通过编码器-解码器语言模型生成互补链配对序列的抗体设计
Generative Antibody Design for Complementary Chain Pairing Sequences through Encoder-Decoder Language Model
AI总结：
提出编码器-解码器模型pAbT5，从配对伙伴生成互补抗体重链或轻链，结合生物学约束实现生成式抗体设计。
摘要：
当前的蛋白质语言模型（pLMs）主要关注单链蛋白质序列，且通常未考虑蛋白质-蛋白质相互作用对生成设计施加的约束。为填补这一空白，我们提出了配对抗体T5（pAbT5），这是一种编码器-解码器模型，能从配对伙伴生成互补的重链或轻链。我们表明，模型遵循框架区的保守性和高变区的可变性，这一点通过与序列比对和可变长度CDR环的一致性得到了证明。我们还表明，模型通过恢复真实链类型和基因家族捕捉了链配对偏好。结果展示了pAbT5在生成抗体设计中的潜力，融合了链配对偏好的生物学约束。
英文摘要：
Current protein language models (pLMs) predominantly focus on single-chain protein sequences and often have not accounted for constraints on generative design imposed by protein-protein interactions. To address this gap, we present paired Antibody T5 (pAbT5), an encoder-decoder model to generate complementary heavy or light chain from its pairing partner. We show that our model respects conservation in framework regions and variability in hypervariable domains, demonstrated by agreement with sequence alignment and variable-length CDR loops. We also show that our model captures chain pairing preferences through the recovery of ground-truth chain type and gene families. Our results showcase the potential of pAbT5 in generative antibody design, incorporating biological constraints from chain pairing preferences.
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。
