Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders

Allan Dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha p Ponnapati, Tess Smidt, JOSEPH JACOBSON

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Keywords: Protein Structure Generative Models, Hierarchical Protein Latent Models, SO(3)-Equivariant Neural Networks, Physics-inspired Machine Learning

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TL;DR: Protein Structural Autoencoders are Scalable Unsupervised Learners

Abstract: Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Yet, traditional graph-based modeling of protein structures is often limited to operate within a single fine-grained resolution, and lacks hourglass neural architectures to learn those high-level building blocks. We narrow this gap by introducing Ophiuchus, an SO(3)-equivariant coarse-graining model that efficiently operates on all heavy atoms of standard protein residues, while respecting their relevant symmetries. Our model departs from current approaches that employ graph modeling, instead focusing on local convolutional coarsening to model sequence-motif interactions in log-linear length complexity. We train Ophiuchus on contiguous fragments of PDB monomers, investigating its reconstruction capabilities across different compression rates. We examine the learned latent space and demonstrate its prompt usage in conformational interpolation, comparing interpolated trajectories to structure snapshots from the PDBFlex dataset. Finally, we leverage denoising diffusion probabilistic models (DDPM) to efficiently sample readily-decodable latent embeddings of diverse miniproteins. Our experiments demonstrate Ophiuchus to be a scalable basis for efficient protein modeling and generation.

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Submission Number: 3661