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.

蛋白质相互作用、组装形成、蛋白质对接、多聚蛋白对接和博弈论框架是本研究的主要关键词和研究领域，通过 DockGame 方法可以提高蛋白质对接问题的效率和生成多种可能的组装结构。

DockGame: 刚性蛋白多聚体对接的合作博弈

DockGame: Cooperative Games for Multimeric Rigid Protein Docking

Structural information about protein-protein interactions, often missing at
the interactome scale, is important for mechanistic understanding of cells and
rational discovery of therapeutics. Protein docking provides a computational
alternative to predict such information. However, ranking near-native docked
models high among a large number of candidates, often known as the scoring
problem, remains a critical challenge. Moreover, estimating model quality, also
known as the quality assessment problem, is rarely addressed in protein
docking.
In this study the two challenging problems in protein docking are regarded as
relative and absolute scoring, respectively, and addressed in one
physics-inspired deep learning framework. We represent proteins' and encounter
complexes' 3D structures as intra- and inter-molecular residue contact graphs
with atom-resolution node and edge features. And we propose a novel graph
convolutional kernel that pool interacting nodes' features through edge
features so that generalized interaction energies can be learned directly from
graph data. The resulting energy-based graph convolutional networks (EGCN) with
multi-head attention are trained to predict intra- and inter-molecular
energies, binding affinities, and quality measures (interface RMSD) for
encounter complexes. Compared to a state-of-the-art scoring function for model
ranking, EGCN has significantly improved ranking for a CAPRI test set involving
homology docking; and is comparable for Score_set, a CAPRI benchmark set
generated by diverse community-wide docking protocols not known to training
data. For Score_set quality assessment, EGCN shows about 27% improvement to our
previous efforts. Directly learning from 3D structure data in graph
representation, EGCN represents the first successful development of graph
convolutional networks for protein docking.

本篇研究使用基于图卷积网络的深度学习模型，将蛋白质的三维结构表示为节点和边特征的联系图，并成功地将其应用于蛋白质对接领域中的评分和质量评估问题，与现有方法相比，获得了显著的改进。