用稀疏实验数据推断蛋白质动态，精度和速度均超现有方法
PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks
- 基于SE(3)等变图神经网络，结合语言模型迁移学习
- 在蛋白数据银行上实现更优的时间与精度表现，尤其擅长大尺度慢变构象
- 适合研究蛋白质动力学、结构生物学及药物设计的科研人员
蛋白质通过运动和形变来实现其生物功能。尽管蛋白质结构预测取得显著进展，但在生理条件下近似构象集合仍是根本性难题。本文提出新视角，直接从稀疏实验观测中推断蛋白质运动的连续紧凑表示。我们设计了针对数据对称性（包括缩放和置换）的任务特异性损失函数。所提方法PETIMOT（基于蛋白质序列与结构的动力学推断）利用预训练蛋白质语言模型的迁移学习，通过SE(3)-等变图神经网络建模。在蛋白质数据银行上的训练与评估表明，PETIMOT在时间和准确性上均优于当前最优的扩散模型与流匹配方法，以及传统物理模型，尤其能有效捕捉大尺度、慢速的构象变化。代码与协议已开源：https://github.com/PhyloSofS-Team/PETIMOT。
原文摘要 · Abstract (English)
Proteins move and deform to ensure their biological functions. Despite significant progress in protein structure prediction, approximating conformational ensembles at physiological conditions remains a fundamental open problem. This paper presents a novel perspective on the problem by directly targeting continuous compact representations of protein motions inferred from sparse experimental observations. We develop a task-specific loss function enforcing data symmetries, including scaling and permutation operations. Our method PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) leverages transfer learning from pre-trained protein language models through an SE(3)-equivariant graph neural network. When trained and evaluated on the Protein Data Bank, PETIMOT shows superior performance in time and accuracy, capturing protein dynamics, particularly large/slow conformational changes, compared to state-of-the-art diffusion and flow-matching approaches, as well as traditional physics-based models. Our code and protocols are available at https://github.com/PhyloSofS-Team/PETIMOT.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。