Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories
[Display omitted] Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays...
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| Published in | Computational and structural biotechnology journal Vol. 20; pp. 459 - 470 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.01.2022
Research Network of Computational and Structural Biotechnology Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2001-0370 2001-0370 |
| DOI | 10.1016/j.csbj.2021.12.042 |
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| Abstract | [Display omitted]
Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔTm values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔTm value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. |
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| AbstractList | Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔTm values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔTm value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high Δ values using MDL method. The optimal variant S121P/D174S/D204P had the highest Δ value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature.Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. [Display omitted] Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔTm values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔTm value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high Δ T m values using MDL method. The optimal variant S121P/D174S/D204P had the highest Δ T m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔTₘ values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔTₘ value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature. |
| Author | Li, Qingbin Zheng, Yi Tian, Jian Su, Tianyuan Wang, Qian Liang, Quanfeng Qi, Qingsheng Zhang, Ziding |
| Author_xml | – sequence: 1 givenname: Qingbin surname: Li fullname: Li, Qingbin organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 2 givenname: Yi surname: Zheng fullname: Zheng, Yi organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 3 givenname: Tianyuan surname: Su fullname: Su, Tianyuan organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 4 givenname: Qian surname: Wang fullname: Wang, Qian organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 5 givenname: Quanfeng surname: Liang fullname: Liang, Quanfeng organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 6 givenname: Ziding surname: Zhang fullname: Zhang, Ziding email: zidingzhang@cau.edu.cn organization: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China – sequence: 7 givenname: Qingsheng surname: Qi fullname: Qi, Qingsheng email: qiqingsheng@sdu.edu.cn organization: State Key Laboratory of Microbial Technology, Shandong University, Qingdao 266237, China – sequence: 8 givenname: Jian surname: Tian fullname: Tian, Jian email: tianjian@caas.cn organization: Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35070168$$D View this record in MEDLINE/PubMed |
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| Keywords | Thermostability Molecular Dynamics Simulations Machine Learning TfCut2 PET |
| Language | English |
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Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show... Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from... |
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| SubjectTerms | biodegradation biotechnology cutinase glass transition temperature Machine Learning molecular dynamics Molecular Dynamics Simulations PET polyethylene terephthalates TfCut2 thermal stability Thermobifida fusca Thermostability |
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| Title | Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories |
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