Summary of Rigid Protein-protein Docking Via Equivariant Elliptic-paraboloid Interface Prediction, by Ziyang Yu et al.
Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction
by Ziyang Yu, Wenbing Huang, Yang Liu
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, ElliDock, is a novel learning-based approach to protein-protein docking that leverages elliptic paraboloid interfaces to predict docking configurations. By independently equivariant with respect to arbitrary rotations/translations of the proteins, ElliDock ensures generalization and achieves fast inference times while being competitive with state-of-the-art models like DiffDock-PP and Multimer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ElliDock is a new way to find how two proteins fit together. It uses special shapes called elliptic paraboloids to help it predict where the proteins match up. This method is good because it’s fast and works well, even when the proteins are in different positions or orientations. It’s similar to other methods like DiffDock-PP and Multimer, but ElliDock does things a bit differently. |
Keywords
* Artificial intelligence * Generalization * Inference
