo-structure are available. In defining the binding sites for each protein, information from all holo-structures is combined, considering in each case the quaternary structure defined by the PQS server. LigASite is built using simple criteria and is automatically updated as new structures become available in the PDB, thereby guaranteeing optimal data coverage over time. Both a redundant and a culled non-redundant version of the dataset is available at http://www.scmbb.ulb.ac.be/Users/benoit/LigASite. The website interface allows users to search the dataset by PDB identifiers, ligand identifiers, protein names or sequence, and to look for structural matches as defined by the CATH homologous superfamilies. The datasets can be downloaded from the website as Schema-validated XML files or comma-separated flat files.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".