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arXiv · 2304.02198

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Bowen Jing·Ezra Erives·Peter Pao-Huang·Gabriele Corso·Bonnie Berger·Tommi Jaakkola
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Abstract

Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold.

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Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi Jaakkola. 2023-04-05. EigenFold: Generative Protein Structure Prediction with Diffusion Models. https://arxiv.org/abs/2304.02198

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