Improved motif-scaffolding with SE(3) flow matching
Jason Yim Andrew Campbell Emile Mathieu Andrew Y. K. Foong Michael Gastegger José Jiménez-Luna Sarah Lewis Victor Garcia Satorras Bastiaan S. Veeling Frank Noé Regina Barzilay Tommi S. Jaakkola
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: https://github.com/
microsoft/frame-flow.