SE(3) diffusion model with application to protein backbone generation
SE(3) diffusion model with application to protein backbone generation
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DOI：
10.48550/arxiv.2302.02277
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发表时间：
2023-02
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影响因子：
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通讯作者：
Jason Yim;Brian L. Trippe;Valentin De Bortoli;Emile Mathieu;A. Doucet;R. Barzilay;T. Jaakkola
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文献类型：
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作者：
Jason Yim;Brian L. Trippe;Valentin De Bortoli;Emile Mathieu;A. Doucet;R. Barzilay;T. Jaakkola
设计新的蛋白质结构仍然是蛋白质工程在生物医学和化学应用中的一个挑战。在这方面的工作中，3D刚体（称为帧）上的扩散模型已经成功地产生了在自然界中尚未观察到的新型功能蛋白质骨架。然而，对于SE（3）上的扩散，不存在原则性的方法框架，SE（3）是R3中保持方向的刚性运动的空间，它作用于框架并赋予群不变性。我们通过开发多帧SE（3）不变扩散模型的理论基础来解决这些缺点，然后是一个新的框架，FrameDiff，用于学习多帧SE（3）等变分数。我们将FrameDiff应用于单体骨架生成，发现它可以生成多达500个氨基酸的可设计单体，而不依赖于预先训练的蛋白质结构预测网络，该网络是以前方法的组成部分。我们发现我们的样本能够概括任何已知的蛋白质结构。
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.