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

arXiv: https://arxiv.org/abs/2204.13048 · HTML: https://mlsanity.com/paper/2204.13048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)

## Built on by (most cited first, 3 of 3)

- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement](https://mlsanity.com/paper/2305.15151/) (2023)
- [Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders](https://mlsanity.com/paper/2310.02508/) (2023)

## Abstract (arXiv)

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.