通过结构检索实现快速且准确的抗体序列设计
Fast and Accurate Antibody Sequence Design via Structure Retrieval
AI总结：
本文提出Igseek，一种基于结构检索的抗体序列设计框架，通过多通道等变图神经网络生成CDR几何表示并利用结构保守基序，实现快速准确的序列恢复，优于现有方法。
摘要：
蛋白质设计的最新进展利用扩散模型生成结构支架，随后进行称为蛋白质逆折叠的过程，该过程涉及在这些支架上进行序列推断。然而，当这些方法应用于高度可变的抗体互补决定区（CDRs）等结构时，面临重大挑战，因为序列推断经常因幻觉而产生非功能性序列。与主流的蛋白质逆折叠方法不同，本文引入了Igseek，一种新颖的结构检索框架，通过从天然抗体数据库中检索相似结构来推断CDR序列。具体而言，Igseek采用一种简单而有效的多通道等变图神经网络来生成CDR骨架结构的高质量几何表示。随后，它对齐结构相似的CDR序列，并利用结构保守的序列基序来提高推断准确性。我们的实验表明，Igseek不仅在结构检索方面表现出高效率，而且在抗体和T细胞受体的序列恢复方面均优于最先进的方法，为治疗性蛋白质设计提供了基于检索的新视角。
英文摘要：
Recent advancements in protein design have leveraged diffusion models to generate structural scaffolds, followed by a process known as protein inverse folding, which involves sequence inference on these scaffolds. However, these methodologies face significant challenges when applied to hyper-variable structures such as antibody Complementarity-Determining Regions (CDRs), where sequence inference frequently results in non-functional sequences due to hallucinations. Distinguished from prevailing protein inverse folding approaches, this paper introduces Igseek, a novel structure-retrieval framework that infers CDR sequences by retrieving similar structures from a natural antibody database. Specifically, Igseek employs a simple yet effective multi-channel equivariant graph neural network to generate high-quality geometric representations of CDR backbone structures. Subsequently, it aligns sequences of structurally similar CDRs and utilizes structurally conserved sequence motifs to enhance inference accuracy. Our experiments demonstrate that Igseek not only proves to be highly efficient in structural retrieval but also outperforms state-of-the-art approaches in sequence recovery for both antibodies and T-Cell Receptors, offering a new retrieval-based perspective for therapeutic protein design.
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。
