Machine learning meets enzyme engineering: examples in the design of polyethylene terephthalate hydrolases

Abstract

The trend of employing machine learning methods has been increasing to develop promising biocatalysts. Leveraging the experimental findings and simulation data, these methods facilitate enzyme engineering and even the design of new-to-nature enzymes. This review focuses on the application of machine learning methods in the engineering of polyethylene terephthalate (PET) hydrolases, enzymes that have the potential to help address plastic pollution. We introduce an overview of machine learning workflows, useful methods and tools for protein design and engineering, and discuss the recent progress of machine learning-aided PET hydrolase engineering and de novo design of PET hydrolases. Finally, as machine learning in enzyme engineering is still evolving, we foresee that advancements in computational power and quality data resources will considerably increase the use of data-driven approaches in enzyme engineering in the coming decades.

Graphical abstract

Keywords

machine learning / artificial intelligence / enzyme engineering / polyethylene terephthalate hydrolase / enzyme design

References

[1] BescondA SPujariA. PET Polymer—Chemical Economics Handbook (IHS Markit). 2020

[2] Carr C M , Clarke D J , Dobson A D W . Microbial polyethylene terephthalate hydrolases: current and future perspectives. Frontiers in Microbiology, 2020, 11: 571265

[3] Wei R , von Haugwitz G , Pfaff L , Mican J , Badenhorst C P S , Liu W , Weber G , Austin H P , Bednar D , Damborsky J . . Mechanism-based design of efficient PET hydrolases. ACS Catalysis, 2022, 12(6): 3382–3396

[4] Fang Y , Chao K , He J , Wang Z , Chen Z . High-efficiency depolymerization/degradation of polyethylene terephthalate plastic by a whole-cell biocatalyst. Biotech, 2023, 13(5): 138

[5] Ambrose-Dempster E , Leipold L , Dobrijevic D , Bawn M , Carter E M , Stojanovski G , Sheppard T D , Jeffries J W , Ward J M , Hailes H C . Mechanoenzymatic reactions for the hydrolysis of PET. RSC Advances, 2023, 13(15): 9954–9962

[6] Cao F , Wang L , Zheng R , Guo L , Chen Y , Qian X . Research and progress of chemical depolymerization of waste PET and high-value application of its depolymerization products. RSC Advances, 2022, 12(49): 31564–31576

[7] Lai J , Huang H , Lin M , Xu Y , Li X , Sun B . Enzyme catalyzes ester bond synthesis and hydrolysis: the key step for sustainable usage of plastics. Frontiers in Microbiology, 2023, 13: 1113705

[8] Magalhães R P , Cunha J M , Sousa S F . Perspectives on the role of enzymatic biocatalysis for the degradation of plastic PET. International Journal of Molecular Sciences, 2021, 22(20): 11257

[9] Akram E , Cao Y , Xing H , Ding Y , Luo Y , Wei R , Zhang Y . On the temperature dependence of enzymatic degradation of poly(ethylene terephthalate). Chinese Journal of Catalysis, 2024, 60: 284–293

[10] Müller R J , Schrader H , Profe J , Dresler K , Deckwer W D . Enzymatic degradation of poly(ethylene terephthalate): rapid hydrolyse using a hydrolase from T. fusca. Macromolecular Rapid Communications, 2005, 26(17): 1400–1405

[11] Sulaiman S , Yamato S , Kanaya E , Kim J J , Koga Y , Takano K , Kanaya S . Isolation of a novel cutinase homolog with polyethylene terephthalate-degrading activity from leaf-branch compost by using a metagenomic approach. Applied and Environmental Microbiology, 2012, 78(5): 1556–1562

[12] Yoshida S , Hiraga K , Takehana T , Taniguchi I , Yamaji H , Maeda Y , Toyohara K , Miyamoto K , Kimura Y , Oda K . A bacterium that degrades and assimilates poly(ethylene terephthalate). Science, 2016, 351(6278): 1196–1199

[13] Cui Y , Chen Y , Liu X , Dong S , Tian Y E , Qiao Y , Mitra R , Han J , Li C , Han X . . Computational redesign of a PETase for plastic biodegradation under ambient condition by the grape strategy. ACS Catalysis, 2021, 11(3): 1340–1350

[14] Ding Z , Xu G , Miao R , Wu N , Zhang W , Yao B , Guan F , Huang H , Tian J . Rational redesign of thermophilic PET hydrolase LCCICCG to enhance hydrolysis of high crystallinity polyethylene terephthalates. Journal of Hazardous Materials, 2023, 453: 131386

[15] Li Q , Zheng Y , Su T , Wang Q , Liang Q , Zhang Z , Qi Q , Tian J . Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories. Computational and Structural Biotechnology Journal, 2022, 20: 459–470

[16] Lu H , Diaz D J , Czarnecki N J , Zhu C , Kim W , Shroff R , Acosta D J , Alexander B R , Cole H O , Zhang Y . . Machine learning-aided engineering of hydrolases for PET depolymerization. Nature, 2022, 604(7907): 662–667

[17] Meng S , Li Z , Zhang P , Contreras F , Ji Y , Schwaneberg U . Deep learning guided enzyme engineering of Thermobifida fusca cutinase for increased PET depolymerization. Chinese Journal of Catalysis, 2023, 50: 229–238

[18] Cui Y , Chen Y , Sun J , Zhu T , Pang H , Li C , Geng W C , Wu B . Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading. Nature Communications, 2024, 15(1): 1417

[19] Bell E L , Smithson R , Kilbride S , Foster J , Hardy F J , Ramachandran S , Tedstone A A , Haigh S J , Garforth A A , Day P J . . Directed evolution of an efficient and thermostable PET depolymerase. Nature Catalysis, 2022, 5(8): 673–681

[20] Liu F , Wang T , Yang W , Zhang Y , Gong Y , Fan X , Wang G , Lu Z , Wang J . Current advances in the structural biology and molecular engineering of PETase. Frontiers in Bioengineering and Biotechnology, 2023, 11: 1263996

[21] Son H F , Cho I J , Joo S , Seo H , Sagong H Y , Choi S Y , Lee S Y , Kim K J . Rational protein engineering of thermo-stable PETase from Ideonella sakaiensis for highly efficient PET degradation. ACS Catalysis, 2019, 9(4): 3519–3526

[22] Zurier H S , Goddard J M . A high-throughput expression and screening platform for applications-driven PETase engineering. Biotechnology and Bioengineering, 2023, 120(4): 1000–1014

[23] Tournier V , Topham C , Gilles A , David B , Folgoas C , Moya Leclair E , Kamionka E , Desrousseaux M L , Texier H , Gavalda S . . An engineered PET depolymerase to break down and recycle plastic bottles. Nature, 2020, 580(7802): 216–219

[24] Thiyagarajan S , Maaskant-Reilink E , Ewing T A , Julsing M K , Van Haveren J . Back-to-monomer recycling of polycondensation polymers: opportunities for chemicals and enzymes. RSC Advances, 2022, 12(2): 947–970

[25] YangK KWuZArnoldF H. Machine learning in protein engineering. Preprint arXiv, 2018, arXiv:181110775

[26] Mazurenko S , Prokop Z , Damborsky J . Machine learning in enzyme engineering. ACS Catalysis, 2020, 10(2): 1210–1223

[27] Chang C , Deringer V L , Katti K S , Van Speybroeck V , Wolverton C M . Simulations in the era of exascale computing. Nature Reviews. Materials, 2023, 8(5): 309–313

[28] Pyzer-Knapp E O , Pitera J W , Staar P W , Takeda S , Laino T , Sanders D P , Sexton J , Smith J R , Curioni A . Accelerating materials discovery using artificial intelligence, high performance computing and robotics. npj Computational Materials, 2022, 8(1): 84

[29] Singh V , Patra S , Murugan N A , Toncu D C , Tiwari A . Recent trends in computational tools and data-driven modeling for advanced materials. Materials Advances, 2022, 3(10): 4069–4087

[30] Beller M , Bender M , Bornscheuer U T , Schunk S . Catalysis—Far from Being a Mature Technology. Chemieingenieurtechnik, 2022, 94(11): 1559–1559

[31] Greener J G , Kandathil S M , Moffat L , Jones D T . A guide to machine learning for biologists. Nature Reviews. Molecular Cell Biology, 2022, 23(1): 40–55

[32] Feehan R , Montezano D , Slusky J S . Machine learning for enzyme engineering, selection and design. Protein Engineering, Design & Selection, 2021, 34: gzab019

[33] Markus B , C G C , Andreas K , Arkadij K , Stefan L , Gustav O , Elina S , Radka S . Accelerating biocatalysis discovery with machine learning: a paradigm shift in enzyme engineering, discovery, and design. ACS Catalysis, 2023, 13(21): 14454–14469

[34] Sampaio P S , Fernandes P . Machine learning: a suitable method for biocatalysis. Catalysts, 2023, 13(6): 961

[35] Olivier ChapelleB SAlexanderZ. A continuation method for semi-supervised SVMs. In: Proceedings of the 23rd International Conference on Machine learning, NY: ACM Press, 2006, 185–192

[36] Kouba P , Kohout P , Haddadi F , Bushuiev A , Samusevich R , Sedlar J , Damborsky J , Pluskal T , Sivic J , Mazurenko S . Machine learning-guided protein engineering. ACS Catalysis, 2023, 13(21): 13863–13895

[37] Schomburg I , Chang A , Schomburg D . Brenda, enzyme data and metabolic information. Nucleic Acids Research, 2002, 30(1): 47–49

[38] Berman H M , Westbrook J , Feng Z , Gilliland G , Bhat T N , Weissig H , Shindyalov I N , Bourne P E . The Protein Data Bank. Nucleic Acids Research, 2000, 28(1): 235–242

[39] Yan B , Ran X , Gollu A , Cheng Z , Zhou X , Chen Y , Yang Z J . IntEnzyDB: an integrated structure-kinetics enzymology database. Journal of Chemical Information and Modeling, 2022, 62(22): 5841–5848

[40] Consortium T U . UniProt: a worldwide hub of protein knowledge. Nucleic Acids Research, 2019, 47(D1): D506–D515

[41] Consortium T U . UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 2021, 49(D1): D480–D489

[42] Pleiss J . Standardized data, scalable documentation, sustainable storage-EnzymeML as a basis for FAIR data management in biocatalysis. ChemCatChem, 2021, 13(18): 3909–3913

[43] Velecký J , Hamsikova M , Stourac J , Musil M , Damborsky J , Bednar D , Mazurenko S . SoluProtMutDB: a manually curated database of protein solubility changes upon mutations. Computational and Structural Biotechnology Journal, 2022, 20: 6339–6347

[44] Xavier J S , Nguyen T B , Karmarkar M , Portelli S , Rezende P M , Velloso J P , Ascher D B , Pires D E . ThermoMutDB: a thermodynamic database for missense mutations. Nucleic Acids Research, 2021, 49(D1): D475–D479

[45] Stourac J , Dubrava J , Musil M , Horackova J , Damborsky J , Mazurenko S , Bednar D . FireProtDB: database of manually curated protein stability data. Nucleic Acids Research, 2021, 49(D1): D319–D324

[46] Nikam R , Kulandaisamy A , Harini K , Sharma D , Gromiha M M . ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Research, 2021, 49(D1): D420–D424

[47] Heid E , Probst D , Green W H , Madsen G K . EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions. Chemical Science, 2023, 48(14): 14229–14242

[48] Probst D , Manica M , Nana Teukam Y G , Castrogiovanni A , Paratore F , Laino T . Biocatalysed synthesis planning using data-driven learning. Nature Communications, 2022, 13(1): 964

[49] Ganter M , Bernard T , Moretti S , Stelling J , Pagni M . MetaNetX. org: a website and repository for accessing, analysing and manipulating metabolic networks. Bioinformatics, 2013, 29(6): 815–816

[50] Hafner J , MohammadiPeyhani H , Sveshnikova A , Scheidegger A , Hatzimanikatis V . MohammadiPeyhani H, Sveshnikova A, Scheidegger A, Hatzimanikatis V. Updated atlas of biochemistry with new metabolites and improved enzyme prediction power. ACS Synthetic Biology, 2020, 9(6): 1479–1482

[51] Wishart D S , Li C , Marcu A , Badran H , Pon A , Budinski Z , Patron J , Lipton D , Cao X , Oler E . . PathBank: a comprehensive pathway database for model organisms. Nucleic Acids Research, 2020, 48(D1): D470–D478

[52] Wittig U , Rey M , Weidemann A , Kania R , Müller W . SABIO-RK: an updated resource for manually curated biochemical reaction kinetics. Nucleic Acids Research, 2018, 46(D1): D656–D660

[53] Afify H M , Abdelhalim M B , Mabrouk M S , Sayed A Y . Protein secondary structure prediction (PSSP) using different machine algorithms. Egyptian Journal of Medical Human Genetics, 2021, 22(1): 1–10

[54] Liu B , Wang X , Lin L , Tang B , Dong Q , Wang X . Prediction of protein binding sites in protein structures using hidden Markov support vector machine. BMC Bioinformatics, 2009, 10(1): 1–14

[55] Palla M , Punthambaker S , Stranges B , Vigneault F , Nivala J , Wiegand D , Ayer A , Craig T , Gremyachinskiy D , Franklin H . . Multiplex single-molecule kinetics of nanopore-coupled polymerases. ACS Nano, 2021, 15(1): 489–502

[56] Fang X , Huang J , Zhang R , Wang F , Zhang Q , Li G , Yan J , Zhang H , Yan Y , Xu L . Convolution neural network-based prediction of protein thermostability. Journal of Chemical Information and Modeling, 2019, 59(11): 4833–4843

[57] Gelman S , Fahlberg S A , Heinzelman P , Romero P A , Gitter A . Neural networks to learn protein sequence-function relationships from deep mutational scanning data. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(48): e2104878118

[58] Mellor J , Grigoras I , Carbonell P , Faulon J L . Semisupervised gaussian process for automated enzyme search. ACS Synthetic Biology, 2016, 5(6): 518–528

[59] Pires D E , Ascher D B , Blundell T L . mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 2014, 30(3): 335–342

[60] Hakala K , Kaewphan S , Björne J , Mehryary F , Moen H , Tolvanen M , Salakoski T , Ginter F . Neural network and random forest models in protein function prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, 19(3): 1772–1781

[61] Kathuria C , Mehrotra D , Misra N K . Predicting the protein structure using random forest approach. Procedia Computer Science, 2018, 132: 1654–1662

[62] Wang C , Chen Y , Zhang Y , Li K , Lin M , Pan F , Wu W , Zhang J . A reinforcement learning approach for protein-ligand binding pose prediction. BMC Bioinformatics, 2022, 23(1): 1–18

[63] Ryu J Y , Kim H U , Lee S Y . Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(28): 13996–14001

[64] Dalkiran A , Rifaioglu A S , Martin M J , Cetin A R , Atalay V , Doğan T . ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinformatics, 2018, 19(1): 1–13

[65] Zou Z , Tian S , Gao X , Li Y . mlDEEPre: multi-functional enzyme function prediction with hierarchical multi-label deep learning. Frontiers in Genetics, 2019, 9: 714

[66] Cadet F , Fontaine N , Li G , Sanchis J , Ng F C M , Pandjaitan R , Vetrivel I , Offmann B , Reetz M T . A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes. Scientific Reports, 2018, 8(1): 16757

[67] Robinson S L , Smith M D , Richman J E , Aukema K G , Wackett L P . Machine learning-based prediction of activity and substrate specificity for OleA enzymes in the thiolase superfamily. Synthetic Biology, 2020, 5(1): ysaa004

[68] Robinson S L , Terlouw B R , Smith M D , Pidot S J , Stinear T P , Medema M H , Wackett L P . Global analysis of adenylate-forming enzymes reveals β-lactone biosynthesis pathway in pathogenic nocardia. Journal of Biological Chemistry, 2020, 295(44): 14826–14839

[69] Song J , Li F , Takemoto K , Haffari G , Akutsu T , Chou K C , Webb G I . Prevail, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology, 2018, 443: 125–137

[70] Torng W , Altman R B . High precision protein functional site detection using 3D convolutional neural networks. Bioinformatics, 2019, 35(9): 1503–1512

[71] Somarowthu S , Yang H , Hildebrand D G , Ondrechen M J . High-performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers, 2011, 95(6): 390–400

[72] Li G , Rabe K S , Nielsen J , Engqvist M K . Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synthetic Biology, 2019, 8(6): 1411–1420

[73] Foroozandeh S M , Farhadyar K , Kavousi K , Azarabad M H , Boroomand A , Ariaeenejad S , Hosseini S G . A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: a xylanase temperature dependence case study. Biotechnology and Bioengineering, 2021, 118(2): 759–769

[74] Li F , Yuan L , Lu H , Li G , Chen Y , Engqvist M K , Kerkhoven E J , Nielsen J . Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nature Catalysis, 2022, 5(8): 662–672

[75] Xie W J , Asadi M , Warshel A . Enhancing computational enzyme design by a maximum entropy strategy. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(7): e2122355119

[76] Shroff R , Cole A W , Diaz D J , Morrow B R , Donnell I , Annapareddy A , Gollihar J , Ellington A D , Thyer R . Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS Synthetic Biology, 2020, 9(11): 2927–2935

[77] Ostafe R , Fontaine N , Frank D , Ng F C M , Prodanovic R , Pandjaitan R , Offmann B , Cadet F , Fischer R . One-shot optimization of multiple enzyme parameters: tailoring glucose oxidase for pH and electron mediators. Biotechnology and Bioengineering, 2020, 117(1): 17–29

[78] Høie M H , Cagiada M , Frederiksen A H B , Stein A , Lindorff Larsen K . Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation. Cell Reports, 2022, 38(2): 110207

[79] Chew A K , Jiang S , Zhang W , Zavala V M , Van Lehn R C . Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks. Chemical Science, 2020, 11(46): 12464–12476

[80] RanXJiangYShaoQYangZ J. EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution. Chemical Science, 2023, 14(43): 12073–12082

[81] Wang S , Tang H , Zhao Y , Zuo L . BayeStab: predicting effects of mutations on protein stability with uncertainty quantification. Protein Science, 2022, 31(11): e4467

[82] Iqbal S , Ge F , Li F , Akutsu T , Zheng Y , Gasser R B , Yu D J , Webb G I , Song J . PROST: Alphafold2-aware sequence-based predictor to estimate protein stability changes upon missense mutations. Journal of Chemical Information and Modeling, 2022, 62(17): 4270–4282

[83] Hernández I M , Dehouck Y , Bastolla U , López-Blanco J R , Chacón P . Predicting protein stability changes upon mutation using a simple orientational potential. Bioinformatics, 2023, 39(1): btad011

[84] Pak M A , Markhieva K A , Novikova M S , Petrov D S , Vorobyev I S , Maksimova E S , Kondrashov F A , Ivankov D N . Using Alphafold to predict the impact of single mutations on protein stability and function. PLoS One, 2023, 18(3): e0282689

[85] Gado J E , Beckham G T , Payne C M . Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning. Journal of Chemical Information and Modeling, 2020, 60(8): 4098–4107

[86] Yang Y , Zeng L , Vihinen M . PON-Sol2: prediction of effects of variants on protein solubility. International Journal of Molecular Sciences, 2021, 22(15): 8027

[87] Russ W P , Figliuzzi M , Stocker C , Barrat-Charlaix P , Socolich M , Kast P , Hilvert D , Monasson R , Cocco S , Weigt M . An evolution-based model for designing chorismate mutase enzymes. Science, 2020, 369(6502): 440–445

[88] Mak W S , Wang X , Arenas R , Cui Y , Bertolani S , Deng W Q , Tagkopoulos I , Wilson D K , Siegel J B . Discovery, design, and structural characterization of alkane-producing enzymes across the ferritin-like superfamily. Biochemistry, 2020, 59(40): 3834–3843

[89] Dauparas J , Anishchenko I , Bennett N , Bai H , Ragotte R J , Milles L F , Wicky B I , Courbet A , de Haas R J , Bethel N . . Robust deep learning-based protein sequence design using ProteinMPNN. Science, 2022, 378(6615): 49–56

[90] Watson J L , Juergens D , Bennett N R , Trippe B L , Yim J , Eisenach H E , Ahern W , Borst A J , Ragotte R J , Milles L F . . De novo design of protein structure and function with RFdiffusion. Nature, 2023, 620(7976): 1089–1100

[91] Wu K E , Yang K K , van den Berg R , Alamdari S , Zou J Y , Lu A X , Amini A P . Berg R V D, Zou J Y, Lu A X, et al. Protein structure generation via folding diffusion. Nature Communications, 2024, 15(1): 1059

[92] ZhangZXuMJamasbAChenthamarakshanVLozanoADasPTangJ. Protein representation learning by geometric structure pretraining. Preprint arXiv: 2203.06125, 2022

[93] Zhang J , Wang H , Luo Z , Yang Z , Zhang Z , Wang P , Li M , Zhang Y , Feng Y , Lu D . . Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling. Communications Biology, 2023, 6(1): 1135

[94] Xu A , Zhou J , Blank L M , Jiang M . Future focuses of enzymatic plastic degradation. Trends in Microbiology, 2023, 31(7): 668–671

[95] Zhang Y . A relay for improving the catalytic efficiency and thermostability of PET hydrolases. Chem Catalysis, 2022, 2(10): 2420–2422

[96] SchymkowitzJBorgJStricherFNysRRousseauFSerranoL. The FoldX web server: an online force field. Nucleic Acids Research, 2005, 33: W382–W388

[97] GuptaAAgrawalS. Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation. Journal of Emgerging Investigators, 2023, 6: doi:10.59720/22-016

[98] DingYZhangSHessHKongXZhangY. Replicating enzymatic activity by positioning active sites with synthetic protein scaffolds. BioRxiv, 2024, bioRxiv 2024.01.31.577620

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant number 32371325, the Seed Funding of China Petrochemical Corporation (Sinopec Group) under grant number 223260, and the Fundamental Research Funds for the Central Universities (QNTD2023-01).

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