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Wang T, Jin X, Lu X, Min X, Ge S, Li S. Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness. Front Genet 2024; 14:1347667. [PMID: 38274106 PMCID: PMC10808456 DOI: 10.3389/fgene.2023.1347667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/20/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.
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Affiliation(s)
- Tianshu Wang
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Xiaocheng Jin
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoli Lu
- Information and Networking Center, Xiamen University, Xiamen, China
| | - Xiaoping Min
- School of Informatics, Institute of Artificial Intelligence, Xiamen University, Xiamen, China
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
| | - Shengxiang Ge
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
| | - Shaowei Li
- State Key Laboratory of Vaccines for Infectious Diseases, Xiamen University, Xiamen, China
- National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, China
- School of Public Health, Xiamen University, Xiamen, China
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Dumina M, Zhdanov D, Zhgun A, Pokrovskaya M, Aleksandrova S, Veselovsky A, El’darov M. Enhancing the Catalytic Activity of Thermo-Asparaginase from Thermococcus sibiricus by a Double Mesophilic-like Mutation in the Substrate-Binding Region. Int J Mol Sci 2023; 24:9632. [PMID: 37298582 PMCID: PMC10253665 DOI: 10.3390/ijms24119632] [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: 04/22/2023] [Revised: 05/27/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
L-asparaginases (L-ASNases) of microbial origin are the mainstay of blood cancer treatment. Numerous attempts have been performed for genetic improvement of the main properties of these enzymes. The substrate-binding Ser residue is highly conserved in L-ASNases regardless of their origin or type. However, the residues adjacent to the substrate-binding Ser differ between mesophilic and thermophilic L-ASNases. Based on our suggestion that the triad, including substrate-binding Ser, either GSQ for meso-ASNase or DST for thermo-ASNase, is tuned for efficient substrate binding, we constructed a double mutant of thermophilic L-ASNase from Thermococcus sibiricus (TsA) with a mesophilic-like GSQ combination. In this study, the conjoint substitution of two residues adjacent to the substrate-binding Ser55 resulted in a significant increase in the activity of the double mutant, reaching 240% of the wild-type enzyme activity at the optimum temperature of 90 °C. The mesophilic-like GSQ combination in the rigid structure of the thermophilic L-ASNase appears to be more efficient in balancing substrate binding and conformational flexibility of the enzyme. Along with increased activity, the TsA D54G/T56Q double mutant exhibited enhanced cytotoxic activity against cancer cell lines with IC90 values from 2.8- to 7.4-fold lower than that of the wild-type enzyme.
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Affiliation(s)
- Maria Dumina
- Federal Research Center “Fundamentals of Biotechnology” of the Russian Academy of Sciences, 117312 Moscow, Russia; (D.Z.); (A.Z.)
| | - Dmitry Zhdanov
- Federal Research Center “Fundamentals of Biotechnology” of the Russian Academy of Sciences, 117312 Moscow, Russia; (D.Z.); (A.Z.)
- Institute of Biomedical Chemistry, 119121 Moscow, Russia (A.V.)
| | - Alexander Zhgun
- Federal Research Center “Fundamentals of Biotechnology” of the Russian Academy of Sciences, 117312 Moscow, Russia; (D.Z.); (A.Z.)
| | | | | | | | - Michael El’darov
- Federal Research Center “Fundamentals of Biotechnology” of the Russian Academy of Sciences, 117312 Moscow, Russia; (D.Z.); (A.Z.)
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Mutation of Key Residues in β-Glycosidase LXYL-P1-2 for Improved Activity. Catalysts 2021. [DOI: 10.3390/catal11091042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The β-glycosidase LXYL-P1-2 identified from Lentinula edodes can be used to hydrolyze 7-β-xylosyl-10-deacetyltaxol (XDT) into 10-deacetyltaxol (DT) for the semi-synthesis of Taxol. Recent success in obtaining the high-resolution X-ray crystal of LXYL-P1-2 and resolving its three-dimensional structure has enabled us to perform molecular docking of LXYL-P1-2 with substrate XDT and investigate the roles of the three noncatalytic amino acid residues located around the active cavity in LXYL-P1-2. Site-directed mutagenesis results demonstrated that Tyr268 and Ser466 were essential for maintaining the β-glycosidase activity, and the L220G mutation exhibited a positive effect on increasing activity by enlarging the channel that facilitates the entrance of the substrate XDT into the active cavity. Moreover, introducing L220G mutation into the other LXYL-P1-2 mutant further increased the enzyme activity, and the β-d-xylosidase activity of the mutant EP2-L220G was nearly two times higher than that of LXYL-P1-2. Thus, the recombinant yeast GS115-EP2-L220G can be used for efficiently biocatalyzing XDT to DT for the semi-synthesis of Taxol. Our study provides not only the prospective candidate strain for industrial production, but also a theoretical basis for exploring the key amino acid residues in LXYL-P1-2.
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