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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; 25:e202400297. [PMID: 39287061 DOI: 10.1002/cbic.202400297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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.
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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
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Simonson T, Mihaila V, Reveguk I. Uncovering substrate specificity determinants of class IIb aminoacyl-tRNA synthetases with machine learning. J Mol Graph Model 2024; 132:108818. [PMID: 39025021 DOI: 10.1016/j.jmgm.2024.108818] [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: 05/01/2024] [Revised: 07/07/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
Specific amino acid (AA) binding by aminoacyl-tRNA synthetases (aaRSs) is necessary for correct translation of the genetic code. Sequence and structure analyses have revealed the main specificity determinants and allowed a partitioning of aaRSs into two classes and several subclasses. However, the information contributed by each determinant has not been precisely quantified, and other, minor determinants may still be unidentified. Growth of genomic data and development of machine learning classification methods allow us to revisit these questions. This work considered the subclass IIb, formed by the three enzymes aspartyl-, asparaginyl-, and lysyl-tRNA synthetase (LysRS). Over 35,000 sequences from the Pfam database were considered, and used to train a machine-learning model based on ensembles of decision trees. The model was trained to reproduce the existing classification of each sequence as AspRS, AsnRS, or LysRS, and to identify which sequence positions were most important for the classification. A few positions (5-8 depending on the AA substrate) sufficed for accurate classification. Most but not all of them were well-known specificity determinants. The machine learning models thus identified sets of mutations that distinguish the three subclass members, which might be targeted in engineering efforts to alter or swap the AA specificities for biotechnology applications.
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Affiliation(s)
- Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France.
| | - Victor Mihaila
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Ivan Reveguk
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
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Chemla Y, Kaufman F, Amiram M, Alfonta L. Expanding the Genetic Code of Bioelectrocatalysis and Biomaterials. Chem Rev 2024; 124:11187-11241. [PMID: 39377473 DOI: 10.1021/acs.chemrev.4c00077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/09/2024]
Abstract
Genetic code expansion is a promising genetic engineering technology that incorporates noncanonical amino acids into proteins alongside the natural set of 20 amino acids. This enables the precise encoding of non-natural chemical groups in proteins. This review focuses on the applications of genetic code expansion in bioelectrocatalysis and biomaterials. In bioelectrocatalysis, this technique enhances the efficiency and selectivity of bioelectrocatalysts for use in sensors, biofuel cells, and enzymatic electrodes. In biomaterials, incorporating non-natural chemical groups into protein-based polymers facilitates the modification, fine-tuning, or the engineering of new biomaterial properties. The review provides an overview of relevant technologies, discusses applications, and highlights achievements, challenges, and prospects in these fields.
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Ferruz N, Stein A. Computational methods for protein design. Protein Eng Des Sel 2024; 37:gzae011. [PMID: 38984793 DOI: 10.1093/protein/gzae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 07/11/2024] Open
Affiliation(s)
- Noelia Ferruz
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Carrer del Doctor Aiguader, 88, 08003 Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Amelie Stein
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
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Brown SM, Mayer-Bacon C, Freeland S. Xeno Amino Acids: A Look into Biochemistry as We Do Not Know It. Life (Basel) 2023; 13:2281. [PMID: 38137883 PMCID: PMC10744825 DOI: 10.3390/life13122281] [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: 10/30/2023] [Revised: 11/18/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023] Open
Abstract
Would another origin of life resemble Earth's biochemical use of amino acids? Here, we review current knowledge at three levels: (1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we do not know it. Amino acids thus remain important and tractable targets for astrobiological research. (2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. (3) Would we expect a similar set of side chains to those within the genetic code? Many plausible alternatives exist. Furthermore, evidence exists for both evolutionary advantage and physicochemical constraint as explanatory factors for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: (a) expanding evidence about amino acids as attractors within chemical evolution; (b) extending characterization of other backbones relative to biological proteins; and (c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides.
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