1
|
Zhang Y, Zhang G, Wang T, Chen Y, Wang J, Li P, Wang R, Su J. Understanding Cytochrome P450 Enzyme Substrate Inhibition and Prospects for Elimination Strategies. Chembiochem 2024:e202400297. [PMID: 39287061 DOI: 10.1002/cbic.202400297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 07/04/2024] [Indexed: 09/19/2024]
Abstract
Cytochrome P450 (CYP450) enzymes, which are widely distributed and pivotal in various biochemical reactions, catalyze diverse processes such as hydroxylation, epoxidation, dehydrogenation, dealkylation, nitrification, and bond formation. These enzymes have been applied in drug metabolism, antibiotic production, bioremediation, and fine chemical synthesis. Recent research revealed that CYP450 catalytic kinetics deviated from the classic Michaelis-Menten model. A notable substrate inhibition phenomenon that affects the catalytic efficiency of CYP450 at high substrate concentrations was identified. However, the substrate inhibition of various reactions catalyzed by CYP450 enzymes have not been comprehensively reviewed. This review describes CYP450 substrate inhibition examples and atypical Michaelis-Menten kinetic models, and provides insight into mechanisms of these enzymes. We also reviewed 3D structure and dynamics of CYP450 with substrate binding. Outline methods for alleviating substrate inhibition in CYP450 and other enzymes, including traditional fermentation approaches and protein engineering modifications. The comprehensive analysis presented in this study lays the foundation for enhancing the catalytic efficiency of CYP450 by deregulating substrate inhibition.
Collapse
Affiliation(s)
- Yisang Zhang
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Guobin Zhang
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Taichang Wang
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Yu Chen
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Junqing Wang
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Piwu Li
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Ruiming Wang
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Jing Su
- State Key Laboratory of Biobased Material and Green Papermaking (LBMP), Qilu University of Technology, Jinan, Shandong, China
- Key Laboratory of Shandong Microbial Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| |
Collapse
|
2
|
Xu G, Luo Z, Yan Y, Wang Q, Ma J. OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer. Structure 2024; 32:1001-1010.e2. [PMID: 38657613 DOI: 10.1016/j.str.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/06/2024] [Accepted: 03/28/2024] [Indexed: 04/26/2024]
Abstract
Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.
Collapse
Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China
| | - Yaming Yan
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai 200131, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 201210, China; Shanghai AI Laboratory, Shanghai 200030, China.
| |
Collapse
|
3
|
Xu G, Luo Z, Zhou R, Wang Q, Ma J. OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow. Brief Bioinform 2023; 24:bbad365. [PMID: 37833840 DOI: 10.1093/bib/bbad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/29/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.
Collapse
Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
| | - Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
| | - Ruhong Zhou
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Shanghai Institute for Advanced Study, Zhejiang University, Shanghai, 201203, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, 200131, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
- Shanghai Institute for Advanced Study, Zhejiang University, Shanghai, 201203, China
| |
Collapse
|