Multi-Modal CLIP-Informed Protein Editing

TL;DR

ProtET is a novel multi-modal learning method that uses CLIP-inspired contrastive learning to enable controllable, interactive protein editing guided by biotext instructions, significantly improving stability and functionality.

Contribution

The paper introduces ProtET, a two-stage CLIP-informed approach that aligns protein and biotext representations for effective, instruction-guided protein editing, addressing limitations of prior methods.

Findings

ProtET outperforms state-of-the-art methods in protein editing tasks.

ProtET achieves over 16% improvements in stability metrics.

The method effectively enhances enzyme activity, stability, and antibody binding.

Abstract

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.