Large Language Model is Secretly a Protein Sequence Optimizer

Yinkai Wang, Jiaxing He, Yuanqi Du, Xiaohui Chen, Jianan Canal Li +3 more
1/16/2025
cs.LGcs.AIq-bio.QM

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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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Cite this paper

@article{wang2025large,
  title  = {Large Language Model is Secretly a Protein Sequence Optimizer},
  author = {Yinkai Wang and Jiaxing He and Yuanqi Du and Xiaohui Chen and Jianan Canal Li and Li-Ping Liu and Xiaolin Xu and Soha Hassoun},
  year   = {2025},
  eprint = {2501.09274},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.09274v2}
}

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