Reaction-conditioned De Novo Enzyme Design with GENzyme
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917847929716736 |
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| author | Hua, Chenqing Lu, Jiarui Liu, Yong Zhang, Odin Tang, Jian Ying, Rex Jin, Wengong Wolf, Guy Precup, Doina Zheng, Shuangjia |
| author_facet | Hua, Chenqing Lu, Jiarui Liu, Yong Zhang, Odin Tang, Jian Ying, Rex Jin, Wengong Wolf, Guy Precup, Doina Zheng, Shuangjia |
| contents | The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interaction prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16694 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reaction-conditioned De Novo Enzyme Design with GENzyme Hua, Chenqing Lu, Jiarui Liu, Yong Zhang, Odin Tang, Jian Ying, Rex Jin, Wengong Wolf, Guy Precup, Doina Zheng, Shuangjia Biomolecules Artificial Intelligence The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interaction prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme. |
| title | Reaction-conditioned De Novo Enzyme Design with GENzyme |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2411.16694 |