Automatic annotation of BIND molecular interactions from three-dimensional structures
Bibliographic record
Abstract
Software to automate the process of extracting molecular interactions from three-dimensional (3D) structures has been developed that records these as Biomolecular Interaction Network Database (BIND) pairwise interaction records. Full annotation of BIND records is provided through a database processing tool called MMDBind, including detailed atom-atom and residue-residue level interaction information. BIND three-dimensional interaction annotation is synthesized by combining information from the Molecular Modeling Database (MMDB), and the HET (heterogen) group dictionary of small molecules in the macromolecular Crystallographic Information Format (mmCIF). Interactions are validated using the Protein Quaternary Structure (PQS) system. A total of 18,166 interactions were removed as being redundant or biologically irrelevant after PQS validation. This first pass MMDBind annotation creates two new divisions of BIND, 3D Biopolymers (BIND-3DBP) comprising 16,737 initial interaction records, and 3D Small Molecules (BIND-3DSM) comprising 48,219 records. Visualization of interacting residues and nucleotides within a macromolecular structure is possible directly from the BIND database owing to added 3D feature annotation within the BIND records that can be conveniently seen using Cn3D ("see-in-3D") after query from the BIND Data Manager. These interaction records provide a further demonstration of the completeness of the BIND data specification and its capabilities as storage and exchange format for all kinds of molecular interactions, including RNA, DNA, protein, and small molecules. Data from the 3DBP and 3DSM sets are available for downloading in Abstract Syntax Notation.1 (ASN.1) or Extensible Markup Language (XML) formats at ftp://ftp.bind.ca/DB/MMDBBind. Data from the 3DBP set is available for interactive query from the BIND Data Manager at www.bind.ca.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".