Controllable Protein Sequence Generation with LLM Preference Optimization

Designing proteins with specific attributes offers an important solution to address biomedical challenges. Pre-trained protein large language models (LLMs) have shown promising results on protein sequence generation. However, to control sequence generation for specific attributes, existing work still exhibits poor functionality and structural stability. In this paper, we propose a novel controllable protein design method called CtrlProt. We finetune a protein LLM with a new multi-listwise preference optimization strategy to improve generation quality and support multi-attribute controllable generation. Experiments demonstrate that CtrlProt can meet functionality and structural stability requirements effectively, achieving state-of-the-art performance in both single-attribute and multi-attribute protein sequence generation.

Authors

• Xiangyu Liu State Key Laboratory for Novel Software Technology, Nanjing University, China
• Yi Liu City University of Hong Kong
• Silei Chen Medical School, Nanjing University, China National Institute of Healthcare Data Science, Nanjing University, China
• Wei Hu State Key Laboratory for Novel Software Technology, Nanjing University, China National Institute of Healthcare Data Science, Nanjing University, China

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