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Three-dimensional native states of natural proteins display recurring andhierarchical patterns. Yet, traditional graph-based modeling of proteinstructures is often limited to operate within a single fine-grained resolution,and lacks hourglass neural architectures to learn those high-level buildingblocks. We narrow this gap by introducing Ophiuchus, an SO(3)-equivariantcoarse-graining model that efficiently operates on all-atom protein structures.Our model departs from current approaches that employ graph modeling, insteadfocusing on local convolutional coarsening to model sequence-motif interactionswith efficient time complexity in protein length. We measure the reconstructioncapabilities of Ophiuchus across different compression rates, and compare it toexisting models. We examine the learned latent space and demonstrate itsutility through conformational interpolation. Finally, we leverage denoisingdiffusion probabilistic models (DDPM) in the latent space to efficiently sampleprotein structures. Our experiments demonstrate Ophiuchus to be a scalablebasis for efficient protein modeling and generation.

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