DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models

• URL: http://arxiv.org/abs/2304.03889v1
• Date: Sat, 8 Apr 2023 02:10:44 GMT
• Title: DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
• Authors: Mohamed Amine Ketata, Cedrik Laue, Ruslan Mammadov, Hannes St\"ark, Menghua Wu, Gabriele Corso, C\'eline Marquet, Regina Barzilay, Tommi S. Jaakkola
• Abstract summary: DiffDock-PP is a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations. We achieve state-of-the-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines.
• Score: 47.73386438748902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem with significant performance boosts over both traditional and deep learning baselines. In this work, we propose a similar approach for rigid protein-protein docking: DiffDock-PP is a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations. We achieve state-of-the-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines. Additionally, DiffDock-PP is faster than all search-based methods and generates reliable confidence estimates for its predictions. Our code is publicly available at $\texttt{https://github.com/ketatam/DiffDock-PP}$

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