AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this work, by taking each chain as a node and assembly actions as edges, we show that an acyclic undirected connected graph can be used to predict the structure of multi-chain protein complexes (a.k.a., protein complex modelling, PCM). However, there are still two challenges: 1) The huge combinatorial optimization space of $N^{N-2}$ ($N$ is the number of chains) for the PCM problem can easily lead to high computational cost. 2) The scales of protein complexes exhibit distribution shift due to variance in chain numbers, which calls for the generalization in modelling complexes of various scales. To address these challenges, we propose GAPN, a Generative Adversarial Policy Network powered by domain-specific rewards and adversarial loss through policy gradient for automatic PCM prediction. Specifically, GAPN learns to efficiently search through the immense assembly space and optimize the direct docking reward through policy gradient. Importantly, we design an adversarial reward function to enhance the receptive field of our model. In this way, GAPN will simultaneously focus on a specific batch of complexes and the global assembly rules learned from complexes with varied chain numbers. Empirically, we have achieved both significant accuracy (measured by RMSD and TM-Score) and efficiency improvements compared to leading PCM softwares.

翻译：AlphaFold可用于单链和多链蛋白质结构预测，但随着链数增加，后者变得极具挑战性。本研究通过将每条链视为节点、组装动作视为边，证明无环无向连通图可用于预测多链蛋白质复合物的结构（即蛋白质复合物建模，PCM）。然而，仍存在两个挑战：1）PCM问题中$N^{N-2}$（$N$为链数）的巨大组合优化空间易导致高计算成本；2）蛋白质复合物的规模因链数差异呈现分布偏移，这要求模型具备多尺度复合物建模的泛化能力。为解决这些问题，我们提出GAPN——一种基于策略梯度的生成对抗策略网络，通过领域特定奖励和对抗损失实现PCM自动预测。具体而言，GAPN学习高效搜索庞大组装空间，并通过策略梯度优化直接对接奖励。更重要的是，我们设计了对抗奖励函数以增强模型的感受野。通过这种方式，GAPN将同时关注特定批次的复合物以及从不同链数复合物中习得的全局组装规则。实验表明，相比主流PCM软件，我们在精度（以RMSD和TM-Score衡量）和效率方面均取得了显著提升。