面向刚性的几何预训练用于蛋白质设计与构象集合
Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles
AI总结：
RigidSSL通过刚性感知的几何预训练，提升蛋白质设计和构象集合的生成能力。
摘要：
生成模型最近通过学习自然结构的统计规律，推动了从头蛋白质设计的发展。然而，当前方法面临三个关键限制：（1）现有方法无法联合学习蛋白质几何和设计任务，其中预训练可以作为解决方案；（2）当前预训练方法大多依赖局部、非刚性的原子表示进行下游任务的属性预测，限制了蛋白质生成任务中的全局几何理解；（3）现有方法尚未有效建模蛋白质结构的丰富动态和构象信息。为克服这些问题，我们引入了RigidSSL（刚性感知自监督学习），一种几何预训练框架，其在生成微调之前优先学习几何特性。第一阶段（RigidSSL-Perturb）从AlphaFold蛋白质结构数据库的432,000个结构中学习几何先验，通过模拟扰动进行学习。第二阶段（RigidSSL-MD）在1,300个分子动力学轨迹上进一步细化这些表示，以捕捉物理上真实的过渡。在两个阶段背后是一种双向、刚性感知的流匹配目标，联合优化平移和旋转动力学，以最大化构象之间的互信息。实验表明，RigidSSL变体在设计性方面提高了43%，同时增强了无条件生成的原创性和多样性。此外，RigidSSL-Perturb在零样本模体支架任务中的成功率提高了5.8%，而RigidSSL-MD在G蛋白偶联受体建模中捕捉到了更具生物物理真实性的构象集合。
英文摘要：
Generative models have recently advanced $\textit{de novo}$ protein design by learning the statistical regularities of natural structures. However, current approaches face three key limitations: (1) Existing methods cannot jointly learn protein geometry and design tasks, where pretraining can be a solution; (2) Current pretraining methods mostly rely on local, non-rigid atomic representations for property prediction downstream tasks, limiting global geometric understanding for protein generation tasks; and (3) Existing approaches have yet to effectively model the rich dynamic and conformational information of protein structures. To overcome these issues, we introduce $\textbf{RigidSSL}$ ($\textit{Rigidity-Aware Self-Supervised Learning}$), a geometric pretraining framework that front-loads geometry learning prior to generative finetuning. Phase I (RigidSSL-Perturb) learns geometric priors from 432K structures from the AlphaFold Protein Structure Database with simulated perturbations. Phase II (RigidSSL-MD) refines these representations on 1.3K molecular dynamics trajectories to capture physically realistic transitions. Underpinning both phases is a bi-directional, rigidity-aware flow matching objective that jointly optimizes translational and rotational dynamics to maximize mutual information between conformations. Empirically, RigidSSL variants improve designability by up to 43% while enhancing novelty and diversity in unconditional generation. Furthermore, RigidSSL-Perturb improves the success rate by 5.8% in zero-shot motif scaffolding and RigidSSL-MD captures more biophysically realistic conformational ensembles in G protein-coupled receptor modeling.
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。
