ICML 2025

Flexibility-conditioned protein structure design with flow matching
Vsevolod Viliuga * 1 2 3 Leif Seute * 1 3 4 Nicolas Wolf 1 3 4 Simon Wagner 4
Arne Elofsson 2 Jan Stühmer 1 5 Frauke Gräter 3 1 4
Abstract
Recent advances in geometric deep learning and
generative modeling have enabled the design of
novel proteins with a wide range of desired proper-
ties. However, current state-of-the-art approaches
are typically restricted to generating proteins with
only static target properties, such as motifs and
symmetries. In this work, we take a step towards
overcoming this limitation by proposing a frame-
work to condition structure generation on flexibil-
ity, which is crucial for key functionalities such
as catalysis or molecular recognition. We first in-
troduce BackFlip, an equivariant neural network
for predicting per-residue flexibility from an in-
put backbone structure. Relying on BackFlip, we
propose FliPS, an SE(3)-equivariant conditional
flow matching model that solves the inverse prob-
lem, that is, generating backbones that display a
target flexibility profile. In our experiments, we
show that FliPS is able to generate novel and di-
verse protein backbones with the desired flexibil-
ity, verified by Molecular Dynamics (MD) simula-
tions. FliPS and BackFlip are available at https:
//github.com/graeter-group/flips.
1. Introduction
The past few years have arguably marked the golden age
of de novo protein design. Rapid advancement of deep
learning-based approaches and their application to the pro-
tein structure prediction problem have enabled an unprece-
dented level of modeling accuracy (Jumper et al., 2021;
Dauparas et al., 2021; Lin et al., 2022). This, in turn, paved
*Equal contribution 1Heidelberg Institute for Theoretical Stud-
ies, Heidelberg, Germany 2Dept. of Biochemistry and Biophysics
at Stockholm University and Science for Life Laboratory, Stock-
holm, Sweden 3Max Planck Institute for Polymer Research, Mainz,
Germany 4IWR, Heidelberg University, Heidel...