1/6 One of the key features of functional proteins is their inherent structural flexibility. In our recent work at @icmlconf, we introduce flexibility to protein structure design. Our model FliPS generates protein structures with custom per-residue flexibilities as validated by

@icmlconf 6/6 Amazing team work with great co-authors @LeifSeute, Nicolas Wolf, Simon Wagner, @arneelof, @JanStuehmer and @graeter_mbm. Try out FliPS and BackFlip yourself, code and Google Colab tutorials are available on GitHub!.

@icmlconf 5/6 We introduce a framework in which we generate candidate protein structures conditioned on flexibility with FliPS and use BackFlip to select the candidates whose predicted flexibility profile best matches the target before running expensive MD simulations.

@icmlconf 4/6 We also introduce BackFlip - an equivariant graph neural network that can accurately predict backbone flexibility as derived from MD simulations in less than a second per protein. Crucially, BackFlip infers flexibility solely from the backbone geometry without requiring

@icmlconf 3/6 In a series of experiments, we demonstrate that FliPS samples novel, realistic proteins with diverse secondary structure composition and a remarkable resemblance to custom target flexibility profiles, as verified in 300ns Molecular Dynamics (MD) simulations of the designed

@icmlconf 2/6 FliPS is a conditional flow matching model for protein structure generation. The model receives a flexibility profile as conditional input feature and learns how to generate realistic protein structures while respecting target flexibilities.

finally!! did the alnTM-score mistake myself. .

RT @TheYotg: The Geometric Deep Learning textbook: Chapter 5 - Graphs. In multiple branches of science, from sociology to particle physics,….

ICML travel grants for PhD students in the US whose funding was cut: .

I finally won a lottery and got the CA visa?! Something must be wrong. Guess that means me + ICML 2025!

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Mounted a co-author @LeifSeute 😎

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