Research map / Probabilistic and scientific modeling / Molecules and proteins
End-to-End Learning on 3D Protein Structure for Interface Prediction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use end-to-end SASNet trained on DIPS rather than the strongest hand-engineered baseline BIPSPI trained on DB5 | Paired protein interface prediction · DB5-test | CAUROC: 0.878 -> 0.892 | Helped |
| Train Node Average on DIPS instead of DB5-train | Node Average · DB5-test | CAUROC: 0.876 -> 0.712 | Hurt |
| Train BIPSPI on DIPS instead of DB5-train | BIPSPI · DB5-test | CAUROC: 0.878 -> 0.836 | Hurt |
| Train SASNet on DIPS instead of DB5-train | SASNet · DB5-test | CAUROC: 0.876 -> 0.892 | Helped |
| Increase the voxel grid edge size, keeping resolution at 1 Å (ablation) | SASNet · DB5-test | CAUROC | Helped |
| Increase the DIPS training dataset size (ablation) | SASNet · DB5-test | CAUROC | Helped |
| Increase grid edge size beyond 27 Å (ablation) | SASNet · DB5-test | CAUROC | No clear effect |
| Filter DIPS training examples with 20% or greater sequence identity to DB5 (ablation) | SASNet · DB5-test | CAUROC: 0.887 | No clear effect |
| Remove DIPS complexes sharing domain-domain interactions with DB5 (ablation) | SASNet · DB5-test | CAUROC: 0.883 | No clear effect |
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