EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design

• URL: http://arxiv.org/abs/2601.19205v1
• Date: Tue, 27 Jan 2026 05:07:55 GMT
• Title: EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design
• Authors: Zefeng Lin, Zhihang Zhang, Weirong Zhu, Tongchang Han, Xianyong Fang, Tianfan Fu, Xiaohua Xu,
• Abstract summary: EnzyPGM is a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates.<n>At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Fusion (RFF) that incorporates enzyme function priors into residue representations.
• Score: 11.03225817843529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substrates. Currently, generative models dominate functional protein design but cannot model pocket-substrate interactions, which limits the generation of enzymes with precise catalytic environments. To address this issue, we propose EnzyPGM, a unified framework that jointly generates enzymes and substrate-binding pockets conditioned on functional priors and substrates, with a particular focus on learning accurate pocket-substrate interactions. At its core, EnzyPGM includes two main modules: a Residue-atom Bi-scale Attention (RBA) that jointly models intra-residue dependencies and fine-grained interactions between pocket residues and substrate atoms, and a Residue Function Fusion (RFF) that incorporates enzyme function priors into residue representations. Also, we curate EnzyPock, an enzyme-pocket dataset comprising 83,062 enzyme-substrate pairs across 1,036 four-level enzyme families. Extensive experiments demonstrate that EnzyPGM achieves state-of-the-art performance on EnzyPock. Notably, EnzyPGM reduces the average binding energy of 0.47 kcal/mol over EnzyGen, showing its superior performance on substrate-specific enzyme design. The code and dataset will be released later.

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