Large Language Model is Secretly a Protein Sequence Optimizer

• URL: http://arxiv.org/abs/2501.09274v2
• Date: Fri, 17 Jan 2025 15:22:00 GMT
• Title: Large Language Model is Secretly a Protein Sequence Optimizer
• Authors: Yinkai Wang, Jiaxing He, Yuanqi Du, Xiaohui Chen, Jianan Canal Li, Li-Ping Liu, Xiaolin Xu, Soha Hassoun,
• Abstract summary: We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. We demonstrate large language models (LLMs), despite being trained on massive texts, are secretly protein sequences.
• Score: 24.55348363931866
• License:
• Abstract: We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. Directed evolution has been a dominating paradigm in this field which has an iterative process to generate variants and select via experimental feedback. We demonstrate large language models (LLMs), despite being trained on massive texts, are secretly protein sequence optimizers. With a directed evolutionary method, LLM can perform protein engineering through Pareto and experiment-budget constrained optimization, demonstrating success on both synthetic and experimental fitness landscapes.

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