用于蛋白质复合物建模的深度强化学习
Deep Reinforcement Learning for Modelling Protein Complexes
AI总结：
本文提出强化学习方法 GAPN，将多链蛋白质复合物组装建模为无环无向连通图搜索，并利用领域奖励、对抗损失与策略梯度提升预测准确率、效率及跨尺度泛化能力。
摘要：
AlphaFold 既可用于单链蛋白质结构预测，也可用于多链蛋白质结构预测，但随着链数量增加，后者会变得极具挑战性。在本研究中，我们将每条链视为一个节点、将组装动作视为边，证明可使用一个无环无向连通图来预测多链蛋白质复合物的结构（又称蛋白质复合物建模，PCM）。然而，仍存在两个挑战：1）PCM 问题具有 $N^{N-2}$（$N$ 为链的数量）的巨大组合优化空间，容易导致高昂的计算成本。2）由于链数量存在差异，蛋白质复合物的尺度表现出分布偏移，这要求在建模不同尺度的复合物时具备泛化能力。为应对这些挑战，我们提出 GAPN，这是一种由领域特定奖励和通过策略梯度实现的对抗损失驱动的生成对抗策略网络，用于自动 PCM 预测。具体而言，GAPN 学习在庞大的组装空间中高效搜索，并通过策略梯度优化直接对接奖励。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 将同时关注特定一批复合物，以及从具有不同链数量的复合物中学到的全局组装规则。实验表明，与主流 PCM 软件相比，我们在准确率（以 RMSD 和 TM-Score 衡量）和效率上均取得了显著提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
