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

doi:10.1007/s11705-024-2500-7

Front. Chem. Sci. Eng.

2024, Vol. 18

Issue (12) : 149 https://doi.org/10.1007/s11705-024-2500-7

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

Rohan Ali^1,², Yifei Zhang^1,²(

)

¹. State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing 100029, China
². Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

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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.

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

Corresponding Author(s): Yifei Zhang

Just Accepted Date: 27 June 2024 Issue Date: 23 September 2024

Cite this article:

Yifei Zhang,Rohan Ali. Machine learning meets enzyme engineering: examples in the design of polyethylene terephthalate hydrolases[J]. Front. Chem. Sci. Eng., 2024, 18(12): 149.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-024-2500-7
https://academic.hep.com.cn/fcse/EN/Y2024/V18/I12/149

Fig.1 Workflow diagram of machine learning model training. From left to right, step 1 involves fetching and sorting data, step 2 is training algorithm with sorted training/labeled data and building model, and step 3 is validating the model with test data and fine-tuning the model.

Objective	Machine learning tool	Machine learning algorithms	Input data	Availability	Ref.
Enzyme classification	DeepEC	Convolutional neural network	Protein sequences	Downloadable	[63]
	ECPred	Ensemble (SVM, k-nearest neighbors)	Protein sequences	Webserver	[64]
	mlDEEPre	Ensemble (Convolutional neural network, recurrent neural networks)	Protein sequences	Webserver	[65]

Substrate identification	innov’SAR	Partial least squares regression	Protein structures and sequences	Webserver	[66]
	pNPred	Random forest	Protein structures and sequences	Webserver	[67]
	AdenylPred	Random forest	Protein sequences	N/A	[68]

Enzyme catalytic site prediction	PREvaIL	Random forest	Protein structures and sequences	Downloadable	[69]
	3DCNN	Convolutional neural network	Protein structure	N/A	[70]
	MAHOMES	Random forest	Protein structures	Downloadable	[32]
	POOL	POOL	Protein structures and sequences	Webserver	[71]

Optimum condition prediction	TOME	Random forest	Protein sequences	Downloadable	[72]
Optimum condition prediction	TAXyl	Random forest	Protein sequences	Downloadable	[73]

Enzyme activity prediction	DLKcat	Graph-based and Convolutional neural networks	Substrates from SMILES and enzyme sequences	Downloadable	[74]
	MaxEnt	Statistical Potts model	Single and pairwise amino acid frequencies from MSA	Downloadable	[75]
	MutCompute	Self-supervised convolutional neural network	Protein structures	Webserver	[76]
	innov’SAR	Partial least-squares regression	N/A	N/A	[77]
	Machine learning-variants-Hoie-et-al	Random forest	Custom preprocessing, derived from PRISM approach	Downloadable	[78]
	innov’SAR	Partial least squares regression	Protein sequences	N/A	[77]
	SolventNet	Convolutional neural network	Protein structures	N/A	[79]
	EnzyKR	Classifier-regressor architecture	Substrate-hydrolase complexes	Downloadable	[80]

Stability prediction (ΔΔG)	BayeStab	Bayesian neural networks	Protein structures	Webserver	[81]
	PROST	Ensemble model	Protein sequences	Downloadable	[82]
	KORPM	Nonlinear regression	Protein sequences	Downloadable	[83]
	ABYSSAL	Siamese deep neural networks	Protein sequences	Downloadable	[84]
	TOMER	bagging with resampling	Protein sequences	Downloadable	[85]

Protein solubility	PON-Sol2	LightGBM	Protein sequences	Webserver	[86]

Protein design	bmDCA	Direct coupling analysis	Protein sequences	Downloadable	[87]
	bmDCA	Linear regression	Protein structures	Downloadable	[88]
	ProteinMPNN	Message-passing neural network	Protein structures	Downloadable	[89]
	RFdiffusion	Denoising diffusion probabilistic model	Protein structures	Downloadable	[90]
	FoldingDiff	Denoising diffusion probabilistic model	Backbones from the CATH data set	Downloadable	[91]
	GearNet	Graph neural network	Protein structures	Downloadable	[92]

Tab.1 A short list of machine learning tools that are helpful in enzyme engineering

Fig.2 Typical machine learning-based strategies for performance and stability enhancement of PET hydrolase. (a) The features of the local microenvironment from proteins in the PDB were extracted by feeding structural characteristics into a series of layers, and as an output, each amino acid was given a probability value (showing chemical congruency). After classification, the predicted mutations obtained were experimentally validated and four mutations showed improvements. These four mutations were introduced into three PETase scaffolds, and with ThermoPETase as a scaffold, the most efficient variant FAST-PETase was obtained. Reprinted with permission from Ref. [16], copyright 2022, Springer Nature. (b) Promising mutations were predicted by the Transformer model after being trained on two different data sets. The mutations in PET binding site residues lead to the H218S/F222I variant (M2). There was a decrease in stability with further engineering of M2 by adding the remaining mutations at predicted sites. Therefore, the GRAPE strategy was used, resulting in the final mutant TurboPETase. Reprinted with permission from Ref. [18], copyright 2024, Springer Nature. (c) Three different machine learning algorithms (Logistic, SVM and Random Forest) were employed on MD trajectories generated by data from Protherm, to understand protein stability and structural features. The learned rules were employed for building a Random Forest-based model to predict thermal stability (T_m) changes leading to the generation of the TfCut^PSP mutant. Reprinted with permission from Ref. [15], copyright 2022, Research Network of Computational and Structural Biotechnology.

Fig.3 Performances of PET hydrolases engineered by machine learning methods. The figure illustrates the increase in activity fold and changes in T_m of the engineered PET hydrolases sorted by their year of discovery. From left to right: TfCut2 (blue) was used as the WT to engineer ① TfCut2^PSP (green) by using MD simulations with MDL [15], ② TfCut2^EEQ (blue) by MutCompute model [17]; ③ Fast-PETase (purple) was engineered by employing MutCompute model from Thermo-PETase (purple) [16], ④ LCC^ICCG-I6M (red) by Preoptem and evolutionary analysis from LCC^ICCG (orange) [14]; ⑤ TurboPETase (green) was engineered from BhrPETase (red) by integrating protein language and model force-field-based algorithms [18]. The color reflects the T_m value of each PET hydrolase.

Fig.4 A computational workflow of protein scaffold remodeling for creating designer PET hydrolases. The workflow comprises four stages: in the scaffold remodeling stage, catalytic sites and some adjacent structures were extracted and the missing scaffold sequences were generated by inpainting. Then, in the computational screening stage, newly generated sequences were computationally analyzed based on protein microenvironment features. In the experimental validation stage, the sequences were expressed and evaluated based on activity and expression level. Designs exhibiting low activity and expression were then fed to the sequence refinement stage for improvement by employing machine learning-based strategies (RF_joint and ProteinMPNN). The quality designs were obtained by employing iterative rounds of sequence refinement, computational screening, and experimental validation.

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