Protein design often begins with knowledge of a desired function from a motif
which motif-scaffolding aims to construct a functional protein around.
Recently, generative models have achieved breakthrough success in designing
scaffolds for a diverse range of motifs. However, the generated scaffolds tend
to lack structural diversity, which can hinder success in wet-lab validation.
In this work, we extend FrameFlow, an SE(3) flow matching model for protein
backbone generation, to perform motif-scaffolding with two complementary
approaches. The first is motif amortization, in which FrameFlow is trained with
the motif as input using a data augmentation strategy. The second is motif
guidance, which performs scaffolding using an estimate of the conditional score
from FrameFlow, and requires no additional training. Both approaches achieve an
equivalent or higher success rate than previous state-of-the-art methods, with
2.5 times more structurally diverse scaffolds. Code: this https URL
microsoft/frame-flow.

蛋白质设计中，基于模体的脚手架技术用于构建功能性蛋白质，但生成的脚手架缺乏结构多样性，限制了湿实验验证的成功。本研究通过两种互补方法，在使用 FrameFlow 进行脚手架设计时实现了较高成功率和更多结构多样性的脚手架生成。