A protein-protein docking decoys set from three different rigid body methods
Bibliographic record
Abstract
This set of decoys was generated for the targets in the Protein-Protein Docking Benchmark 5 (Vreven <em>et al.</em>, 2015), using ZDock (Chen <em>et al.</em>, 2003), FTDock (Gabb <em>et al.</em>, 1997), and HADDOCK (only the FFT phase) (Dominguez <em>et al.</em>, 2003; de Vries <em>et al.</em>, 2007). Included are only those targets for which at least one correct decoy could be generated. Decoys were analyzed with the CAPRI quality standards (Lensink <em>et al.</em>, 2007), based on the comparison to the corresponding target experimental structure. For each decoy, all the needed information is reported in its name, e.g.: <br> <strong>H</strong>_<strong>CP57</strong>_<strong>2211</strong>_<strong>I</strong>.pdb H <- Haddock CP57 <- BM5 id label 2211 <- decoy number I <- CAPRI quality (H)high, (M)medium , (A)acceptable ,(I)Incorrect References: Chen,R. <em>et al.</em> (2003) ZDOCK: an initial-stage protein-docking algorithm. <em>Proteins</em>, <strong>52</strong>, 80–87. Dominguez,C. <em>et al.</em> (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. <em>J Am Chem Soc</em>, <strong>125</strong>, 1731–1737. Gabb,H.A. <em>et al.</em> (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. <em>J Mol Biol</em>, <strong>272</strong>, 106–120. Lensink,M.F. <em>et al.</em> (2007) Docking and scoring protein complexes: CAPRI 3rd Edition. <em>Proteins</em>, <strong>69</strong>, 704–718. Vreven,T. <em>et al.</em> (2015) Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. <em>Journal of Molecular Biology</em>, <strong>427</strong>, 3031–3041. de Vries,S.J. <em>et al.</em> (2007) HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. <em>Proteins</em>, <strong>69</strong>, 726–733.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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; both teacher heads agree on what is shown here.
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".