Progress in the application of protein engineering in the developing of feed enzymes

doi:10.12211/2096-8280.2022-027

Abstract

Abstract:

Feed enzymes are one of the hotspots for research in the field of feed additives. Because of their superior qualities of no residue, no pollution, and no drug resistance, feed enzymes have been widely promoted and applied in the feed industry. As functional additives, feed enzymes have tremendously fostered feed development. Since the catalytic performance of feed enzyme is the core factor in determining their application effectiveness, one of the primary challenges faced by scientists engaged in the R&D of feed enzymes is how to improve their overall performance. Based on the application requirement for feed enzymes, this review focuses on their thermal stability, pH dependence, protease resistance, and catalytic activities. We summarize the application of computer-aided protein rational design technology in the R&D of feed enzymes, and introduce effective molecular design strategies for improving their catalytic performance. In addition, we present the progress in improving the performance of key feed enzymes by protein engineering technology, demonstrating the application prospect of enzyme molecular design technology based on the knowledge of protein structures. We also prospect the future development of protein engineering technology in feed enzymes, including (1) application-oriented designs for feed enzymes, (2) molecular design of multifunctional enzymes for efficient utilization of feed materials, (3) improvement of the thermostability as well as the catalytic performance of feed enzymes to meet the requirement of feed pelleting and under digestive and intestinal conditions, and (4) innovation and development of enzyme molecular design technology. Improving the catalytic performance of feed enzymes comprehensively from the perspective of enzyme proteins using synthetic biology tools will promote the development of the environmental adaptive molecular design of feed enzymes to a new stage.

Key words: feed enzymes, protein engineering, molecular designing, thermostability, pH dependence, protease resistance, catalytic activity

摘要：

饲料用酶制剂作为饲料添加剂领域最为热门的研究热点之一，以其无残留、无污染、无耐药性等强势优势被广泛推广和应用，极大促进了饲料行业的健康发展。其中，饲料用酶的催化性能是决定其应用功效的核心因素，如何提高饲料用酶的综合性能是饲料用酶制剂研发过程中面临的关键科学问题之一。本文从饲料用酶的实际应用需求出发，聚焦饲料用酶的热稳定性、pH依赖性、蛋白酶抗性和催化活性等4个方面，综述了计算机辅助的蛋白质理性设计技术在饲料用酶制剂研发中的应用研究进展，介绍了用于提升饲料用酶催化性能的有效分子设计策略。通过蛋白质工程技术在关键饲料用酶制剂研发中的应用案例介绍，展示了基于结构基础的酶分子设计技术在饲料用酶制剂研发中的应用前景。与此同时，作为一种应用导向极强的饲料添加剂，利用合成生物学的思想从酶蛋白全局角度出发综合提升饲料用酶催化性能的发展方向，将推动饲料用酶制剂环境适应性分子设计的研发迈向新的台阶。

关键词: 饲料用酶, 蛋白质工程, 分子设计, 热稳定性, pH依赖性, 蛋白酶抗性, 催化活性

CLC Number:

Q816

Tao TU, Huiying LUO, Bin YAO. Progress in the application of protein engineering in the developing of feed enzymes[J]. Synthetic Biology Journal, 2022, 3(3): 487-499.

涂涛, 罗会颖, 姚斌. 蛋白质工程在饲料用酶研发中的应用研究进展[J]. 合成生物学, 2022, 3(3): 487-499.

Figures/Tables 4

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软件	描述	网址	参考文献
Consensus Finder	基于共识分析构建共识序列	http://kazlab.umn.edu/	[7]
Disulfide by Design 2.0	基于高B因子参数引入二硫键提高热稳定性	http://cptweb.cpt.wayne.edu/DbD2/	[8]
EASE-MM	综合多种机器学习模型预测点突变引起的稳定性变化	http://sparks-lab.org/server/ease-mm/	[9]
Eris	基于Medusa模型预测突变位点引起的蛋白质稳定性变化	https://dokhlab.med.psu.edu/eris/login.php	[10]
ETSS	基于酶表面电荷优化的策略		[11]
FireProt	基于能量和进化信息的热稳定性多点突变体设计	https://loschmidt.chemi.muni.cz/fireprot/	[12]
FoldX	基于折叠自由能计算酶不稳定区域	https://foldxsuite.crg.eu/	[13]
I-Mutant	基于神经网络预测单点突变引起的稳定性变化	https://folding.biofold.org/i-mutant/i-mutant2.0.html	[14]
mCSM	基于图形特征预测突变位点对蛋白质稳定性的影响	http://biosig.unimelb.edu.au/mcsm/stability	[15]
PoPMuSiC	基于突变位点的溶剂可及性预测其对蛋白质稳定性的影响	https://soft.dezyme.com/	[16]
PremPS	基于解折叠自由能的计算评估单点突变对对酶稳定性的影响	https://lilab.jysw.suda.edu.cn/research/PremPS/	[17]
Prethermut	基于机器学习的方法预测单点或多点突变引起的酶热稳定性变化	http://www.mobioinfor.cn/prethermut	[18]
PROSS	基于结构和序列的自动化蛋白质高表达和稳定性设计	http://pross.weizmann.ac.il	[19]
Rosetta	基于蒙特卡洛模拟退火的优化方法寻找突变位点提高蛋白质稳定性	https://www.rosettacommons.org/software	[20]

软件	描述	网址	参考文献
Consensus Finder	基于共识分析构建共识序列	http://kazlab.umn.edu/	[7]
Disulfide by Design 2.0	基于高B因子参数引入二硫键提高热稳定性	http://cptweb.cpt.wayne.edu/DbD2/	[8]
EASE-MM	综合多种机器学习模型预测点突变引起的稳定性变化	http://sparks-lab.org/server/ease-mm/	[9]
Eris	基于Medusa模型预测突变位点引起的蛋白质稳定性变化	https://dokhlab.med.psu.edu/eris/login.php	[10]
ETSS	基于酶表面电荷优化的策略		[11]
FireProt	基于能量和进化信息的热稳定性多点突变体设计	https://loschmidt.chemi.muni.cz/fireprot/	[12]
FoldX	基于折叠自由能计算酶不稳定区域	https://foldxsuite.crg.eu/	[13]
I-Mutant	基于神经网络预测单点突变引起的稳定性变化	https://folding.biofold.org/i-mutant/i-mutant2.0.html	[14]
mCSM	基于图形特征预测突变位点对蛋白质稳定性的影响	http://biosig.unimelb.edu.au/mcsm/stability	[15]
PoPMuSiC	基于突变位点的溶剂可及性预测其对蛋白质稳定性的影响	https://soft.dezyme.com/	[16]
PremPS	基于解折叠自由能的计算评估单点突变对对酶稳定性的影响	https://lilab.jysw.suda.edu.cn/research/PremPS/	[17]
Prethermut	基于机器学习的方法预测单点或多点突变引起的酶热稳定性变化	http://www.mobioinfor.cn/prethermut	[18]
PROSS	基于结构和序列的自动化蛋白质高表达和稳定性设计	http://pross.weizmann.ac.il	[19]
Rosetta	基于蒙特卡洛模拟退火的优化方法寻找突变位点提高蛋白质稳定性	https://www.rosettacommons.org/software	[20]

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