做抗体设计或蛋白质工程的团队，EvoStruct解决了GNN方法词汇坍塌的痛点，序列恢复和多样性双双提升，值得直接参考方法或复现实验。
现有基于等变图神经网络（GNN）的抗体CDR设计方法虽然序列恢复率高，但存在严重的词汇坍塌问题，即过度预测少数氨基酸（如酪氨酸和甘氨酸），忽略功能重要的残基。EvoStruct通过交叉注意力适配器将冻结的蛋白质语言模型（PLM）与E(3)-等变GNN的3D结构上下文结合，并采用渐进式PLM解冻和R-Drop一致性正则化，专门解决CDR设计的词汇坍塌问题。在CHIMERA-Bench数据集上，EvoStruct相比最佳GNN基线，序列恢复率提升16%，困惑度降低43%，氨基酸多样性恢复提升2.3倍，且与真实结合对相关性最高。该方法为抗体设计提供了更准确、更多样化的序列生成能力。
EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
Equivariant graph neural network (GNN) methods for antibody complementarity-determining region (CDR) design achieve the highest sequence recovery but suffer from severe vocabulary collapse. The current best GNN methods over-predict very few amino acids, such as tyrosine and glycine, while ignoring functionally important residues. We trace this failure to GNN encoders learning amino acid distributions de novo from limited structural data, discarding substitution patterns encoded in evolutionary databases. To resolve this, we propose EvoStruct, which bridges a frozen protein language model (PLM) with 3D structural context from an E(3)-equivariant GNN via a cross-attention adapter. Unlike prior PLM-structure adapters for general protein design, EvoStruct targets the vocabulary collapse problem specific to CDR design through progressive PLM unfreezing and R-Drop consistency regularization. On the CHIMERA-Bench dataset, EvoStruct achieves the highest amino acid recovery and lowest perplexity among several antibody design methods, improving sequence recovery by 16% and reducing perplexity by 43% relative to the best GNN baselines, while recovering 2.3x greater amino acid diversity and the highest binding-pair correlation with ground truth.