Large language models (LLMs) have significantly advanced protein representation learning. However, their capacity to interpret and design antibodies through natural language remains limited. To address this challenge, we present AFD-Instruction, the first large-scale instruction dataset with functional annotations tailored to antibodies. This dataset encompasses two key components: antibody understanding, which infers functional attributes directly from sequences, and antibody design, which enables de novo sequence generation under functional constraints. These components provide explicit sequence-function alignment and support antibody design guided by natural language instructions. Extensive instruction-tuning experiments on general-purpose LLMs demonstrate that AFD-Instruction consistently improves performance across diverse antibody-related tasks. By linking antibody sequences with textual descriptions of function, AFD-Instruction establishes a new foundation for advancing antibody modeling and accelerating therapeutic discovery.

翻译：大语言模型（LLMs）已显著推动了蛋白质表征学习。然而，它们通过自然语言解释和设计抗体的能力仍然有限。为应对这一挑战，我们提出了AFD-Instruction，这是首个针对抗体定制、具有功能注释的大规模指令数据集。该数据集包含两个关键组成部分：抗体理解（直接从序列推断功能属性）和抗体设计（在功能约束下实现从头序列生成）。这些组件提供了明确的序列-功能对齐，并支持通过自然语言指令引导的抗体设计。在通用大语言模型上进行的大量指令微调实验表明，AFD-Instruction能持续提升多种抗体相关任务的性能。通过将抗体序列与功能的文本描述联系起来，AFD-Instruction为推进抗体建模和加速治疗发现奠定了新的基础。