ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention

Xinyi Zhou, Guangyong Chen (), Junjie Ye, Ercheng Wang, Jun Zhang, Cong Mao, Zhanwei Li, Jianye Hao, Xingxu Huang, Jin Tang and Pheng Ann Heng
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Xinyi Zhou: The Chinese University of Hong Kong
Guangyong Chen: Zhejiang Lab
Junjie Ye: Huawei
Ercheng Wang: Zhejiang Lab
Jun Zhang: Nanjing Medical University
Cong Mao: Nanjing Medical University
Zhanwei Li: Zhejiang Lab
Jianye Hao: Huawei
Xingxu Huang: Zhejiang Lab
Jin Tang: Zhejiang Lab
Pheng Ann Heng: The Chinese University of Hong Kong

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to rely on noisy predicted residues located in the local neighborhood when generating sequences. To address this limitation, we propose an entropy-based residue selection method to remove noise in the input residue context. Additionally, we introduce ProRefiner, a memory-efficient global graph attention model to fully utilize the denoised context. Our proposed method achieves state-of-the-art performance on multiple sequence design benchmarks in different design settings. Furthermore, we demonstrate the applicability of ProRefiner in redesigning Transposon-associated transposase B, where six out of the 20 variants we propose exhibit improved gene editing activity.

Date: 2023
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DOI: 10.1038/s41467-023-43166-6

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