工业蛋白质构效关系的计算生物学解析
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摘要
工业催化用酶已经成为现代生物制造技术的核心"芯片"。不断设计和研发新型高效的酶催化剂是发展工业生物技术的关键。工业催化剂创新设计的科学基础是对酶与底物的相互作用、结构与功能关系及其调控机制的深入剖析。随着生物信息学和智能计算技术的发展,可以通过计算的方法解析酶的催化反应机理,进而对其结构的特定区域进行理性重构,实现酶催化性能的定向设计与改造,促进其工业应用。聚焦工业酶结构-功能关系解析的计算模拟和理性设计,已成为工业酶高效创制改造不可或缺的关键技术。本文就各种计算方法和设计策略以及未来发展趋势进行简要介绍和讨论。
Industrial enzymes have become the core "chip" for bio-manufacturing technology. Design and development of novel and efficient enzymes is the key to the development of industrial biotechnology. The scientific basis for the innovative design of industrial catalysts is an in-depth analysis of the structure-activity relationship between enzymes and substrates, as well as their regulatory mechanisms. With the development of bioinformatics and computational technology, the catalytic mechanism of the enzyme can be solved by various calculation methods. Subsequently, the specific regions of the structure can be rationally reconstructed to improve the catalytic performance, which will further promote the industrial application of the target enzyme. Computational simulation and rational design based on the analysis of the structure-activity relationship have become the crucial technology for the preparation of high-efficiency industrial enzymes. This review provides a brief introduction and discussion on various calculation methods and design strategies as well as future trends.
Industrial enzymes have become the core "chip" for bio-manufacturing technology. Design and development of novel and efficient enzymes is the key to the development of industrial biotechnology. The scientific basis for the innovative design of industrial catalysts is an in-depth analysis of the structure-activity relationship between enzymes and substrates, as well as their regulatory mechanisms. With the development of bioinformatics and computational technology, the catalytic mechanism of the enzyme can be solved by various calculation methods. Subsequently, the specific regions of the structure can be rationally reconstructed to improve the catalytic performance, which will further promote the industrial application of the target enzyme. Computational simulation and rational design based on the analysis of the structure-activity relationship have become the crucial technology for the preparation of high-efficiency industrial enzymes. This review provides a brief introduction and discussion on various calculation methods and design strategies as well as future trends.
引文
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[14] Park SB, Bae DW, Clavio NAB, et al. Structural and biochemical characterization of the curcumin-reducing activity of CurA from Vibrio vulnificus. J Agric Food Chem, 2018, 66(40):10608–10616.
[15] Ren WW, Pengelly R, Farren-Dai M, et al. Revealing the mechanism for covalent inhibition of glycoside hydrolases by carbasugars at an atomic level. Nat Commun, 2018, 9(1):3243.
[16] Fan FF, Chen NH, Wang YH, et al. QM/MM and MM MD simulations on the pyrimidine-specific nucleoside hydrolase:a comprehensive understanding of enzymatic hydrolysis of uridine. J Phys Chem B, 2018, 122(3):1121–1131.
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[19] Mhashal AR, Vardi-Kilshtain A, Kohen A, et al. The role of the Met20 loop in the hydride transfer in Escherichia coli dihydrofolate reductase. J Biol Chem, 2017,292(34):14229–14239.
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[23] Haatveit KC, Garcia-Borràs M, Houk KN.Computational protocol to understand P450 mechanisms and design of efficient and selective biocatalysts. Front Chem, 2019, 6:663.
[24] Faponle AS, Quesne MG, De Visser SP. Origin of the regioselective fatty-acid hydroxylation versus decarboxylation by a cytochrome P450 peroxygenase:what drives the reaction to biofuel production?Chemistry, 2016, 22(16):5478–5483.
[25] Zhou HY, Wang BJ, Wang F, et al. Chemo-and regioselective dihydroxylation of benzene to hydroquinone enabled by engineered cytochrome P450monooxygenase. Angew Chem Int Ed, 2019, 58(3):764–768.
[26]?widerek K, Tu?ón I, Williams IH, et al. Insights on the origin of catalysis on glycine N-methyltransferase from computational modeling. J Am Chem Soc, 2018,140(12):4327–4334.
[27] Marques SM, Dunajova Z, Prokop Z, et al. Catalytic cycle of haloalkane dehalogenases toward unnatural substrates explored by computational modeling. J Chem Inf Model, 2017, 57(8):1970–1989.
[28] Wang BJ, Cao ZX, Rovira C, et al. Fenton-derived OH radicals enable the MPnS enzyme to convert2-hydroxyethylphosphonate to methylphosphonate:insights from Ab initio QM/MM MD simulations. J Am Chem Soc, 2019, 141(23):9284–9291.
[29] Sheng X, Plasch K, Payer SE, et al. Reaction mechanism and substrate specificity of Iso-orotate decarboxylase:a combined theoretical and experimental study. Front Chem, 2018, 6:608.
[30] Behiry EM, Ruiz-Pernia JJ, Luk L, et al. Isotope substitution of promiscuous alcohol dehydrogenase reveals the origin of substrate preference in the transition state. Angew Chem Int Ed, 2018, 57(12):3128–3131.
[31] Liu J, Wu SK, Li Z. Recent advances in enzymatic oxidation of alcohols. Curr Opin Chem Biol, 2018, 43:77–86.
[32] Kong XD, Yuan SG, Li L, et al. Engineering of an epoxide hydrolase for efficient bioresolution of bulky pharmaco substrates. Proc Natl Acad Sci USA, 2014,111(44):15717–15722.
[33] Herter S, Medina F, Wagschal S, et al. Mapping the substrate scope of monoamine oxidase(MAO-N)as a synthetic tool for the enantioselective synthesis of chiral amines. Bioorg Med Chem, 2018, 26(7):1338–1346.
[34] Li GY, Yao PY, Gong R, et al. Simultaneous engineering of an enzyme’s entrance tunnel and active site:the case of monoamine oxidase MAO-N. Chem Sci, 2017, 8(5):4093–4099.
[35] Collazo L, Klinman JP. Control of the position of oxygen delivery in soybean lipoxygenase-1 by amino acid side chains within a gas migration channel. J Biol Chem, 2016, 291(17):9052–9059.
[36] Han SW, Kim J, Cho HS, et al. Active site engineering ofω-transaminase guided by docking orientation analysis and virtual activity screening. ACS Catal, 2017,7(6):3752–3762.
[37] Mader SL, Br?uer A, Groll M, et al. Catalytic mechanism and molecular engineering of quinolone biosynthesis in dioxygenase AsqJ. Nat Commun, 2018,9(1):1168.
[38] Dijkman WP, Binda C, Fraaije MW, et al. Structurebased enzyme tailoring of 5-hydroxymethylfurfural oxidase. ACS Catal, 2015, 5(3):1833–1839.
[39] Ferrari AR, Lee M, Fraaije MW. Expanding the substrate scope of chitooligosaccharide oxidase from Fusarium graminearum by structure-inspired mutagenesis.Biotechnol Bioeng, 2015, 112(6):1074–1080.
[40] Zheng GW, Liu YY, Chen Q, et al. Preparation of structurally diverse chiral alcohols by engineering ketoreductase CgKR1. ACS Catal, 2017, 7(10):7174–7181.
[41] Chen Q, Luan ZJ, Cheng XL, et al. Molecular dynamics investigation of the substrate binding mechanism in carboxylesterase. Biochemistry, 2015, 54(9):1841–1848.
[42] Chen FF, Zheng GW, Liu L, et al. Reshaping the active pocket of amine dehydrogenases for asymmetric synthesis of bulky aliphatic amines. ACS Catal, 2018,8(3):2622–2628.
[43] Maria-Solano MA, Serrano-Hervás E, Romero-Rivera A,et al. Role of conformational dynamics in the evolution of novel enzyme function. Chem Commun(Camb),2018, 54(50):6622–6634.
[44] Romero-Rivera A, Garcia-Borràs M, Osuna S.Computational tools for the evaluation of laboratory-engineered biocatalysts. Chem Commun(Camb), 2016, 53(2):284–297.
[45] Wijma HJ, Marrink SJ, Janssen DB. Computationally efficient and accurate enantioselectivity modeling by clusters of molecular dynamics simulations. J Chem Inf Model, 2014, 54(7):2079–2092.
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