Boosting In-Silicon Directed Evolution with Fine-Tuned Protein Language Model and Tree Search

Abstract: Protein evolution through amino acid sequence mutations is a cornerstone of life sciences. While current in-silicon directed evolution algorithms focus on designing search strategies, they overlook how to utilize the transformative protein LLMs, which encode rich evolutionary patterns, to guide search. To bridge this gap, we propose AlphaDE, a novel framework to evolve protein sequences by harnessing the innovative paradigms of LLMs. First, AlphaDE fine-tunes pretrained protein LLMs using masked language modeling on homologous protein sequences to activate the evolutionary plausibility for the interested protein class. Second, AlphaDE introduces test-time inference based on Monte Carlo tree search, which effectively evolves proteins with evolutionary guidance from the fine-tuned protein LLM. Extensive benchmark experiments show that AlphaDE remarkably outperforms previous state-of-the-art methods even with few-shot fine-tuning. An interesting case study further shows that AlphaDE supports condensing the protein sequence space through computational evolution.