对比不同模型架构对抗体特征的捕捉能力，发现专用模型更懂抗体关键区域。
Exploring Protein Language Model Architecture-Induced Biases for Antibody Comprehension
- 用注意力分析揭示抗体模型天然聚焦抗原结合区
- 专用模型在抗体特异性预测中表现一致但偏见各异
- 适合抗体设计与药物研发领域的研究人员参考
蛋白质语言模型（PLMs）在理解蛋白序列方面取得显著进展，但不同模型架构对抗体特异性生物学特征的捕捉程度尚不明确。本文系统评估了三种先进PLM（AntiBERTa、BioBERT、ESM2）与通用语言模型（GPT-2）在抗体靶点特异性预测任务上的表现。结果表明，尽管所有模型均达到高分类准确率，但在识别V基因使用、体细胞超突变模式和同种型信息等生物特征时表现出明显偏差。通过注意力归因分析发现，如AntiBERTa等抗体专用模型能自然聚焦于互补决定区（CDRs），而通用蛋白模型则需显式引入以CDR为中心的训练策略才能有效学习。研究揭示了模型架构与生物特征提取之间的关联，为计算抗体设计中的未来PLM开发提供重要指导。
原文摘要 · Abstract (English)
Recent advances in protein language models (PLMs) have demonstrated remarkable capabilities in understanding protein sequences. However, the extent to which different model architectures capture antibody-specific biological properties remains unexplored. In this work, we systematically investigate how architectural choices in PLMs influence their ability to comprehend antibody sequence characteristics and functions. We evaluate three state-of-the-art PLMs-AntiBERTa, BioBERT, and ESM2--against a general-purpose language model (GPT-2) baseline on antibody target specificity prediction tasks. Our results demonstrate that while all PLMs achieve high classification accuracy, they exhibit distinct biases in capturing biological features such as V gene usage, somatic hypermutation patterns, and isotype information. Through attention attribution analysis, we show that antibody-specific models like AntiBERTa naturally learn to focus on complementarity-determining regions (CDRs), while general protein models benefit significantly from explicit CDR-focused training strategies. These findings provide insights into the relationship between model architecture and biological feature extraction, offering valuable guidance for future PLM development in computational antibody design.
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