  Computational protein design has the potential to deliver novel molecular
structures, binders, and catalysts for myriad applications. Recent neural
graph-based models that use backbone coordinate-derived features show
exceptional performance on native sequence recovery tasks and are promising
frameworks for design. A statistical framework for modeling protein sequence
landscapes using Tertiary Motifs (TERMs), compact units of recurring structure
in proteins, has also demonstrated good performance on protein design tasks. In
this work, we investigate the use of TERM-derived data as features in neural
protein design frameworks. Our graph-based architecture, TERMinator,
incorporates TERM-based and coordinate-based information and outputs a Potts
model over sequence space. TERMinator outperforms state-of-the-art models on
native sequence recovery tasks, suggesting that utilizing TERM-based and
coordinate-based features together is beneficial for protein design.
