酶工程：从人工设计到人工智能

doi:10.11949/0438-1157.20201941

化工学报 ›› 2021, Vol. 72 ›› Issue (7): 3590-3600.DOI: 10.11949/0438-1157.20201941

酶工程：从人工设计到人工智能

王雅丽¹(),付友思¹,陈俊宏¹,黄佳城¹,廖浪星¹,张永辉⁴,方柏山^1,^2,³()

^1.厦门大学化学化工学院，福建厦门 361005
^2.厦门市合成生物学重点实验室，福建厦门 361005
^3.福建省化学生物学重点实验室，福建厦门 361005
^4.集美大学食品与生物工程学院，福建厦门 361021

收稿日期:2020-12-30 修回日期:2021-04-26 出版日期:2021-07-05 发布日期:2021-07-05
通讯作者: 方柏山
作者简介:王雅丽（1990—），女，博士研究生，yaliwang@stu.xmu.edu.cn
基金资助:
国家自然科学基金项目(21978245)

Enzyme engineering: from artificial design to artificial intelligence

WANG Yali¹(),FU Yousi¹,CHEN Junhong¹,HUANG Jiacheng¹,LIAO Langxing¹,ZHANG Yonghui⁴,FANG Baishan^1,^2,³()

^1.College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China
^2.The Key Laboratory for Synthetic Biotechnology of Xiamen City, Xiamen University, Xiamen 361005, Fujian, China
^3.The Key Laboratory for Chemical Biology of Fujian Province, Xiamen 361005, Fujian, China
^4.College of Food and Biological Engineering, Jimei University, Xiamen 361021, Fujian, China

Received:2020-12-30 Revised:2021-04-26 Online:2021-07-05 Published:2021-07-05
Contact: FANG Baishan

摘要/Abstract

摘要：

计算机在酶工程中的应用使得酶的序列空间探索度不断被扩大。随着不同分子力场参数的建立，涌现出诸多以计算分子能量为基础的算法，并被用于酶的催化活性、稳定性、底物特异性等的改造与筛选。伴随计算机硬件的提升与算法的优化，从头设计全新功能的人工酶取得成功并得以发展。近年来，人工智能在蛋白质结构预测上不断获得突破，同时也被应用到酶的设计中。介绍了分子力场基础和酶设计与筛选的算法，重点阐述了从头设计的方法和成功案例，以及机器学习设计酶的流程和最新的研究进展，展望了人工智能在酶工程领域的未来发展，为酶的改造与全新功能的生物催化剂的设计助力。

关键词: 酶工程, 从头设计, 人工智能, 机理, 能量函数

Abstract:

The application of computers in enzyme engineering has led to the continuous expansion of the sequence space exploration of enzymes. With the establishment of different molecular force fields, many algorithms came out upon computing molecular energy and were applied to the enzyme redesign and screening of catalytic activity, stability, substrate specificity, etc. With the improvement of computer hardware and the optimization of algorithms, artificial enzymes with completely new functions have been successfully designed and developed. In recent years, artificial intelligence has made breakthroughs in protein structure prediction and has also been applied to enzyme design. Basis of molecular force field and enzyme design and selection algorithm were introduced in this paper. The methods and successful cases of de novo design, as well as the process of machine learning to design enzymes and the latest research progress are described emphatically. The outlooks of artificial intelligence in the enzyme engineering are given in the end, contributing for enzymatic engineering and brand-new functional biocatalysts design.

Key words: enzyme engineering, de novo design, artificial intelligence, mechanism, energy function

中图分类号:

Q 814.9

王雅丽,付友思,陈俊宏,黄佳城,廖浪星,张永辉,方柏山. 酶工程：从人工设计到人工智能[J]. 化工学报, 2021, 72(7): 3590-3600.

WANG Yali,FU Yousi,CHEN Junhong,HUANG Jiacheng,LIAO Langxing,ZHANG Yonghui,FANG Baishan. Enzyme engineering: from artificial design to artificial intelligence[J]. CIESC Journal, 2021, 72(7): 3590-3600.

图/表 1

参考文献 119

1	Fischer E. Über Die Glucoside der Alkohole[M]//Untersuchungen Über Kohlenhydrate und Fermente (1884—1908). Berlin, Heidelberg: Springer, 1909: 682-694.
2	Sumner J B. The isolation and crystallization of the enzyme urease: preliminary paper[J]. Journal of Biological Chemistry, 1926, 69(2): 435-441.
3	Northrop J H. Crystalline pepsin(Ⅰ): Isolation and tests of purity[J]. The Journal of General Physiology, 1930, 13(6): 739-766.
4	Stanley W M, Loring H S. The isolation of crystalline tobacco mosaic virus protein from diseased tomato plants[J]. Science, 1936, 83(2143): 85.
5	Sanger F, Tuppy H. The amino-acid sequence in the phenylalanyl chain of insulin(2): The investigation of peptides from enzymic hydrolysates[J]. Biochemical Journal, 1951, 49(4): 481-490.
6	Sanger F, Thompson E O P. The amino-acid sequence in the glycyl chain of insulin(2): The investigation of peptides from enzymic hydrolysates[J]. Biochemical Journal, 1953, 53(3): 366-374.
7	Phillips D C. The hen egg-white lysozyme molecule [J]. PNAS, 1967,57(3): 483-495.
8	Watson H C, Kendrew J C. Comparison between the amino-acid sequences of sperm whale myoglobin and of human hemoglobin[J]. Nature, 1961, 190(4777): 670-672.
9	Koshland D E. The key-lock theory and the induced fit theory[J]. Angewandte Chemie International Edition, 1995, 33(23/24): 2375-2378.
10	Chakraborty P, Di Cera E. Induced fit is a special case of conformational selection[J]. Biochemistry, 2017, 56(22): 2853-2859.
11	Prokop Z, Gora A, Brezovsky J, et al. Engineering of protein tunnels: keyhole-lock-key model for catalysis by the enzymes with active sites[M]//Lutz S, Bornscheuer U T. Protein Engineering Handbook. Wiley-VCH, 2012: 421-464.
12	Hall B G. Regulation of newly evolved enzymes(Ⅳ): Directed evolution of the Ebg repressor[J]. Genetics, 1978, 90(4): 673-681.
13	Chen K, Arnold F H. Tuning the activity of an enzyme for unusual environments: sequential random mutagenesis of subtilisin E for catalysis in dimethylformamide[J]. PNAS, 1993, 90(12): 5618-5622.
14	Ozimek L K, van Hijum S A F T, van Koningsveld G A, et al. Site-directed mutagenesis study of the three catalytic residues of the fructosyltransferases of Lactobacillus reuteri 121[J]. FEBS Letters, 2004, 560(1/2/3): 131-133.
15	Griswold K E, Kawarasaki Y, Ghoneim N, et al. Evolution of highly active enzymes by homology-independent recombination[J]. PNAS, 2005, 102(29): 10082-10087.
16	Mukherjee S. Isolation and purification of industrial enzymes: advances in enzyme technology[M]//Advances in Enzyme Technology. Amsterdam: Elsevier, 2019: 41-70.
17	Bornscheuer U T, Hauer B, Jaeger K E, et al. Directed evolution empowered redesign of natural proteins for the sustainable production of chemicals and pharmaceuticals[J]. Angewandte Chemie International Edition, 2019, 58(1): 36-40.
18	Kohn W. De novo design of α-helical coiled coils and bundles: models for the development of protein-design principles[J]. Trends in Biotechnology, 1998, 16(9): 379-389.
19	Simons K T, Bonneau R, Ruczinski I, et al. Ab initio protein structure prediction of CASP III targets using ROSETTA[J]. Proteins: Structure, Function, and Genetics, 1999, 37(S3): 171-176.
20	Zanghellini A. De novo computational enzyme design[J]. Current Opinion in Biotechnology, 2014, 29: 132-138.
21	Baker D. An exciting but challenging road ahead for computational enzyme design[J]. Protein Science, 2010, 19(10): 1817-1819.
22	Chowdhury R, Maranas C D. From directed evolution to computational enzyme engineering—a review[J]. AIChE Journal, 2020, 66(3): e16847.
23	Hilvert D. Design of protein catalysts[J]. Annual Review of Biochemistry, 2013, 82: 447-470.
24	Pauling L. Chemical achievement and hope for the future[J]. American Scientist, 1948, 36(1): 51.
25	Sheldon R A, Pereira P C. Biocatalysis engineering: the big picture[J]. Chemical Society Reviews, 2017, 46(10): 2678-2691.
26	Mackerell A D. Empirical force fields for biological macromolecules: overview and issues[J]. Journal of Computational Chemistry, 2004, 25(13): 1584-1604.
27	Senn H M, Thiel W. QM/MM methods for biomolecular systems[J]. Angewandte Chemie International Edition, 2009, 48(7): 1198-1229.
28	Apol E, Apostolov R, Berendsen H J C, et al. Gromacs User Manual (Version5.0-rc1)[M]. Sweden: Royal Institute of Technology and Uppsala University, 2014.
29	Brooks B R, Bruccoleri R E, Olafson B D, et al. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations[J]. Journal of Computational Chemistry, 1983, 4(2): 187-217.
30	Cornell W D, Cieplak P, Bayly C I, et al. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules[J]. Journal of the American Chemical Society, 1995, 117(19): 5179-5197.
31	Jorgensen W L, Tirado-Rives J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin[J]. Journal of the American Chemical Society, 1988, 110(6): 1657-1666.
32	Kuhlman B, Baker D. Native protein sequences are close to optimal for their structures [J]. PNAS, 2000, 97(19): 10383-10388.
33	Alford R F, Leaver-Fay A, Jeliazkov J R, et al. The Rosetta all-atom energy function for macromolecular modeling and design[J]. Journal of Chemical Theory and Computation, 2017, 13(6): 3031-3048.
34	Harder E, Damm W, Maple J, et al. OPLS3: a force field providing broad coverage of drug-like small molecules and proteins[J]. Journal of Chemical Theory and Computation, 2016, 12(1): 281-296.
35	Huang J, Rauscher S, Nawrocki G, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins[J]. Nat. Methods, 2017, 14(1): 71-73.
36	Robustelli P, Piana S, Shaw D E. Developing a molecular dynamics force field for both folded and disordered protein states[J].PNAS, 2018, 115(21): E4758-E4766.
37	Anfinsen C B. Principles that govern the folding of protein chains[J]. Science, 1973, 181(4096): 223-230.
38	Koga N, Koga N, Tatsumi-Koga R, et al. Principles for designing ideal protein structures[J]. Nature, 2012, 491(7423): 222-227.
39	Liu Y, Kuhlman B. RosettaDesign server for protein design[J]. Nucleic Acids Res., 2006, 34: W235-W238.
40	Chowdhury R, Ren T, Shankla M, et al. PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore[J]. Nature Communications, 2018, 9(1): 3661.
41	Pantazes R J, Grisewood M J, Li T, et al. The iterative protein redesign and optimization (IPRO) suite of programs[J]. Journal of Computational Chemistry, 2015, 36(4): 251-263.
42	Świderek K, Tuñón I, Moliner V, et al. Computational strategies for the design of new enzymatic functions[J]. Archives of Biochemistry and Biophysics, 2015, 582: 68-79.
43	Schramm V L. Enzymatic transition states, transition-state analogs, dynamics, thermodynamics, and lifetimes[J]. Annual Review of Biochemistry, 2011, 80: 703-732.
44	Schramm V L. Transition states and transition state analogue interactions with enzymes[J]. Accounts of Chemical Research, 2015, 48(4): 1032-1039.
45	Zanghellini A, Jiang L, Wollacott A M, et al. New algorithms and an in silico benchmark for computational enzyme design[J]. Protein Science, 2006, 15(12): 2785-2794.
46	Weitzner B D, Kipnis Y, Daniel A G, et al. A computational method for design of connected catalytic networks in proteins[J]. Protein Science, 2019, 28(12): 2036-2041.
47	Gohlke H, Hendlich M, Klebe G. Knowledge-based scoring function to predict protein-ligand interactions[J]. Journal of Molecular Biology, 2000, 295(2): 337-356.
48	Crick F H C, Kendrew J C. X-Ray analysis and protein structure[J]. Advances in Protein Chemistry, 1957, 12: 133-214.
49	Yee A A, Savchenko A, Ignachenko A, et al. NMR and X-ray crystallography, complementary tools in structural proteomics of small proteins[J]. Journal of the American Chemical Society, 2005, 127(47): 16512-16517.
50	Zhang X, Jin L, Fang Q, et al. 3.3 Å cryo-EM structure of a nonenveloped virus reveals a priming mechanism for cell entry[J]. Cell, 2010, 141(3): 472-482.
51	Berman H M, Westbrook J, Feng Z, et al. The protein data bank[J]. Nucleic Acids Research, 2000, 28(1): 235-242.
52	Voigt C A, Martinez C, Wang Z G, et al. Protein building blocks preserved by recombination[J]. Nature Structural Biology, 2002, 9(7): 553-558.
53	Meyer M M, Hochrein L, Arnold F H. Structure-guided SCHEMA recombination of distantly related β-lactamases[J]. Protein Engineering, Design and Selection, 2006, 19(12): 563-570.
54	Bedbrook C N, Rice A J, Yang K K, et al. Structure-guided SCHEMA recombination generates diverse chimeric channelrhodopsins[J]. Proceedings of the National Academy of Sciences, 2017, 114(13): E2624-E2633.
55	Smith M A, Arnold F H. Noncontiguous SCHEMA protein recombination[J]. Methods in Molecular Biology, 2014, 1179: 345-352.
56	Buchete N V, Straub J, Thirumalai D. Development of novel statistical potentials for protein fold recognition[J]. Current Opinion in Structural Biology, 2004, 14(2): 225-232.
57	Dong Q W, Wang X L, Lin L. Novel knowledge-based mean force potential at the profile level[J]. BMC Bioinformatics, 2006, 7(1): 1-13.
58	Xiong P, Hu X H, Huang B, et al. Increasing the efficiency and accuracy of the ABACUS protein sequence design method[J]. Bioinformatics, 2020, 36(1): 136-144.
59	Kozma D, Tusnády G E. TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool[J]. BMC Bioinformatics, 2015, 16(1): 1-9.
60	Richter F, Leaver-Fay A, Khare S D, et al. De novo enzyme design using Rosetta3[J]. PLoS One, 2011, 6(5): e19230.
61	Leaver-Fay A, Tyka M, Lewis S M, et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules[J]. Methods in Enzymology, 2011, 487: 545-574.
62	Gainza P, Roberts K E, Georgiev I, et al. OSPREY: protein design with ensembles, flexibility, and provable algorithms[J]. Methods in Enzymology, 2013, 523: 87-107.
63	Rackers J A, Wang Z, Lu C, et al. Tinker 8: software tools for molecular design[J]. Journal of Chemical Theory and Computation, 2018, 14(10): 5273-5289.
64	Fischer A, Enkler N, Neudert G, et al. TransCent: computational enzyme design by transferring active sites and considering constraints relevant for catalysis[J]. BMC Bioinformatics, 2009, 10(1): 1-16.
65	Fazelinia H, Cirino P C, Maranas C D. OptGraft: a computational procedure for transferring a binding site onto an existing protein scaffold[J]. Protein Science, 2009, 18(1): 180-195.
66	Grisewood M J, Gifford N P, Pantazes R J, et al. OptZyme: computational enzyme redesign using transition state analogues[J]. PLoS One, 2013, 8(10): e75358.
67	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.
68	Goldenzweig A, Fleishman S J. Principles of protein stability and their application in computational design[J]. Annual Review of Biochemistry, 2018, 87: 105-129.
69	Chen C Y, Georgiev I, Anderson A C, et al. Computational structure-based redesign of enzyme activity[J]. PNAS, 2009, 106(10): 3764-3769.
70	Murphy P M, Bolduc J M, Gallaher J L, et al. Alteration of enzyme specificity by computational loop remodeling and design[J]. PNAS, 2009, 106(23): 9215-9220.
71	Khoury G A, Fazelinia H, Chin J W, et al. Computational design of Candida boidinii xylose reductase for altered cofactor specificity[J]. Protein Science, 2009, 18(10): 2125-2138.
72	García-Guevara F, Bravo I, Martínez-Anaya C, et al. Cofactor specificity switch in Shikimate dehydrogenase by rational design and consensus engineering[J]. Protein Engineering, Design & Selection, 2017, 30(8): 533-541.
73	Cui D B, Zhang L J, Jiang S Q, et al. A computational strategy for altering an enzyme in its cofactor preference to NAD(H) and/or NADP(H)[J]. The FEBS Journal, 2015, 282(12): 2339-2351.
74	Grisewood M J, Hernández-Lozada N J, Thoden J B, et al. Computational redesign of acyl-ACP thioesterase with improved selectivity toward medium-chain-length fatty acids[J]. ACS Catalysis, 2017, 7(6): 3837-3849.
75	Wijma H J, Floor R J, Bjelic S, et al. Enantioselective enzymes by computational design and in silico screening[J]. Angewandte Chemie International Edition, 2015, 54(12): 3726-3730.
76	Cao L, Goreshnik I, Coventry B, et al. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors[J]. Science, 2020, 370(6515): 426-431.
77	Chowdhury R, Allan M F, Maranas C D. OptMAVEn-2.0: de novo design of variable antibody regions against targeted antigen epitopes[J]. Antibodies, 2018, 7(3): 23.
78	Shen H, Fallas J A, Lynch E, et al. De novo design of self-assembling helical protein filaments[J]. Science, 2018, 362(6415): 705-709.
79	King N P, Bale J B, Sheffler W, et al. Accurate design of co-assembling multi-component protein nanomaterials[J]. Nature, 2014, 510(7503): 103-108.
80	Fallas J A, Ueda G, Sheffler W, et al. Computational design of self-assembling cyclic protein homo-oligomers[J]. Nature Chemistry, 2017, 9(4): 353-360.
81	Hsia Y, Bale J B, Gonen S, et al. Corrigendum: design of a hyperstable 60-subunit protein icosahedron[J]. Nature, 2016, 540(7631): 150.
82	Khare S D, Fleishman S J. Emerging themes in the computational design of novel enzymes and protein-protein interfaces[J]. FEBS Letters, 2013, 587(8): 1147-1154.
83	Vaissier Welborn V, Head-Gordon T. Computational design of synthetic enzymes[J]. Chemical Reviews, 2019, 119(11): 6613-6630.
84	Jiang L, Althoff E A, Clemente F R, et al. De novo computational design of retro-aldol enzymes[J]. Science, 2008, 319(5868): 1387-1391.
85	Röthlisberger D, Khersonsky O, Wollacott A M, et al. Kemp elimination catalysts by computational enzyme design[J]. Nature, 2008, 453(7192): 190-195.
86	Ehren J, Govindarajan S, Morón B, et al. Protein engineering of improved prolyl endopeptidases for celiac sprue therapy[J]. Protein Engineering, Design and Selection, 2008, 21(12): 699-707.
87	Wijma H J, Fürst M J L J, Janssen D B. A computational library design protocol for rapid improvement of protein stability: FRESCO[J]. Methods in Molecular Biology, 2018, 1685:69-85.
88	Arabnejad H, Dal Lago M, Jekel P A, et al. A robust cosolvent-compatible halohydrin dehalogenase by computational library design[J]. Protein Engineering, Design and Selection, 2017, 30(3): 175-189.
89	Wijma H J, Floor R J, Jekel P A, et al. Computationally designed libraries for rapid enzyme stabilization[J]. Protein Engineering, Design and Selection, 2014, 27(2): 49-58.
90	Romero Pss A, Stone E, Lamb C, et al. SCHEMA-designed variants of human Arginase I and II reveal sequence elements important to stability and catalysis[J]. ACS Synthetic Biology, 2012, 1(6): 221-228.
91	Heinzelman P, Snow C D, Smith M A, et al. SCHEMA recombination of a fungal cellulase uncovers a single mutation that contributes markedly to stability[J]. Journal of Biological Chemistry, 2009, 284(39): 26229-26233.
92	Li R, Wijma H J, Song L, et al. Computational redesign of enzymes for regio- and enantioselective hydroamination[J]. Nature Chemical Biology, 2018, 14(7): 664-670.
93	Lin Y R, Koga N, Tatsumi-Koga R, et al. Control over overall shape and size in de novo designed proteins[J]. PNAS, 2015, 112(40): E5478-E5485.
94	Geiger-Schuller K, Sforza K, Yuhas M, et al. Extreme stability in de novo-designed repeat arrays is determined by unusually stable short-range interactions[J]. PNAS, 2018, 115(29): 7539-7544.
95	Lu P, Min D, DiMaio F, et al. Accurate computational design of multipass transmembrane proteins[J]. Science, 2018, 359(6379): 1042-1046.
96	Silva D A, Yu S, Ulge U Y, et al. De novo design of potent and selective mimics of IL-2 and IL-15[J]. Nature, 2019, 565(7738): 186-191.
97	Huang P S, Boyken S E, Baker D. The coming of age of de novo protein design[J]. Nature, 2016, 537(7620): 320-327.
98	Khersonsky O, Röthlisberger D, Dym O, et al. Evolutionary optimization of computationally designed enzymes: Kemp eliminases of the KE07 series[J]. Journal of Molecular Biology, 2010, 396(4): 1025-1042.
99	Khersonsky O, Röthlisberger D, Wollacott A M, et al. Optimization of the in-silico-designed Kemp eliminase KE70 by computational design and directed evolution[J]. Journal of Molecular Biology, 2011, 407(3): 391-412.
100	Khersonsky O, Kiss G, Röthlisberger D, et al. Bridging the gaps in design methodologies by evolutionary optimization of the stability and proficiency of designed Kemp eliminase KE59[J]. PNAS, 2012, 109(26): 10358-10363.
101	Blomberg R, Kries H, Pinkas D M, et al. Precision is essential for efficient catalysis in an evolved Kemp eliminase[J]. Nature, 2013, 503(7476): 418-421.
102	Siegel J B, Zanghellini A, Lovick H M, et al. Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction[J]. Science, 2010, 329(5989): 309-313.
103	Eiben C B, Siegel J B, Bale J B, et al. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players[J]. Nat. Biotechnol., 2012, 30(2): 190-192.
104	Lapidoth G, Khersonsky O, Lipsh R, et al. Highly active enzymes by automated combinatorial backbone assembly and sequence design[J]. Nature Communications, 2018, 9(1): 2780.
105	Der B S, Edwards D R, Kuhlman B. Catalysis by a de novo zinc-mediated protein interface: implications for natural enzyme evolution and rational enzyme engineering[J]. Biochemistry, 2012, 51(18): 3933-3940.
106	Schwizer F, Okamoto Y, Heinisch T, et al. Artificial metalloenzymes: reaction scope and optimization strategies[J]. Chemical Reviews, 2018, 118(1): 142-231.
107	Skander M, Humbert N, Collot J, et al. Artificial metalloenzymes: (strept)avidin as host for enantioselective hydrogenation by achiral biotinylated Rhodium-Diphosphine complexes[J]. Journal of the American Chemical Society, 2004, 126(44): 14411-14418.
108	Studer S, Hansen D A, Pianowski Z L, et al. Evolution of a highly active and enantiospecific metalloenzyme from short peptides[J]. Science, 2018, 362(6420): 1285-1288.
109	Bos J, Fusetti F, Driessen A J M, et al. Enantioselective artificial metalloenzymes by creation of a novel active site at the protein dimer interface[J]. Angewandte Chemie International Edition, 2012, 51(30): 7472-7475.
110	Bos J, Roelfes G. Artificial metalloenzymes for enantioselective catalysis[J]. Current Opinion in Chemical Biology, 2014, 19: 135-143.
111	Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
112	Mazurenko S, Prokop Z, Damborsky J. Machine learning in enzyme engineering[J]. ACS Catalysis, 2020, 10(2): 1210-1223.
113	Xu J. Distance-based protein folding powered by deep learning[J]. PNAS, 2019, 116(34): 16856-16865.
114	Siedhoff N E, Schwaneberg U, Davari M D. Machine learning-assisted enzyme engineering[J]. Methods in Enzymology, 2020, 643: 281-315.
115	Alley E C, Khimulya G, Biswas S, et al. Unified rational protein engineering with sequence-based deep representation learning[J]. Nature Methods, 2019, 16(12): 1315-1322.
116	Tallorin L, Wang J, Kim W E, et al. Discovering de novo peptide substrates for enzymes using machine learning[J]. Nature Communications, 2018, 9(1): 5253.
117	Yang K K, Wu Z, Bedbrook C N, et al. Learned protein embeddings for machine learning[J]. Bioinformatics, 2018, 34(15): 2642-2648.
118	Wu Z, Kan S B J, Lewis R D, et al. Machine learning-assisted directed protein evolution with combinatorial libraries[J]. PNAS, 2019, 116(18): 8852-8858.
119	Yang J Y, Anishchenko I, Park H, et al. Improved protein structure prediction using predicted interresidue orientations[J]. PNAS, 2020, 117(3): 1496-1503.

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酶工程：从人工设计到人工智能

Enzyme engineering: from artificial design to artificial intelligence

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