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Poster
SE(3) diffusion model with application to protein backbone generation
Jason Yim · Brian Trippe · Valentin De Bortoli · Emile Mathieu · Arnaud Doucet · Regina Barzilay · Tommi Jaakkola

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #224
in Poster Session 1 »

The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over rigid bodies in 3D (referred to as frames) has shown success in generating novel, functional protein backbones that have not been observed in nature. However, there exists no principled methodological framework for diffusion on SE(3), the space of orientation preserving rigid motions in R3, that operates on frames and confers the group invariance. We address these shortcomings by developing theoretical foundations of SE(3) invariant diffusion models on multiple frames followed by a novel framework, FrameDiff, for estimating the SE(3) equivariant score over multiple frames. We apply FrameDiff on monomer backbone generation and find it can generate designable monomers up to 500 amino acids without relying on a pretrained protein structure prediction network that has been integral to previous methods. We find our samples are capable of generalizing beyond any known protein structure.

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Author Information

Jason Yim (Massachusetts Institute of Technology)
Brian Trippe (Columbia University)
Valentin De Bortoli (CNRS, ENS Ulm (projet NORIA))
Emile Mathieu (University of Oxford)
Arnaud Doucet (Oxford University)
Regina Barzilay (MIT CSAIL)
Regina Barzilay

Regina Barzilay is an Israeli-American computer scientist. She is a professor at the Massachusetts Institute of Technology and a faculty lead for artificial intelligence at the MIT Jameel Clinic. Her research interests are in natural language processing and applications of deep learning to chemistry and oncology.

Tommi Jaakkola (MIT)

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