多模态CLIP指导的蛋白质编辑
Multi-Modal CLIP-Informed Protein Editing
AI总结：
针对现有方法难以利用生物文本指令进行蛋白质编辑的问题，提出多模态方法ProtET，通过对比学习对齐蛋白质与文本表示并融合特征作为编辑条件，在酶活性、稳定性等多个属性域大幅提升编辑效果。
摘要：
蛋白质掌控着生命必需的大多数生物学功能，但实现可控的蛋白质发现与优化仍具挑战。近期，机器学习辅助的蛋白质编辑（MLPE）在加速优化周期和减少实验工作量方面展现出潜力。然而，现有方法难以应对潜在蛋白质编辑的庞大组合空间，且无法利用生物文本指令显式进行蛋白质编辑，限制了其与人类反馈的交互性。为填补这些空白，我们提出一种名为ProtET的新方法，通过多模态学习实现高效的CLIP指导蛋白质编辑。该方法包含两个阶段：在预训练阶段，对比学习分别对齐由两个大型语言模型（LLM）编码的蛋白质与生物文本表示；随后在蛋白质编辑阶段，来自编辑指令文本和原始蛋白质序列的融合特征作为生成目标蛋白质序列的最终编辑条件。综合实验证明了ProtET在跨多个属性域编辑蛋白质以增强人类预期功能方面的优越性，这些属性域包括酶催化活性、蛋白质稳定性和抗体特异结合能力。并且ProtET大幅提升了当前最优结果，带来了16.67%和16.90%的显著稳定性提升。这一能力使ProtET能够推进现实世界的人工蛋白质编辑，有望满足学术界、工业界和临床界未竟的需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
