高技术通讯2025，Vol.35Issue(12)：1325-1336,12.DOI:10.3772/j.issn.1002-0470.2025.12.006
基于图神经网络的抗体蛋白质结构优化方法
An optimization method for antibody protein structure based on graph neural network
摘要
Abstract
Antibodies play a crucial role in immune response,disease defense,and other aspects.At present,predicting the complementary determining regions of antibodies using antibody protein structure prediction methods remains a challenge.This article proposes an optimization model of immune body structure based on graph neural network(GraphIR).Given the initial structure of the antibody,the complementary decision region sequence representation and other sequence features as well as structural features of the initial structure are obtained through antibody pre-training language models.Then,an equivariant graph neural network is designed to optimize the complementary de-termining region structure of antibodies.The experimental results show that on 46 antibody benchmark test sets,the average root mean square deviation(RMSD)of the CDR H3 region predicted by GraphIR is 1.37 Å,and the pre-diction accuracy is improved by 7.45％,6.11％,7.65％,and 1.27％compared to the benchmark methods ABodyBuilder,RepertoireBuilder,RosettaAntibody,and DeepAb,respectively.关键词
抗体/抗体结构优化/图神经网络/预训练语言模型Key words
antibody/optimization of immune body structure/graph neural network/pre-training language model引用本文复制引用
柴瑞峰,夏瑜豪,崔新月,王苏慧,张贵军..基于图神经网络的抗体蛋白质结构优化方法[J].高技术通讯,2025,35(12):1325-1336,12.基金项目
浙江省自然科学基金(LZ20F030002)资助项目. （LZ20F030002）
