标题： 基于关系感知的等变图网络用于表位未知的抗体设计和特异性优化 显示英文标题

标题： Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization

摘要： 抗体是Y形蛋白质，通过结合特定抗原来保护宿主，其结合主要由抗体中的互补决定区（CDRs）决定。尽管在CDR设计方面取得了重大进展，现有的计算方法仍然面临几个挑战：1）由于上下文信息不足，对具有长序列的复杂CDRs建模能力较差；2）依赖于预先给定的抗原表位及其与目标抗体的静态相互作用；3）在抗体优化过程中忽视了特异性，导致非特异性抗体。在本文中，我们考虑了多种节点特征、边特征和边关系，以包含更多的上下文和几何信息。我们提出了一种新的关系感知抗体设计（RAAD）框架，该框架动态建模抗原-抗体相互作用，用于共同设计抗原特异性CDRs的序列和结构。此外，我们提出了一种新的评估指标，以更好地衡量抗体的特异性，并开发了一种对比特异性增强约束，以优化抗体的特异性。大量实验表明，RAAD在不同CDR类型、序列长度、预训练策略和输入上下文的抗体建模、生成和优化方面表现出卓越的能力。 显示英文摘要

摘要： Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody. Despite the great progress made in CDR design, existing computational methods still encounter several challenges: 1) poor capability of modeling complex CDRs with long sequences due to insufficient contextual information; 2) conditioned on pre-given antigenic epitopes and their static interaction with the target antibody; 3) neglect of specificity during antibody optimization leads to non-specific antibodies. In this paper, we take into account a variety of node features, edge features, and edge relations to include more contextual and geometric information. We propose a novel Relation-Aware Antibody Design (RAAD) framework, which dynamically models antigen-antibody interactions for co-designing the sequences and structures of antigen-specific CDRs. Furthermore, we propose a new evaluation metric to better measure antibody specificity and develop a contrasting specificity-enhancing constraint to optimize the specificity of antibodies. Extensive experiments have demonstrated the superior capability of RAAD in terms of antibody modeling, generation, and optimization across different CDR types, sequence lengths, pre-training strategies, and input contexts.