Enzyme engineering in the age of artificial intelligence

doi:10.12211/2096-8280.2023-009

Abstract

Abstract:

Enzymes have garnered significant attention in both research and industry due to their unparalleled specificity and functionality, and thus opportunities remain for enhancing their physichemical properties and fitness to improve catalytic performance. The primary objective of enzyme engineering is to optimize the fitness of targeted enzymes through various strategies for their modifications, even redesigning. This review provides a comprehensive overview for progress made in enzyme engineering, with a focus on artificial intelligence (AI)-guided design methodology. Several key strategies have been employed in enzyme engineering, including rational design, directed evolution, semi-rational design, and AI-guided design. Rational design relies on an extensive knowledge based on encompassing protein structures and catalytic mechanisms, allowing for purposeful manipulations of enzyme properties. Directed evolution, on the other hand, involves the generation of a library of random variants for subsequent high-throughput screening to identify beneficial mutations. Semi-rational design combines rational design and directed evolution, resulting in a smaller, yet more targeted, library of variants, which mitigates high cost associated with extensive screening of large libraries developed through directed evolution. In recent years, AI technologies, particularly deep neural networks, have emerged as a promising approach for enzyme engineering, and AI-guided methods leverage a vast amount of information regarding protein sequences, multiple sequence alignments, and protein structures to learn key features for correlations. These learned features can then be applied to various downstream tasks in enzyme engineering, such as predicting mutations with beneficial effect, optimizing protein stability, and enhancing catalytic activity. Herewith, we delves into advancements and successes in each of these strategies for enzyme engineering, highlighting the growing impact of AI-guided design on the process. By offering a detailed examination of the current state of enzyme engineering, we aim at providing valuable insight for researchers and engineers to further advance the development and optimization of enzymes for more applications.

Key words: enzyme engineering, directed evolution, artificial intelligence, deep neural network

摘要：

自然界中存在的酶拥有多种多样的功能，它们已经被应用在工业生产和学术研究中，但其中许多酶的性质和功能还不能完全满足应用需要，通过改造来提升这类酶的某些特性是酶工程的重要任务。本文介绍了酶工程的主要发展历程，并重点梳理了人工智能（AI）助力酶工程领域的研究进展。酶工程主要包括理性设计、定向进化、半理性设计和人工智能辅助设计等策略。理性设计方法根据酶的催化机理、结构等先验知识进行改造。定向进化技术通过构建随机突变文库和高通量筛选提升目标酶的稳定性和活性等性质。半理性设计方法借助一系列计算方法构建相比于定向进化更小也更合理的突变文库以降低筛选工作量。人工智能技术在大量数据驱动下可以学习有关蛋白质构成和进化的特征信息。通过直接学习自然界中存在的蛋白质序列、共进化信息和结构，深度神经网络已经可以解决许多类型的酶工程问题，如预测具有有益影响的突变、优化蛋白质的稳定性、提高催化活性等。通过对酶工程现状进行分析，本文旨在进一步推动酶的开发和优化以实现更广泛的应用，为研究者和相关从业人员提供更多有价值的见解。

关键词: 酶工程, 定向进化, 人工智能, 深度学习

CLC Number:

Q814

KANG Liqi, TAN Pan, HONG Liang. Enzyme engineering in the age of artificial intelligence[J]. Synthetic Biology Journal, 2023, 4(3): 524-534.

康里奇, 谈攀, 洪亮. 人工智能时代下的酶工程[J]. 合成生物学, 2023, 4(3): 524-534.

Figures/Tables 3

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蛋白质适应度分类	数据集	ESM-IF1	ESM-1v	MSA transformer	ProGen2	Tranception
催化活性	B3VI55_LIPST	0.291	0.272	0.316	0.239	0.290
	MTH3_HAEAESTABILIZED	0.423	0.488	0.564	0.507	0.479
	KKA2_KLEPN	0.204	0.198	0.153	0.191	0.077
	MK01_HUMAN	0.155	0.164	0.182	0.200	0.203
	AMIE_PSEAE	0.295	0.537	0.523	0.526	0.517
	RASH_HUMAN	0.070	0.131	0.089	0.135	0.085
	UBC9_HUMAN	0.485	0.518	0.425	0.484	0.476
	BG_STRSQ	0.665	0.670	0.727	0.749	0.656
	TRPC_THEMA	0.392	0.488	0.462	0.397	0.444
	TIM_SULSO	0.506	0.617	0.613	0.529	0.594
	P84126_THETH	0.519	0.564	0.656	0.558	0.548
	BLAT_ECOLX	0.673	0.692	0.538	0.601	0.622
稳定性	PTEN_HUMAN	0.559	0.458	0.366	0.471	0.430
稳定性	TPMT_HUMAN	0.560	0.531	0.530	0.458	0.478
肽段结合能力	DLG4_RAT	0.468	0.531	0.224	0.361	0.446
肽段结合能力	WW	0.415	0.399	0.441	0.309	0.563
蛋白质结合能力	IF1_ECOLI	0.337	0.356	0.363	0.246	0.347
	SUMO1_HUMAN	0.543	0.548	0.565	0.482	0.423
	RL40B_YEAST	0.372	0.365	0.647	0.473	0.455
DNA结合能力	FOSJUN	0.532	0.464	0.366	0.515	0.536
DNA结合能力	GAL4_YEAST	0.326	0.476	0.386	0.468	0.458
RNA 结合能力	RRM	0.443	0.536	0.509	0.512	0.407
RNA 结合能力	TDP43	0.158	0.026	0.117	0.013	0.125
Ig-G结合能力	GB1	0.337	0.105	0.329	0.232	0.254
平均值		0.413	0.453	0.438	0.376	0.374

蛋白质适应度分类	数据集	ESM-IF1	ESM-1v	MSA transformer	ProGen2	Tranception
催化活性	B3VI55_LIPST	0.291	0.272	0.316	0.239	0.290
	MTH3_HAEAESTABILIZED	0.423	0.488	0.564	0.507	0.479
	KKA2_KLEPN	0.204	0.198	0.153	0.191	0.077
	MK01_HUMAN	0.155	0.164	0.182	0.200	0.203
	AMIE_PSEAE	0.295	0.537	0.523	0.526	0.517
	RASH_HUMAN	0.070	0.131	0.089	0.135	0.085
	UBC9_HUMAN	0.485	0.518	0.425	0.484	0.476
	BG_STRSQ	0.665	0.670	0.727	0.749	0.656
	TRPC_THEMA	0.392	0.488	0.462	0.397	0.444
	TIM_SULSO	0.506	0.617	0.613	0.529	0.594
	P84126_THETH	0.519	0.564	0.656	0.558	0.548
	BLAT_ECOLX	0.673	0.692	0.538	0.601	0.622
稳定性	PTEN_HUMAN	0.559	0.458	0.366	0.471	0.430
稳定性	TPMT_HUMAN	0.560	0.531	0.530	0.458	0.478
肽段结合能力	DLG4_RAT	0.468	0.531	0.224	0.361	0.446
肽段结合能力	WW	0.415	0.399	0.441	0.309	0.563
蛋白质结合能力	IF1_ECOLI	0.337	0.356	0.363	0.246	0.347
	SUMO1_HUMAN	0.543	0.548	0.565	0.482	0.423
	RL40B_YEAST	0.372	0.365	0.647	0.473	0.455
DNA结合能力	FOSJUN	0.532	0.464	0.366	0.515	0.536
DNA结合能力	GAL4_YEAST	0.326	0.476	0.386	0.468	0.458
RNA 结合能力	RRM	0.443	0.536	0.509	0.512	0.407
RNA 结合能力	TDP43	0.158	0.026	0.117	0.013	0.125
Ig-G结合能力	GB1	0.337	0.105	0.329	0.232	0.254
平均值		0.413	0.453	0.438	0.376	0.374

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