The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over rigid bodies in 3D (referred to as frames) has shown success in generating novel, functional protein backbones that have not been observed in nature. However, there exists no principled methodological framework for diffusion on SE(3), the space of orientation preserving rigid motions in R3, that operates on frames and confers the group invariance. We address these shortcomings by developing theoretical foundations of SE(3) invariant diffusion models on multiple frames followed by a novel framework, FrameDiff, for learning the SE(3) equivariant score over multiple frames. We apply FrameDiff on monomer backbone generation and find it can generate designable monomers up to 500 amino acids without relying on a pretrained protein structure prediction network that has been integral to previous methods. We find our samples are capable of generalizing beyond any known protein structure.

翻译：新型蛋白质结构的设计仍是生物医学与化学领域蛋白质工程中的一项挑战。在此类研究中，基于三维空间刚体（称为帧）的扩散模型已成功生成了自然界中未曾观察到的新型功能性蛋白质骨架。然而，目前尚缺乏一个在帧上运行且赋予群不变性的SE(3)（R³中保定向刚体运动空间）扩散方法学框架。为解决这些不足，我们首先建立了多帧上SE(3)不变扩散模型的理论基础，随后提出了一个名为FrameDiff的新框架，用于学习多帧上的SE(3)等变分数。我们将FrameDiff应用于单体骨架生成，发现其无需依赖先前方法中不可或缺的预训练蛋白质结构预测网络，即可生成长达500个氨基酸的可设计单体。结果表明，我们的样本能够泛化至超越任何已知蛋白质结构的范围。