合成生物学 ›› 2022, Vol. 3 ›› Issue (3): 487-499.DOI: 10.12211/2096-8280.2022-027
涂涛, 罗会颖, 姚斌
收稿日期:
2022-05-06
修回日期:
2022-05-28
出版日期:
2022-06-30
发布日期:
2022-07-13
通讯作者:
姚斌
作者简介:
基金资助:
Tao TU, Huiying LUO, Bin YAO
Received:
2022-05-06
Revised:
2022-05-28
Online:
2022-06-30
Published:
2022-07-13
Contact:
Bin YAO
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。
中图分类号:
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.
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.
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/ | [ |
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/ | [ |
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/ | [ |
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 | [ |
ETSS | 基于酶表面电荷优化的策略 | [ | |
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/ | [ |
FoldX | 基于折叠自由能计算酶不稳定区域 | https://foldxsuite.crg.eu/ | [ |
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 | [ |
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 | [ |
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/ | [ |
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/ | [ |
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 | [ |
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 | [ |
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 | [ |
表1 用于酶热稳定性设计的软件总结
Tab. 1 Summary for the software used for designing thermo-toler enzymes
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/ | [ |
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/ | [ |
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/ | [ |
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 | [ |
ETSS | 基于酶表面电荷优化的策略 | [ | |
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/ | [ |
FoldX | 基于折叠自由能计算酶不稳定区域 | https://foldxsuite.crg.eu/ | [ |
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 | [ |
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 | [ |
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/ | [ |
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/ | [ |
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 | [ |
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 | [ |
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 | [ |
图2 调整饲料用酶催化残基pKa值的3种策略[42](1)静电作用:在催化残基周围引入带负电荷氨基酸或带正电荷氨酸从而提高或降低其pKa值;(2)氢键作用:通过催化残基作为氢受体或供体从而提高或降低其pKa值;(3)疏水作用:通过催化残基的羧基基团去质子化从而提高其pKa值
Fig. 2 Strategies for adjusting the pKa value of catalytic residues in feeding enzymes[42](1) Electrostatic interactions: Introducing the negatively or positively charged amino acids around catalytic residues to increase or decrease the pKa value; (2) Hydrogen-bond interactions: Acting as the hydrogen acceptor or donor of catalytic residues to increase or decrease the pKa value; (3) Hydrophobic interactions: Deprotonation of the carboxyl group of catalytic residues to increase the pKa value.
1 | SINHA R, SHUKLA P. Current trends in protein engineering: updates and progress[J]. Current Protein & Peptide Science, 2019, 20(5): 398-407. |
2 | PONGSUPASA V, ANUWAN P, MAENPUEN S, et al. Rational-design engineering to improve enzyme thermostability[J]. Methods in Molecular Biology, 2022, 2397: 159-178. |
3 | 冯定远. 酶制剂在饲料养殖中发挥替代抗生素作用的领域及其机理[J]. 饲料工业, 2020, 41(12): 1-10. |
FENG D Y. The different fields and their mechanism of enzymes for replacing antibiotic in feeds and feeding[J]. Feed Industry, 2020, 41(12): 1-10. | |
4 | VICTORINO DA SILVA AMATTO I, GONSALES DA ROSA-GARZON N, ANTÔNIO DE OLIVEIRA SIMÕES F, et al. Enzyme engineering and its industrial applications[J]. Biotechnology and Applied Biochemistry, 2022, 69(2): 389-409. |
5 | 范振港, 陈东, 王祚, 等. 粉碎、制粒工艺对饲料营养成分和单胃动物生产性能的影响[J]. 粮食与饲料工业, 2020(1): 32-36. |
FAN Z G, CHEN D, WANG Z, et al. Effects of crushing and pelletizing technology on nutrient composition of feed and production performance of monogastric animals[J]. Cereal & Feed Industry, 2020(1): 32-36. | |
6 | MRUDULA VASUDEVAN U, JAISWAL A K, KRISHNA S, et al. Thermostable phytase in feed and fuel industries[J]. Bioresource Technology, 2019, 278: 400-407. |
7 | JONES B J, LIM H Y, HUANG J, et al. Comparison of five protein engineering strategies for stabilizing an α/β-hydrolase[J]. Biochemistry, 2017, 56(50): 6521-6532. |
8 | CRAIG D B, DOMBKOWSKI A A. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins[J]. BMC Bioinformatics, 2013, 14: 346. |
9 | FOLKMAN L, STANTIC B, SATTAR A, et al. EASE-MM: Sequence-based prediction of mutation-induced stability changes with feature-based multiple models[J]. Journal of Molecular Biology, 2016, 428(6): 1394-1405. |
10 | YIN S Y, DING F, DOKHOLYAN N V. Eris: an automated estimator of protein stability[J]. Nature Methods, 2007, 4(6): 466-467. |
11 | ZHANG L J, TANG X M, CUI D B, et al. A method to rationally increase protein stability based on the charge-charge interaction, with application to lipase LipK107[J]. Protein Science: a Publication of the Protein Society, 2014, 23(1): 110-116. |
12 | KHAN R T, MUSIL M, STOURAC J, et al. Fully automated ancestral sequence reconstruction using FireProtASR [J]. Current Protocols, 2021, 1(2): e30. |
13 | SCHYMKOWITZ J, BORG J, STRICHER F, et al. The FoldX web server: an online force field[J]. Nucleic Acids Research, 2005, 33(Web Server issue): W382-W388. |
14 | CAPRIOTTI E, FARISELLI P, CASADIO R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure[J]. Nucleic Acids Research, 2005, 33(s2): W306-W310. |
15 | PIRES D E V, ASCHER D B, BLUNDELL T L. mCSM: predicting the effects of mutations in proteins using graph-based signatures[J]. Bioinformatics, 2014, 30(3): 335-342. |
16 | DEHOUCK Y, KWASIGROCH J M, GILIS D, et al. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality[J]. BMC Bioinformatics, 2011, 12: 151. |
17 | CHEN Y T, LU H Y, ZHANG N, et al. PremPS: Predicting the impact of missense mutations on protein stability[J]. PLoS Computational Biology, 2020, 16(12): e1008543. |
18 | TIAN J, WU N F, CHU X Y, et al. Predicting changes in protein thermostability brought about by single- or multi-site mutations[J]. BMC Bioinformatics, 2010, 11: 370. |
19 | GOLDENZWEIG A, GOLDSMITH M, HILL S E, et al. Automated structure-and sequence-based design of proteins for high bacterial expression and stability[J]. Molecular Cell, 2016, 63(2): 337-346. |
20 | KELLOGG E H, LEAVER-FAY A, BAKER D. Role of conformational sampling in computing mutation-induced changes in protein structure and stability[J]. Proteins, 2011, 79(3): 830-838. |
21 | DOTSENKO A S, DENISENKO Y A, ROZHKOVA A M, et al. Enhancement of thermostability of GH10 xylanase E Penicillium canescens directed by ΔΔG calculations and structure analysis[J]. Enzyme and Microbial Technology, 2021, 152: 109938. |
22 | NAVONE L, VOGL T, LUANGTHONGKAM P, et al. Disulfide bond engineering of AppA phytase for increased thermostability requires co-expression of protein disulfide isomerase in Pichia pastoris [J]. Biotechnology for Biofuels, 2021, 14(1): 80. |
23 | SANCHEZ-RUIZ J M. Protein kinetic stability[J]. Biophysical Chemistry, 2010, 148(1/2/3): 1-15. |
24 | CHAKRAVARTY A K, FNASc. Thermodynamic stability of biomolecules and evolution[J]. Journal of Theoretical Biology, 2017, 427: 8-9. |
25 | XIE Y, AN J, YANG G Y, et al. Enhanced enzyme kinetic stability by increasing rigidity within the active site[J]. Journal of Biological Chemistry, 2014, 289(11): 7994-8006. |
26 | CHADHA B S, KAUR B, BASOTRA N, et al. Thermostable xylanases from thermophilic fungi and bacteria: current perspective[J]. Bioresource Technology, 2019, 277: 195-203. |
27 | WANG X C, YOU S P, ZHANG J X, et al. Rational design of a thermophilic β-mannanase from Bacillus subtilis TJ-102 to improve its thermostability[J]. Enzyme and Microbial Technology, 2018, 118: 50-56. |
28 | NING X Y, ZHANG Y L, YUAN T T, et al. Enhanced thermostability of glucose oxidase through computer-aided molecular design[J]. International Journal of Molecular Sciences, 2018, 19(2): 425. |
29 | LI G L, FANG X R, SU F, et al. Enhancing the thermostability of Rhizomucor miehei lipase with a limited screening library by rational-design point mutations and disulfide bonds[J]. Applied and Environmental Microbiology, 2018, 84(2): e02129-e02117. |
30 | TU T, WANG Z Y, LUO Y, et al. Structural insights into the mechanisms underlying the kinetic stability of GH28 endo-polygalacturonase[J]. Journal of Agricultural and Food Chemistry, 2021, 69(2): 815-823. |
31 | WANG S, MENG K, SU X Y, et al. Cysteine engineering of an endo-polygalacturonase from Talaromyces leycettanus JCM 12802 to improve its thermostability[J]. Journal of Agricultural and Food Chemistry, 2021, 69(22): 6351-6359. |
32 | LIU W N, TU T, GU Y, et al. Insight into the thermophilic mechanism of a glycoside hydrolase family 5 β-mannanase[J]. Journal of Agricultural and Food Chemistry, 2019, 67(1): 473-483. |
33 | ZHENG F, VERMAAS J V, ZHENG J, et al. Activity and thermostability of GH5 endoglucanase chimeras from mesophilic and thermophilic parents[J]. Applied and Environmental Microbiology, 2019, 85(5): e02079-e02018. |
34 | BU Y F, CUI Y L, PENG Y, et al. Engineering improved thermostability of the GH11 xylanase from Neocallimastix patriciarum via computational library design[J]. Applied Microbiology and Biotechnology, 2018, 102(8): 3675-3685. |
35 | 刘娇, 陈志敏, 郑爱娟, 等. 葡萄糖氧化酶对大肠杆菌攻毒肉鸭生长性能、免疫功能及肠道健康的影响[J]. 中国农业科学, 2021, 54(22): 4917-4930. |
LIU J, CHEN Z M, ZHENG A J, et al. Effects of glucose oxidase on growth performance, immune functions and intestinal health of ducks challenged by Escherichia coli [J]. Scientia Agricultura Sinica, 2021, 54(22): 4917-4930. | |
36 | LIANG Z Q, YAN Y R, ZHANG W, et al. Review of glucose oxidase as a feed additive: production, engineering, applications, growth-promoting mechanisms, and outlook[J]. Critical Reviews in Biotechnology, 2022. DOI: 10.1080/07388551.2022.2057275 . |
37 | LIU Z M, YUAN M X, ZHANG X Y, et al. A thermostable glucose oxidase from Aspergillus heteromophus CBS 117.55 with broad pH stability and digestive enzyme resistance[J]. Protein Expression and Purification, 2020, 176: 105717. |
38 | TU T, WANG Y, HUANG H Q, et al. Improving the thermostability and catalytic efficiency of glucose oxidase from Aspergillus niger by molecular evolution[J]. Food Chemistry, 2019, 281: 163-170. |
39 | JIANG X, WANG Y R, WANG Y, et al. Exploiting the activity-stability trade-off of glucose oxidase from Aspergillus niger using a simple approach to calculate thermostability of mutants[J]. Food Chemistry, 2021, 342: 128270. |
40 | 胡炜恒, 郑文才, 廖星毅, 等. 益生菌有机酸复合添加剂对AA肉鸡血清生化指标和消化道pH值的影响[J]. 广东饲料, 2014, 23(12): 30-34. |
HU W H, ZHENG W C, LIAO X Y, et al. Effects of probiotic organic acid compound additive on serum biochemical indices and digestive pH value of AA broilers[J]. Guangdong Feed, 2014, 23(12): 30-34. | |
41 | ABBASI KHEIRABADI M, SAFFAR B, HEMMATI R, et al. Thermally stable and acidic pH tolerant mutant phytases with high catalytic efficiency from Yersinia intermedia for potential application in feed industries[J]. Environmental Science and Pollution Research International, 2022, 29(22): 33713-33724. |
42 | LI S F, CHENG F, WANG Y J, et al. Strategies for tailoring pH performances of glycoside hydrolases[J]. Critical Reviews in Biotechnology, 2021: 1-21. DOI: 10.1080/07388551.2021.2004084 . |
43 | ANANDAKRISHNAN R, AGUILAR B, ONUFRIEV A V. H++ 3.0: Automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations[J]. Nucleic Acids Research, 2012, 40(W1): W537-W541. |
44 | MONGAN J, CASE D A, MCCAMMON J A. Constant pH molecular dynamics in generalized Born implicit solvent[J]. Journal of Computational Chemistry, 2004, 25(16): 2038-2048. |
45 | TYNAN-CONNOLLY B M, NIELSEN J E. Redesigning protein pKa values[J]. Protein Science, 2007, 16(2): 239-249. |
46 | ROSTKOWSKI M, OLSSON M H M, SØNDERGAARD C R, et al. Graphical analysis of pH-dependent properties of proteins predicted using PROPKA[J]. BMC Structural Biology, 2011, 11: 6. |
47 | COCKBURN D W, CLARKE A J. Modulating the pH-activity profile of cellulase A from Cellulomonas fimi by replacement of surface residues[J]. Protein Engineering, Design and Selection, 2011, 24(5): 429-437. |
48 | WANG C H, LIU X L, HUANG R B, et al. Enhanced acidic adaptation of Bacillus subtilis Ca-independent α-amylase by rational engineering of pKa values[J]. Biochemical Engineering Journal, 2018, 139: 146-153. |
49 | LUDWICZEK M L, D'ANGELO I, YALLOWAY G N, et al. Strategies for modulating the pH-dependent activity of a family 11 glycoside hydrolase[J]. Biochemistry, 2013, 52(18): 3138-3156. |
50 | FUSHINOBU S, UNO T, KITAOKA M, et al. Mutational analysis of fungal family 11 xylanases on pH optimum determination[J]. Journal of Applied Glycoscience, 2011, 58(3): 107-114. |
51 | TISHKOV V I, GUSAKOV A V, CHERKASHINA A S, et al. Engineering the pH-optimum of activity of the GH12 family endoglucanase by site-directed mutagenesis[J]. Biochimie, 2013, 95(9): 1704-1710. |
52 | POKHREL S, JOO J C, YOO Y J. Shifting the optimum pH of Bacillus circulans xylanase towards acidic side by introducing arginine[J]. Biotechnology and Bioprocess Engineering, 2013, 18(1): 35-42. |
53 | LI Z H, ZHANG X S, WANG Q Q, et al. Understanding the pH-dependent reaction mechanism of a glycoside hydrolase using high-resolution X-ray and neutron crystallography[J]. ACS Catalysis, 2018, 8(9): 8058-8069. |
54 | LI H, TURUNEN O. Effect of acidic amino acids engineered into the active site cleft of Thermopolyspora flexuosa GH11 xylanase[J]. Biotechnology and Applied Biochemistry, 2015, 62(4): 433-440. |
55 | LI Q F, JIANG T, LIU R, et al. Tuning the pH profile of β-glucuronidase by rational site-directed mutagenesis for efficient transformation of glycyrrhizin[J]. Applied Microbiology and Biotechnology, 2019, 103(12): 4813-4823. |
56 | XIA W, XU X X, QIAN L C, et al. Engineering a highly active thermophilic β-glucosidase to enhance its pH stability and saccharification performance[J]. Biotechnology for Biofuels, 2016, 9: 147. |
57 | ZHOU S J, LIU Z M, XIE W C, et al. Improving catalytic efficiency and maximum activity at low pH of Aspergillus neoniger phytase using rational design[J]. International Journal of Biological Macromolecules, 2019, 131: 1117-1124. |
58 | MA F Q, XIE Y, LUO M J, et al. Sequence homolog-based molecular engineering for shifting the enzymatic pH optimum[J]. Synthetic and Systems Biotechnology, 2016, 1(3): 195-206. |
59 | NIU C F, YANG P L, YAO B. Engineering protease-resistant and highly active phytases[J]. Methods in Molecular Biology, 2020, 2091: 155-162. |
60 | QIU Y X, WU X Y, XIE C F, et al. A rational design for improving the trypsin resistance of aflatoxin-detoxifizyme (ADTZ) based on molecular structure evaluation[J]. Enzyme and Microbial Technology, 2016, 86: 84-92. |
61 | NIU C F, YANG P L, LUO H Y, et al. Engineering the residual side chains of HAP phytases to improve their pepsin resistance and catalytic efficiency[J]. Scientific Reports, 2017, 7: 42133. |
62 | NIU C F, YANG P L, LUO H Y, et al. Engineering of Yersinia phytases to improve pepsin and trypsin resistance and thermostability and application potential in the food and feed industry[J]. Journal of Agricultural and Food Chemistry, 2017, 65(34): 7337-7344. |
63 | NIU C F, WAN X Y. Engineering a trypsin-resistant thermophilic α-galactosidase to enhance pepsin resistance, acidic tolerance, catalytic performance, and potential in the food and feed industry[J]. Journal of Agricultural and Food Chemistry, 2020, 68(39): 10560-10573. |
64 | NIU C F, LUO H Y, SHI P J, et al. N-glycosylation improves the pepsin resistance of histidine acid phosphatase phytases by enhancing their stability at acidic pHs and reducing pepsin's accessibility to its cleavage sites[J]. Applied and Environmental Microbiology, 2015, 82(4): 1004-1014. |
65 | HU W X, LIU X Y, LI Y F, et al. Rational design for the stability improvement of Armillariella tabescens β-mannanase MAN47 based on N-glycosylation modification[J]. Enzyme and Microbial Technology, 2017, 97: 82-89. |
66 | WANG H, LIN X N, LI S, et al. Rational molecular design for improving digestive enzyme resistance of β-glucosidase from Trichoderma viride based on inhibition of bound state formation[J]. Enzyme and Microbial Technology, 2020, 133: 109465. |
67 | GOLDSMITH M, TAWFIK D S. Enzyme engineering: reaching the maximal catalytic efficiency peak[J]. Current Opinion in Structural Biology, 2017, 47: 140-150. |
68 | PINTO G P, CORBELLA M, DEMKIV A O, et al. Exploiting enzyme evolution for computational protein design[J]. Trends in Biochemical Sciences, 2022, 47(5): 375-389. |
69 | TU T, MENG K, LUO H Y, et al. New insights into the role of T3 loop in determining catalytic efficiency of GH28 endo-polygalacturonases[J]. PLoS One, 2015, 10(9): e0135413. |
70 | TU T, PAN X, MENG K, et al. Substitution of a non-active-site residue located on the T3 loop increased the catalytic efficiency of endo-polygalacturonases[J]. Process Biochemistry, 2016, 51(9): 1230-1238. |
71 | TU T, LI Y Q, LUO Y, et al. A key residue for the substrate affinity enhancement of a thermophilic endo-polygalacturonase revealed by computational design[J]. Applied Microbiology and Biotechnology, 2018, 102(10): 4457-4466. |
72 | YANG H, SHI P J, LIU Y, et al. Loop 3 of fungal endoglucanases of glycoside hydrolase family 12 modulates catalytic efficiency[J]. Applied and Environmental Microbiology, 2017, 83(6): e03123-16. |
73 | ZHENG F, TU T, WANG X Y, et al. Enhancing the catalytic activity of a novel GH5 cellulase Gt Cel5 from Gloeophyllum trabeum CBS 900.73 by site-directed mutagenesis on loop 6[J]. Biotechnology for Biofuels, 2018, 11: 76. |
74 | YU X R, TU T, LUO H Y, et al. Biochemical characterization and mutational analysis of a lactone hydrolase from Phialophora americana [J]. Journal of Agricultural and Food Chemistry, 2020, 68(8): 2570-2577. |
75 | SINGH S, KUMAR K, NATH P, et al. Role of glycine 256 residue in improving the catalytic efficiency of mutant endoglucanase of family 5 glycoside hydrolase from Bacillus amyloliquefaciens SS35[J]. Biotechnology and Bioengineering, 2020, 117(9): 2668-2682. |
76 | WANG L J, CAO K, PEDROSO M M, et al. Sequence- and structure-guided improvement of the catalytic performance of a GH11 family xylanase from Bacillus subtilis [J]. Journal of Biological Chemistry, 2021, 297(5): 101262. |
77 | DONG R Y, LIU X Q, WANG Y R, et al. Fusion of a proline-rich oligopeptide to the C-terminus of a ruminal xylanase improves catalytic efficiency[J]. Bioengineered, 2022, 13(4): 10482-10492. |
78 | RUBIO M V, TERRASAN C R F, CONTESINI F J, et al. Redesigning N-glycosylation sites in a GH3 β-xylosidase improves the enzymatic efficiency[J]. Biotechnology for Biofuels, 2019, 12: 269. |
79 | ZHANG D D, TU T, WANG Y, et al. Improving the catalytic performance of a Talaromyces leycettanus α-amylase by changing the linker length[J]. Journal of Agricultural and Food Chemistry, 2017, 65(24): 5041-5048. |
80 | KIM M K, AN Y J, SONG J M, et al. Structure-based investigation into the functional roles of the extended loop and substrate-recognition sites in an endo-β-1,4-D-mannanase from the Antarctic springtail, Cryptopygus antarcticus [J]. Proteins, 2014, 82(11): 3217-3223. |
81 | WANG K, LUO H Y, TIAN J, et al. Thermostability improvement of a streptomyces xylanase by introducing proline and glutamic acid residues[J]. Applied and Environmental Microbiology, 2014, 80(7): 2158-2165. |
82 | MIN K, KIM H, PARK H J, et al. Improving the catalytic performance of xylanase from Bacillus circulans through structure-based rational design[J]. Bioresource Technology, 2021, 340: 125737. |
83 | REN Y X, LUO H Y, HUANG H Q, et al. Improving the catalytic performance of Proteinase K from Parengyodontium album for use in feather degradation[J]. International Journal of Biological Macromolecules, 2020, 154: 1586-1595. |
84 | CHENG L, QI C L, YANG H X, et al. gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites[J]. Nucleic Acids Research, 2022, 50(D1): D795-D800. |
85 | CAO H, SUN L C, HUANG Y, et al. Structural insights into the dual-substrate recognition and catalytic mechanisms of a bifunctional acetyl ester-xyloside hydrolase from Caldicellulosiruptor lactoaceticus [J]. ACS Catalysis, 2019, 9(3): 1739-1747. |
86 | YOU S, LI J, ZHANG F, et al. Loop engineering of a thermostable GH10 xylanase to improve low-temperature catalytic performance for better synergistic biomass-degrading abilities[J]. Bioresource Technology, 2021, 342: 125962. |
87 | DING Z D, GUAN F F, XU G S, et al. MPEPE, a predictive approach to improve protein expression in E. coli based on deep learning[J]. Computational and Structural Biotechnology Journal, 2022, 20: 1142-1153. |
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