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Computational protein design has the potential to deliver novel molecularstructures, binders, and catalysts for myriad applications. Recent neuralgraph-based models that use backbone coordinate-derived features showexceptional performance on native sequence recovery tasks and are promisingframeworks for design. A statistical framework for modeling protein sequencelandscapes using Tertiary Motifs (TERMs), compact units of recurring structurein proteins, has also demonstrated good performance on protein design tasks. Inthis work, we investigate the use of TERM-derived data as features in neuralprotein design frameworks. Our graph-based architecture, TERMinator,incorporates TERM-based and coordinate-based information and outputs a Pottsmodel over sequence space. TERMinator outperforms state-of-the-art models onnative sequence recovery tasks, suggesting that utilizing TERM-based andcoordinate-based features together is beneficial for protein design.

TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs