  Proteins are macromolecules that mediate a significant fraction of the
cellular processes that underlie life. An important task in bioengineering is
designing proteins with specific 3D structures and chemical properties which
enable targeted functions. To this end, we introduce a generative model of both
protein structure and sequence that can operate at significantly larger scales
than previous molecular generative modeling approaches. The model is learned
entirely from experimental data and conditions its generation on a compact
specification of protein topology to produce a full-atom backbone configuration
as well as sequence and side-chain predictions. We demonstrate the quality of
the model via qualitative and quantitative analysis of its samples. Videos of
sampling trajectories are available at https://nanand2.github.io/proteins .
