SyNDock：基于可学习群同步的N刚性蛋白质对接
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
AI总结：
SyNDock将多聚体蛋白质对接建模为全局变换学习问题，通过可训练的两步SE(3)群同步算法实现快速精确组装，在准确率和速度上显著超越现有方法。
摘要：
活细胞内各种细胞过程的调控高度依赖于蛋白质复合物，因此需要全面理解其三维结构以阐明潜在机制。虽然神经对接技术在二元蛋白质对接中已展现出有前景的结果，但将先进神经架构应用于多聚体蛋白质对接仍存在不确定性。本研究提出SyNDock，一个自动化框架，可在数秒内快速组装精确的多聚体复合物，其性能有望超越或媲美近期先进方法。SyNDock具有若干以往方法不具备的优势。首先，SyNDock将多聚体蛋白质对接形式化为学习全局变换的问题，以整体刻画复合物中各链单元的放置方式，从而实现以学习为核心的解决方案。其次，SyNDock提出了一种可训练的两步SE(3)算法，包括初始成对变换与置信度估计，以及随后的全局变换同步。这使得能够以全局一致的方式有效学习组装复合物。最后，在我们提出的基准数据集上进行的大量实验表明，SyNDock在准确率和运行时间等关键性能指标上优于现有对接软件。例如，其性能提升4.5%，速度实现了百万倍的显著加速。
英文摘要：
The regulation of various cellular processes heavily relies on the protein complexes within a living cell, necessitating a comprehensive understanding of their three-dimensional structures to elucidate the underlying mechanisms. While neural docking techniques have exhibited promising outcomes in binary protein docking, the application of advanced neural architectures to multimeric protein docking remains uncertain. This study introduces SyNDock, an automated framework that swiftly assembles precise multimeric complexes within seconds, showcasing performance that can potentially surpass or be on par with recent advanced approaches. SyNDock possesses several appealing advantages not present in previous approaches. Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution. Secondly, SyNDock proposes a trainable two-step SE(3) algorithm, involving initial pairwise transformation and confidence estimation, followed by global transformation synchronization. This enables effective learning for assembling the complex in a globally consistent manner. Lastly, extensive experiments conducted on our proposed benchmark dataset demonstrate that SyNDock outperforms existing docking software in crucial performance metrics, including accuracy and runtime. For instance, it achieves a 4.5% improvement in performance and a remarkable millionfold acceleration in speed.
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。
