Protein as a Second Language for LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909841358848000 |
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| author | Chen, Xinhui Li, Zuchao Gao, Mengqi Zhang, Yufeng Leong, Chak Tou Li, Haoyang Chen, Jiaqi |
| author_facet | Chen, Xinhui Li, Zuchao Gao, Mengqi Zhang, Yufeng Leong, Chak Tou Li, Haoyang Chen, Jiaqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Protein as a Second Language for LLMs Chen, Xinhui Li, Zuchao Gao, Mengqi Zhang, Yufeng Leong, Chak Tou Li, Haoyang Chen, Jiaqi Machine Learning Artificial Intelligence Biomolecules 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. |
| title | Protein as a Second Language for LLMs |
| topic | Machine Learning Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2510.11188 |