让大模型把蛋白质序列当第二语言理解，零样本就能猜出功能
Protein as a Second Language for LLMs
- 把氨基酸序列转成类语言的问答对，用上下文提示让大模型理解
- 在79,926个蛋白问答数据上测试，平均ROUGE-L提升7%，最高达17.2%
- 无需训练就能超越专用蛋白模型，适合想快速理解未知蛋白的人
解析未知蛋白质序列的功能是具有广泛科学意义的基础挑战，但现有方法大多依赖任务特定适配器或大规模有监督微调。我们提出“蛋白质作为第二语言”框架，将氨基酸序列重新表述为大语言模型可通过上下文示例理解的句子形式。该方法在零样本设置下自适应构建序列-问题-答案三元组，揭示功能线索而无需额外训练。为此，我们构建了一个包含79,926个蛋白问答实例的双语语料库，涵盖属性预测、描述性理解与延伸推理。实验表明，该方法在多种开源LLM及GPT-4上均取得一致提升，平均ROUGE-L提高7%，最高达17.2%，甚至超越微调过的专用蛋白语言模型。结果表明，通过蛋白质语言提示引导，通用大模型可优于领域专用模型，为基础模型中的蛋白质理解提供了一条可扩展路径。
原文摘要 · Abstract (English)
Deciphering the function of unseen protein sequences is a fundamental challenge with broad scientific impact, yet most existing methods depend on task-specific adapters or large-scale supervised fine-tuning. We introduce the "Protein-as-Second-Language" framework, which reformulates amino-acid sequences as sentences in a novel symbolic language that large language models can interpret through contextual exemplars. Our approach adaptively constructs sequence-question-answer triples that reveal functional cues in a zero-shot setting, without any further training. To support this process, we curate a bilingual corpus of 79,926 protein-QA instances spanning attribute prediction, descriptive understanding, and extended reasoning. Empirically, our method delivers consistent gains across diverse open-source LLMs and GPT-4, achieving up to 17.2% ROUGE-L improvement (average +7%) and even surpassing fine-tuned protein-specific language models. These results highlight that generic LLMs, when guided with protein-as-language cues, can outperform domain-specialized models, offering a scalable pathway for protein understanding in foundation models.
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