Protein as a Second Language for LLMs

Xinhui Chen, Zuchao Li, Mengqi Gao, Yufeng Zhang, Jiaqi Chen, Chak Tou Leong, Haoyang Li

Keywords: Large language models; Protein–QA dataset; Context-Driven Learning; Zero-shot learning

TL;DR: We propose a protein–language framework and bilingual dataset that enable LLMs to reason about protein function via context-driven learning without fine-tuning.

Abstract: 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 without any parameter updates. 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-4o, achieving up to 15% ROUGE-L improvement (average +6.14%) 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.

Primary Area: datasets and benchmarks

Submission Number: 5183