论文标题
部分可观测时空混沌系统的无模型预测
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
论文作者
论文摘要
计算抗体设计旨在自动创建与抗原结合的抗体。结合亲和力受3D结合界面的控制，其中抗体残基（角膜膜）与抗原残基（表位）紧密相互作用。因此，预测3D寄生虫 - 远距离复合物（对接）是找到最佳寄生虫的关键。在本文中，我们提出了一个新模型，称为层状对接和设计的称为层次模棱两可的改进网络（HERN）。在对接过程中，Hern采用层次消息传递网络来预测原子力，并使用它们以迭代，模棱两可的方式来完善结合复合物。在生成期间，其自回归解码器逐渐扩展了寄生虫，并构建了绑定界面的几何表示，以指导下一个残基选择。我们的结果表明，HERN在伞形对接和设计基准测试基准方面的先验最先进。
Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, predicting 3D paratope-epitope complex (docking) is the key to finding the best paratope. In this paper, we propose a new model called Hierarchical Equivariant Refinement Network (HERN) for paratope docking and design. During docking, HERN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding complex in an iterative, equivariant manner. During generation, its autoregressive decoder progressively docks generated paratopes and builds a geometric representation of the binding interface to guide the next residue choice. Our results show that HERN significantly outperforms prior state-of-the-art on paratope docking and design benchmarks.
