DockGame: Cooperative Games for Multimeric Rigid Protein Docking
- URL: http://arxiv.org/abs/2310.06177v1
- Date: Mon, 9 Oct 2023 22:02:05 GMT
- Title: DockGame: Cooperative Games for Multimeric Rigid Protein Docking
- Authors: Vignesh Ram Somnath, Pier Giuseppe Sessa, Maria Rodriguez Martinez,
Andreas Krause
- Abstract summary: We introduce DockGame, a novel game-theoretic framework for docking.
We view protein docking as a cooperative game between proteins, where the final assembly structure(s) constitute stable equilibria.
On the Docking Benchmark 5.5 dataset, DockGame has much faster runtimes than traditional docking methods.
- Score: 45.970633276976045
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Protein interactions and assembly formation are fundamental to most
biological processes. Predicting the assembly structure from constituent
proteins -- referred to as the protein docking task -- is thus a crucial step
in protein design applications. Most traditional and deep learning methods for
docking have focused mainly on binary docking, following either a search-based,
regression-based, or generative modeling paradigm. In this paper, we focus on
the less-studied multimeric (i.e., two or more proteins) docking problem. We
introduce DockGame, a novel game-theoretic framework for docking -- we view
protein docking as a cooperative game between proteins, where the final
assembly structure(s) constitute stable equilibria w.r.t. the underlying game
potential. Since we do not have access to the true potential, we consider two
approaches - i) learning a surrogate game potential guided by physics-based
energy functions and computing equilibria by simultaneous gradient updates, and
ii) sampling from the Gibbs distribution of the true potential by learning a
diffusion generative model over the action spaces (rotations and translations)
of all proteins. Empirically, on the Docking Benchmark 5.5 (DB5.5) dataset,
DockGame has much faster runtimes than traditional docking methods, can
generate multiple plausible assembly structures, and achieves comparable
performance to existing binary docking baselines, despite solving the harder
task of coordinating multiple protein chains.
Related papers
- Deep Learning for Protein-Ligand Docking: Are We There Yet? [6.138222365802935]
PoseBench is the first comprehensive benchmark for broadly applicable protein-ligand docking.
It enables researchers to rigorously and systematically evaluate DL docking methods for apo-to-holo protein-ligand docking and protein-ligand structure generation.
arXiv Detail & Related papers (2024-05-23T02:27:39Z) - Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion
Bridge [69.80471117520719]
Re-Dock is a novel diffusion bridge generative model extended to geometric manifold.
We propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations.
Experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
arXiv Detail & Related papers (2024-02-18T05:04:50Z) - Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid
Interface Prediction [19.73508673791042]
The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering.
We propose a novel learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface.
By its design, ElliDock is independently equivariant with respect to arbitrary rotations/translations of the proteins.
arXiv Detail & Related papers (2024-01-17T05:39:03Z) - Multi-scale Iterative Refinement towards Robust and Versatile Molecular
Docking [17.28573902701018]
Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets.
We introduce DeltaDock, a robust and versatile framework designed for efficient molecular docking.
arXiv Detail & Related papers (2023-11-30T14:09:20Z) - A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
We propose a latent diffusion model that can reduce the complexity of protein modeling.
We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
arXiv Detail & Related papers (2023-05-06T19:10:19Z) - DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models [47.73386438748902]
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.
arXiv Detail & Related papers (2023-04-08T02:10:44Z) - Independent SE(3)-Equivariant Models for End-to-End Rigid Protein
Docking [57.2037357017652]
We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures.
We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position.
Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment.
arXiv Detail & Related papers (2021-11-15T18:46:37Z) - BERTology Meets Biology: Interpreting Attention in Protein Language
Models [124.8966298974842]
We demonstrate methods for analyzing protein Transformer models through the lens of attention.
We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure.
We also present a three-dimensional visualization of the interaction between attention and protein structure.
arXiv Detail & Related papers (2020-06-26T21:50:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.