用几何代数提升蛋白质骨架生成设计性与多样性
Generating Highly Designable Proteins with Geometric Algebra Flow Matching
- 基于几何代数构建残基间高阶几何信息传递机制
- 生成骨架符合天然蛋白质二级结构分布，设计性与新颖性俱佳
- 适合蛋白质设计与生成模型研究者参考
我们提出一种用于蛋白质骨架设计的生成模型，利用几何积和高阶消息传递。具体地，引入Clifford Frame Attention（CFA），扩展AlphaFold2中的不变点注意力（IPA）架构，将骨架残基帧与几何特征在射影几何代数中表示，从而通过代数双线性运算构造几何表达力强的消息传递，包含高阶项。我们将该架构融入当前最先进的流匹配模型FrameFlow，评估结果表明：所提模型在设计性、多样性和新颖性方面表现优异，同时生成的蛋白质骨架能良好遵循天然蛋白质中二级结构元素的统计分布，这一特性此前许多先进生成模型未能充分实现。
原文摘要 · Abstract (English)
We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric features are represented in the projective geometric algebra. This enables to construct geometrically expressive messages between residues, including higher order terms, using the bilinear operations of the algebra. We evaluate our architecture by incorporating it into the framework of FrameFlow, a state-of-the-art flow matching model for protein backbone generation. The proposed model achieves high designability, diversity and novelty, while also sampling protein backbones that follow the statistical distribution of secondary structure elements found in naturally occurring proteins, a property so far only insufficiently achieved by many state-of-the-art generative models.
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