A Multi-Modal Pre-training Framework-Driven Active Learning System for Enhanced Protein Evolution of Cs CE

Abstract Protein language models have shown strong potential in modeling fitness landscapes for directed evolution; however, their predictive accuracy and generalization to unexplored sequence space remain limited under few-shot learning conditions. Here, we present MmALS (Multi-modal Active Learning System), a few-shot active learning framework that incorporates cold-start region scanning and multi-objective optimization to enable unbiased mutation-site selection. Its learning module employs a multimodal fusion architecture that incorporates sequence, structural, and multiple sequence alignment information, enabling accurate variant fitness prediction under low-sample conditions. The robustness of the MmALS logical framework was validated through in silico benchmarking across 12 independent datasets, in which it consistently accelerated convergence and demonstrated transferability across diverse enzyme fitness landscapes. Using Caldicellulosiruptor saccharolyticus Cellobiose 2-epimerase (CsCE) as the experimental validation, MmALS achieved a 3.88-fold enhancement of isomerization activity after only three iterative rounds (approximately 50 mutants per round), reaching a record-high activity of 20.72 U/mg. Overall, MmALS represents a data-efficient active learning framework for protein evolution, advancing computational enzyme design by enabling accurate optimization under limited experimental sampling.