Large Language Model is Secretly a Protein Sequence Optimizer

Yinkai Wang, Jiaxing He, Yuanqi Du, Xiaohui Chen, Jianan Canal Li, Liping Liu, Xiaolin Xu, Soha Hassoun

Track: Full Paper Track

Keywords: Protein optimization, Directed evolution, Large Language Models, Evolutionary Search, AI for Science

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.

Attendance: Soha Hassoun

Submission Number: 1