  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-atom protein structures.
Our model departs from current approaches that employ graph modeling, instead
focusing on local convolutional coarsening to model sequence-motif interactions
with efficient time complexity in protein length. We measure the reconstruction
capabilities of Ophiuchus across different compression rates, and compare it to
existing models. We examine the learned latent space and demonstrate its
utility through conformational interpolation. Finally, we leverage denoising
diffusion probabilistic models (DDPM) in the latent space to efficiently sample
protein structures. Our experiments demonstrate Ophiuchus to be a scalable
basis for efficient protein modeling and generation.
