  Proteins govern most biological functions essential for life, but achieving
controllable protein discovery and optimization remains challenging. Recently,
machine learning-assisted protein editing (MLPE) has shown promise in
accelerating optimization cycles and reducing experimental workloads. However,
current methods struggle with the vast combinatorial space of potential protein
edits and cannot explicitly conduct protein editing using biotext instructions,
limiting their interactivity with human feedback. To fill these gaps, we
propose a novel method called ProtET for efficient CLIP-informed protein
editing through multi-modality learning. Our approach comprises two stages: in
the pretraining stage, contrastive learning aligns protein-biotext
representations encoded by two large language models (LLMs), respectively.
Subsequently, during the protein editing stage, the fused features from editing
instruction texts and original protein sequences serve as the final editing
condition for generating target protein sequences. Comprehensive experiments
demonstrated the superiority of ProtET in editing proteins to enhance
human-expected functionality across multiple attribute domains, including
enzyme catalytic activity, protein stability and antibody specific binding
ability. And ProtET improves the state-of-the-art results by a large margin,
leading to significant stability improvements of 16.67% and 16.90%. This
capability positions ProtET to advance real-world artificial protein editing,
potentially addressing unmet academic, industrial, and clinical needs.
