中国化学工程学报 ›› 2022, Vol. 41 ›› Issue (1): 6-21.DOI: 10.1016/j.cjche.2021.08.017
Ziheng Cui, Shiding Zhang, Shengyu Zhang, Biqiang Chen, Yushan Zhu, Tianwei Tan
收稿日期:
2021-07-06
修回日期:
2021-08-15
出版日期:
2022-01-28
发布日期:
2022-02-25
通讯作者:
Yushan Zhu,E-mail address:zhuys@mail.buct.edu.cn;Tianwei Tan,E-mail address:twtan@mail.buct.edu.cn
基金资助:
Ziheng Cui, Shiding Zhang, Shengyu Zhang, Biqiang Chen, Yushan Zhu, Tianwei Tan
Received:
2021-07-06
Revised:
2021-08-15
Online:
2022-01-28
Published:
2022-02-25
Contact:
Yushan Zhu,E-mail address:zhuys@mail.buct.edu.cn;Tianwei Tan,E-mail address:twtan@mail.buct.edu.cn
Supported by:
摘要: Biomanufacturing, which uses renewable resources as raw materials and uses biological processes to produce energy and chemicals, has long been regarded as a production model that replaces the unsustainable fossil economy. The construction of non-natural and efficient biosynthesis routes of chemicals is an important goal of green biomanufacturing. Traditional methods that rely on experience are difficult to support the realization of this goal. However, with the rapid development of information technology, the intelligence of biomanufacturing has brought hope to achieve this goal. Retrobiosynthesis and computational enzyme design, as two of the main technologies in intelligent biomanufacturing, have developed rapidly in recent years and have made great achievements and some representative works have demonstrated the great value that the integration of the two fields may bring. To achieve the final integration of the two fields, it is necessary to examine the information, methods and tools from a bird’s-eye view, and to find a feasible idea and solution for establishing a connection point. For this purpose, this article briefly reviewed the main ideas, methods and tools of the two fields, and put forward views on how to achieve the integration of the two fields.
Ziheng Cui, Shiding Zhang, Shengyu Zhang, Biqiang Chen, Yushan Zhu, Tianwei Tan. Green biomanufacturing promoted by automatic retrobiosynthesis planning and computational enzyme design[J]. 中国化学工程学报, 2022, 41(1): 6-21.
Ziheng Cui, Shiding Zhang, Shengyu Zhang, Biqiang Chen, Yushan Zhu, Tianwei Tan. Green biomanufacturing promoted by automatic retrobiosynthesis planning and computational enzyme design[J]. Chinese Journal of Chemical Engineering, 2022, 41(1): 6-21.
[1] T. Werpy, G. Petersen, Top value added chemicals from biomass:volume I-results of screening for potential candidates from sugars and synthesis gas, National Renewable Energy Lab., Golden, CO (US), 2004. [2] J. Becker, C. Wittmann, Advanced biotechnology:metabolically engineered cells for the bio-based production of chemicals and fuels, materials, and health-care products, Angew. Chem. Int. Ed. Engl. 54(11)(2015)3328-3350. [3] K.-A. Baritugo, H.T. Kim, Y.C. David, J.H. Choi, J.-I. Choi, T.W. Kim, C. Park, S.H. Hong, J.-G. Na, K.J. Jeong, J.C. Joo, S.J. Park, Recent advances in metabolic engineering of Corynebacterium glutamicumas a potential platform microorganism for biorefinery, Biofuels, Bioprod. Bioref. 12(5)(2018)899-925. [4] Y. Zhang, J. Yu, Y. Wu, M. Li, Y. Zhao, H. Zhu, C. Chen, M. Wang, B. Chen, T. Tan, Efficient production of chemicals from microorganism by metabolic engineering and synthetic biology, Chin. J. Chem. Eng. 30(2021)14-28. [5] M.P. Thompson, I. Peñafiel, S.C. Cosgrove, N.J. Turner, Biocatalysis using immobilized enzymes in continuous flow for the synthesis of fine chemicals, Org. Process Res. Dev. 23(1)(2019)9-18. [6] R.A. Sheldon, D. Brady, Streamlining design, engineering, and applications of enzymes for sustainable biocatalysis, ACS Sustainable Chem. Eng. 9(24)(2021) 8032-8052. [7] X.R. Zhao, Systems and synthetic biology-aided biosynthesis pathway design, in:Systems and Synthetic Metabolic Engineering, Elsevier, Amsterdam, 2020, pp. 51-75. [8] B.O. Bachmann, Biosynthesis:is it time to go retro?, Nature chemical biology 6 (6)(2010)390-393 [9] N.J. Turner, E. O'Reilly, Biocatalytic retrosynthesis, Nat. Chem. Biol. 9(5)(2013) 285-288. [10] E.J. Corey, General methods for the construction of complex molecules, Pure Appl. Chem. 14(1)(1967)19-38. [11] E.J. Corey, L. Kurti, Enantioselective Chemical Synthesis:Methods, Logic, and Practice, Elsevier, Amsterdam, 2013. [12] A. Wołos, R. Roszak, A. Żądło-Dobrowolska, W. Beker, B. Mikulak-Klucznik, G. Spólnik, M. Dygas, S. Szymkuć, B.A. Grzybowski, Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry, Science 369(6511)(2020) eaaw1955. [13] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature 559(7715)(2018)547-555. [14] A. Button, D. Merk, J.A. Hiss, G. Schneider, Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis, Nat. Mach. Intell. 1(7)(2019)307-315. [15] J. Pottel, N. Moitessier, Customizable generation of synthetically accessible, local chemical subspaces, J. Chem. Inf. Model. 57(3)(2017)454-467. [16] M.H.S. Segler, M. Preuss, M.P. Waller, Planning chemical syntheses with deep neural networks and symbolic AI, Nature 555(7698)(2018)604-610. [17] W. Finnigan, L.J. Hepworth, S.L. Flitsch, N.J. Turner, RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades, Nat. Catal. 4(2)(2021)98-104. [18] R.O.M.A. de Souza, L.S.M. Miranda, U.T. Bornscheuer, A retrosynthesis approach for biocatalysis in organic synthesis, Chemistry 23(50)(2017) 12040-12063. [19] N.J. Turner, L. Humphreys, Biocatalysis in organic synthesis:the retrosynthesis approach, Focus Catal. 2018(1)(2018)7. [20] P. Gainza-Cirauqui, B.E. Correia, Computational protein design-the next generation tool to expand synthetic biology applications, Curr. Opin. Biotechnol. 52(2018)145-152. [21] J. Planas-Iglesias, S.M. Marques, G.P. Pinto, M. Musil, J. Stourac, J. Damborsky, D. Bednar, Computational design of enzymes for biotechnological applications, Biotechnol. Adv. 47(2021)107696. [22] S.D. Khare, S.J. Fleishman, Emerging themes in the computational design of novel enzymes and protein-protein interfaces, FEBS Lett. 587(8)(2013) 1147-1154. [23] P. Dušan, K. Shina Caroline Lynn, Molecular modeling of conformational dynamics and its role in enzyme evolution, Curr. Opin. Struct. Biol. 52(2018) 50-57. [24] M. Musil, H. Konegger, J. Hon, D. Bednar, J. Damborsky, Computational design of stable and soluble biocatalysts, ACS Catal. 9(2)(2019)1033-1054. [25] C.M. Miton, S. Jonas, G. Fischer, F. Duarte, M.F. Mohamed, B. van Loo, B. Kintses, S.C.L. Kamerlin, N. Tokuriki, M. Hyvönen, F. Hollfelder, Evolutionary repurposing of a sulfatase:a new Michaelis complex leads to efficient transition state charge offset, Proc. Natl. Acad. Sci. USA 115(31)(2018) E7293-E7302. [26] H. Yim, R. Haselbeck, W. Niu, C. Pujol-Baxley, A. Burgard, J. Boldt, J. Khandurina, J.D. Trawick, R.E. Osterhout, R. Stephen, J. Estadilla, S. Teisan, H.B. Schreyer, S. Andrae, T.H. Yang, S.Y. Lee, M.J. Burk, S. Van Dien, Metabolic engineering of Escherichia coli for direct production of 1, 4-butanediol, Nat. Chem. Biol. 7(7)(2011)445-452. [27] W.R. Birmingham, C.A. Starbird, T.D. Panosian, D.P. Nannemann, T.M. Iverson, B.O. Bachmann, Bioretrosynthetic construction of a didanosine biosynthetic pathway, Nat. Chem. Biol. 10(5)(2014)392-399. [28] T. Schwander, L. Schada von Borzyskowski, S. Burgener, N.S. Cortina, T.J. Erb, A synthetic pathway for the fixation of carbon dioxide in vitro, Science 354 (6314)(2016)900-904. [29] J.B. Siegel, A.L. Smith, S. Poust, A.J. Wargacki, A. Bar-Even, C. Louw, B.W. Shen, C.B. Eiben, H.M. Tran, E. Noor, J.L. Gallaher, J. Bale, Y. Yoshikuni, M.H. Gelb, J.D. Keasling, B.L. Stoddard, M.E. Lidstrom, D. Baker, Computational protein design enables a novel one-carbon assimilation pathway, Proc. Natl. Acad. Sci. 112 (12)(2015)3704-3709. [30] A.J.M. Ribeiro, G.L. Holliday, N. Furnham, J.D. Tyzack, K. Ferris, J.M. Thornton, Mechanism and Catalytic Site Atlas (M-CSA):a database of enzyme reaction mechanisms and active sites, Nucleic Acids Res. 46(D1)(2018) D618-D623. [31] T. Dinmukhamed, Z. Huang, Y. Liu, X. Lv, J. Li, G. Du, L. Liu, Current advances in design and engineering strategies of industrial enzymes, Syst. Microbiol. Biomanufacturing 1(1)(2021)15-23. [32] J. Damborsky, J. Brezovsky, Computational tools for designing and engineering enzymes, Curr. Opin. Chem. Biol. 19(2014)8-16. [33] A. Madhavan, K.B. Arun, P. Binod, R. Sirohi, A. Tarafdar, R. Reshmy, M. Kumar Awasthi, R. Sindhu, Design of novel enzyme biocatalysts for industrial bioprocess:Harnessing the power of protein engineering, high throughput screening and synthetic biology, Bioresour. Technol. 325(2021)124617. [34] T.L. Coates, N. Young, A.J. Jarrett, C.J. Morris, J.D. Moody, D.D. Corte, Current computational methods for enzyme design, Mod. Phys. Lett. B 35(09)(2021) 2150155. [35] W. Finnigan, S.L. Flitsch, L.J. Hepworth, N.J. Turner, 2 Enzyme Cascade Design: Retrosynthesis, Enzyme Cascade Design Modelling (2021)7. [36] S.P. France, L.J. Hepworth, N.J. Turner, S.L. Flitsch, Constructing biocatalytic cascades:in vitro and in vivo approaches to de novo multi-enzyme pathways, ACS Catal. 7(1)(2017)710-724. [37] B. Kuhlman, P. Bradley, Advances in protein structure prediction and design, Nat. Rev. Mol. Cell Biol. 20(11)(2019)681-697. [38] G.-M. Lin, R. Warden-Rothman, C.A. Voigt, Retrosynthetic design of metabolic pathways to chemicals not found in nature, Curr. Opin. Syst. Biol. 14(2019) 82-107. [39] A.J. Lawson, J. Swienty-Busch, T. Géoui, D. Evans, in:The making of reaxys-towards unobstructed access to relevant chemistry information. ACS Symposium Series, American Chemical Society, Washington, DC, 2014, pp. 127-148. [40] S.W. Gabrielson, SciFinder, J. Med. Libr. Assoc. 106(4)(2018)588-590. [41] H.E. Pence, A. Williams, ChemSpider:an online chemical information resource, J. Chem. Educ. 87(11)(2010)1123-1124. [42] D.L. Roth, SPRESIweb 2.1, a selective chemical synthesis and reaction database, J. Chem. Inf. Model. 45(5)(2005)1470-1473. [43] M. Kanehisa, S. Goto, KEGG:Kyoto encyclopedia of genes and genomes, Nucleic Acids Res. 28(1)(2000)27-30. [44] S. Moretti, O. Martin, T. van Du Tran, A. Bridge, A. Morgat, M. Pagni, MetaNetX/MNXref:reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks, Nucl. Acids Res 44(D1) (2016) D523-D526. [45] T. Lombardot, A. Morgat, K.B. Axelsen, L. Aimo, N. Hyka-Nouspikel, A. Niknejad, A. Ignatchenko, I. Xenarios, E. Coudert, N. Redaschi, A. Bridge, Updates in Rhea:SPARQLing biochemical reaction data, Nucleic Acids Res. 47 (D1)(2019) D596-D600. [46] E. Corey, A. Long, S. Rubenstein, Computer-assisted analysis in organic synthesis, Science 228(4698)(1985)408-418. [47] J. Law, Z. Zsoldos, A. Simon, D. Reid, Y. Liu, S.Y. Khew, A.P. Johnson, S. Major, R. A. Wade, H.Y. Ando, Route designer:a retrosynthetic analysis tool utilizing automated retrosynthetic rule generation, J. Chem. Inf. Model. 49(3)(2009) 593-602. [48] A. Bøgevig, H.J. Federsel, F. Huerta, M.G. Hutchings, H. Kraut, T. Langer, P. Löw, C. Oppawsky, T. Rein, H. Saller, Route design in the 21st century:the ICSYNTH software tool as an idea generator for synthesis prediction, Org. Process Res. Dev. 19(2)(2015)357-368. [49] C.W. Coley, R. Barzilay, T.S. Jaakkola, W.H. Green, K.F. Jensen, Prediction of organic reaction outcomes using machine learning, ACS Cent. Sci. 3(5)(2017) 434-443. [50] C.D. Christ, M. Zentgraf, J.M. Kriegl, Mining electronic laboratory notebooks: analysis, retrosynthesis, and reaction based enumeration, J. Chem. Inf. Model. 52(7)(2012)1745-1756. [51] M.H.S. Segler, M.P. Waller, Neural-symbolic machine learning for retrosynthesis and reaction prediction, Chemistry 23(25)(2017)5966-5971. [52] O. Khersonsky, C. Roodveldt, D.S. Tawfik, Enzyme promiscuity:evolutionary and mechanistic aspects, Curr. Opin. Chem. Biol. 10(5)(2006)498-508. [53] M. Koch, T. Duigou, J.-L. Faulon, Reinforcement learning for bioretrosynthesis, ACS Synth. Biol. 9(1)(2020)157-168. [54] D. Fooshee, A. Andronico, P. Baldi, ReactionMap:an efficient atom-mapping algorithm for chemical reactions, J. Chem. Inf. Model. 53(11)(2013)2812-2819. [55] M. Latendresse, J.P. Malerich, M. Travers, P.D. Karp, Accurate atom-mapping computation for biochemical reactions, J. Chem. Inf. Model. 52(11)(2012) 2970-2982. [56] S.A. Rahman, G. Torrance, L. Baldacci, S. Martínez Cuesta, F. Fenninger, N. Gopal, S. Choudhary, J.W. May, G.L. Holliday, C. Steinbeck, J.M. Thornton, Reaction Decoder Tool (RDT):extracting features from chemical reactions, Bioinformatics 32(13)(2016)2065-2066. [57] N. Osório, P. Vilaça, M. Rocha, A critical evaluation of automatic atom mapping algorithms and tools, in:F. Fdez-Riverola, M.S. Mohamad, M. Rocha, J.F. De Paz, T. Pinto (Eds.)11th International Conference on Practical Applications of Computational Biology&Bioinformatics, Springer International Publishing, Cham, 2017, pp. 257-264. [58] V. Hatzimanikatis, C. Li, J.A. Ionita, C.S. Henry, M.D. Jankowski, L.J. Broadbelt, Exploring the diversity of complex metabolic networks, Bioinformatics 21(8) (2005)1603-1609. [59] I. Otero-Muras, P. Carbonell, Automated engineering of synthetic metabolic pathways for efficient biomanufacturing, Metab. Eng. 63(2021)61-80. [60] P. Schneider, S. Klamt, Characterizing and ranking computed metabolic engineering strategies, Bioinformatics 35(17)(2019)3063-3072. [61] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of Go with deep neural networks and tree search, Nature 529(7587)(2016)484-489. [62] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, D. Hassabis, Mastering the game of Go without human knowledge, Nature 550(7676)(2017)354-359. [63] C. Li, C.S. Henry, M.D. Jankowski, J.A. Ionita, V. Hatzimanikatis, L.J. Broadbelt, Computational discovery of biochemical routes to specialty chemicals, Chem. Eng. Sci. 59(22-23)(2004)5051-5060. [64] T. Duigou, M. du Lac, P. Carbonell, J.L. Faulon, RetroRules:a database of reaction rules for engineering biology, Nucleic Acids Res. 47(d1)(2019) D1229-D1235. [65] A. Kumar, P.F. Suthers, C.D. Maranas, MetRxn:a knowledgebase of metabolites and reactions spanning metabolic models and databases, BMC Bioinf. 13(2012)6. [66] J.G. Jeffryes, R.L. Colastani, M. Elbadawi-Sidhu, T. Kind, T.D. Niehaus, L.J. Broadbelt, A.D. Hanson, O. Fiehn, K.E. Tyo, C.S. Henry, MINEs:open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics, J. Cheminform 7(2015)44. [67] Z. Ni, A.E. Stine, K.E.J. Tyo, L.J. Broadbelt, Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design, Metab. Eng. 65(2021)79-87. [68] Z.N. Liu, X. Zhang, D.W. Lei, B. Qiao, G.R. Zhao, Metabolic engineering of Escherichia coli for de novo production of 3-phenylpropanol via retrobiosynthesis approach, Microb. Cell Fact. 20(1)(2021)121. [69] J. Hafner, J. Payne, H. MohammadiPeyhani, V. Hatzimanikatis, C. Smolke, A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives, Nat. Commun. 12(1)(2021)1760. [70] H. Liu, K.R.M. Ramos, K.N. Valdehuesa, G.M. Nisola, W.-K. Lee, W.-J. Chung, Biosynthesis of ethylene glycol in Escherichia coli, Appl. Microbiol. Biotechnol. 97(8)(2013)3409-3417. [71] D. Wu, Q. Wang, R.S. Assary, L.J. Broadbelt, G. Krilov, A computational approach to design and evaluate enzymatic reaction pathways:application to 1-butanol production from pyruvate, J. Chem. Inf. Model. 51(7)(2011)1634-1647. [72] A. Stine, M. Zhang, S. Ro, S. Clendennen, M.C. Shelton, K.E.J. Tyo, L.J. Broadbelt, Exploring De Novo metabolic pathways from pyruvate to propionic acid, Biotechnol. Prog. 32(2)(2016)303-311. [73] A. Vila-Santa, M.A. Islam, F.C. Ferreira, K.L.J. Prather, N.P. Mira, Prospecting biochemical pathways to implement microbe-based production of the newto-nature platform chemical levulinic acid, ACS Synth. Biol. 10(4)(2021)724-736. [74] M.A. Islam, N. Hadadi, M. Ataman, V. Hatzimanikatis, G. Stephanopoulos, Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas, Metab. Eng. 41(2017)173-181. [75] W.M.C.d. Silva, J.L. Andersen, M.T. Holanda, M.E.M.T. Walter, M.M. Brigido, P. F. Stadler, C. Flamm, Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks, Processes 7(4)(2019). [76] T.J. Erb, P.R. Jones, A. Bar-Even, Synthetic metabolism:metabolic engineering meets enzyme design, Curr. Opin. Chem. Biol. 37(2017)56-62. [77] R. Wolfenden, M.J. Snider, The depth of chemical time and the power of enzymes as catalysts, Acc. Chem. Res. 34(12)(2001)938-945. [78] A. Sharma, G. Gupta, T. Ahmad, S. Mansoor, B. Kaur, Enzyme engineering: current trends and future perspectives, Food Rev. Int. 37(2)(2021)121-154. [79] T. Warnecke, R.T. Gill, Organic acid toxicity, tolerance, and production in Escherichia coli biorefining applications, Microb. Cell Fact. 4(2005)25. [80] N. Maity, S. Barman, Y. Minenkov, S. Ould-Chikh, E. Abou-Hamad, T. Ma, Z.S. Qureshi, L. Cavallo, V. D'Elia, B.C. Gates, J.-M. Basset, A silica-supported monoalkylated tungsten dioxo complex catalyst for olefin metathesis, ACS Catal. 8(4)(2018)2715-2729. [81] W.S. Mak, J.B. Siegel, Computational enzyme design:transitioning from catalytic proteins to enzymes, Curr. Opin. Struct. Biol. 27(2014)87-94. [82] S. Polydorides, E. Michael, D. Mignon, K. Druart, G. Archontis, T. Simonson, Proteus and the Design of Ligand Binding Sites, in:B.L. Stoddard (Ed.), Computational Design of Ligand Binding Proteins, Springer, New York, 2016, pp. 77-97. [83] G. Kiss, N. Çelebi-Ölçüm, R. Moretti, D. Baker, K.N. Houk, Computational Enzyme Design, Angew. Chem. Int. Ed. 52(22)(2013)5700-5725. [84] B.I. Dahiyat, S.L. Mayo, Protein design automation, Protein Sci. 5(5)(1996) 895-903. [85] R. Bonneau, J. Tsai, I. Ruczinski, D. Baker, Functional inferences from blind ab initio protein structure predictions, J. Struct. Biol. 134(2-3)(2001)186-190. [86] R.L. Dunbrack, Rotamer libraries in the 21st century, Curr. Opin. Struct. Biol. 12 (4)(2002)431-440. [87] A.G. Street, S.L. Mayo, Computational protein design, Structure 7(5)(1999) R105-R109. [88] F.E. Boas, P.B. Harbury, Design of protein-ligand binding based on the molecular-mechanics energy model, J. Mol. Biol. 380(2)(2008)415-424. [89] C.A. Rohl, D. Baker, De novo determination of protein backbone structure from residual dipolar couplings using Rosetta, J. Am. Chem. Soc. 124(11) (2002)2723-2729. [90] J. Desmet, M.D. Maeyer, B. Hazes, I. Lasters, The dead-end elimination theorem and its use in protein side-chain positioning, Nature 356(6369) (1992)539-542. [91] P. Koehl, M. Delarue, Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy, J. Mol. Biol. 239(2)(1994)249-275. [92] P. Koehl, M. Delarue, A self consistent mean field approach to simultaneous gap closure and side-chain positioning in homology modelling, Nat. Struct. Biol. 2(2)(1995)163-170. [93] J. Desmet, J. Spriet, I. Lasters, Fast and accurate side-chain topology and energy refinement (FASTER) as a new method for protein structure optimization, Proteins:Struct. Funct. Genet. 48(1)(2002)31-43. [94] B.D. Allen, S.L. Mayo, Dramatic performance enhancements for the FASTER optimization algorithm, J. Comput. Chem. 27(10)(2006)1071-1075. [95] X. Huang, K. Han, Y. Zhu, Systematic optimization model and algorithm for binding sequence selection in computational enzyme design, Protein Sci. 22 (7)(2013)929-941. [96] S.K. Burley, C. Bhikadiya, C.X. Bi, S. Bittrich, L. Chen, G.V. Crichlow, C.H. Christie, K. Dalenberg, L. di Costanzo, J.M. Duarte, S. Dutta, Z.K. Feng, S. Ganesan, D.S. Goodsell, S. Ghosh, R.K. Green, V. Guranović, D. Guzenko, B.P. Hudson, C.L. Lawson, Y.H. Liang, R. Lowe, H. Namkoong, E. Peisach, I. Persikova, C. Randle, A. Rose, Y. Rose, A. Sali, J. Segura, M. Sekharan, C.H. Shao, Y.P. Tao, M. Voigt, J.D. Westbrook, J.Y. Young, C. Zardecki, M. Zhuravleva, RCSB Protein Data Bank:powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences, Nucleic Acids Res. 49(D1)(2021) D437-D451. [97] M.A. Martí-Renom, A.C. Stuart, A. Fiser, R. Sánchez, F. Melo, A. Šali, Comparative protein structure modeling of genes and genomes, Annu. Rev. Biophys. Biomol. Struct. 29(1)(2000)291-325. [98] D.T. Jones, W.R. Taylor, J.M. Thornton, A new approach to protein fold recognition, Nature 358(6381)(1992)86-89. [99] J. Bowie, R. Luthy, D. Eisenberg, A method to identify protein sequences that fold into a known three-dimensional structure, Science 253(5016)(1991) 164-170. [100] K.T. Simons, C. Kooperberg, E. Huang, D. Baker, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions11Edited by F. E. Cohen, J. Mol. Biol. 268(1)(1997)209-225. [101] A. Liwo, J. Lee, D.R. Ripoll, J. Pillardy, H.A. Scheraga, Protein structure prediction by global optimization of a potential energy function, Proc. Natl. Acad. Sci. USA 96(10)(1999)5482-5485. [102] S.T. Wu, J. Skolnick, Y. Zhang, Ab initio modeling of small proteins by iterative TASSER simulations, BMC Biol. 5(2007)17. [103] J. Lee, P.L. Freddolino, Y. Zhang, Ab Initio Protein Structure Prediction, in:D.J. Rigden (Ed.), From Protein Structure to Function with Bioinformatics, Springer Netherlands, Dordrecht, 2017, pp. 3-35. [104] K. Arnold, L. Bordoli, J. Kopp, T. Schwede, The SWISS-MODEL workspace:a web-based environment for protein structure homology modelling, Bioinformatics 22(2)(2006)195-201. [105] J.Y. Yang, R.X. Yan, A. Roy, D. Xu, J. Poisson, Y. Zhang, The I-TASSER Suite: protein structure and function prediction, Nat. Methods 12(1)(2015)7-8. [106] L.A. Kelley, S. Mezulis, C.M. Yates, M.N. Wass, M.J.E. Sternberg, The Phyre2 web portal for protein modeling, prediction and analysis, Nat. Protoc. 10(6) (2015)845-858. [107] D. Xu, Y. Zhang, Toward optimal fragment generations for ab initio protein structure assembly, Proteins:Struct., Funct., Bioinform. 81(2)(2013)229-239. [108] H. Park, D.E. Kim, S. Ovchinnikov, D. Baker, F. DiMaio, Automatic structure prediction of oligomeric assemblies using Robetta in CASP12, Proteins:Struct. Funct. Bioinform. 86(2018)283-291. [109] D.W. Buchan, F. Minneci, T.C. Nugent, K. Bryson, D.T. Jones, Scalable web services for the PSIPRED Protein Analysis Workbench, Nucleic Acids Res. 41 (web server issue)(2013) W349-W357. [110] J. Ma, S. Wang, F. Zhao, J. Xu, Protein threading using context-specific alignment potential, Bioinformatics 29(13)(2013) i257-i265. [111] W. Zheng, C. Zhang, Y. Li, R. Pearce, E.W. Bell, Y. Zhang, Folding nonhomologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations, Cell Rep Methods 1(3)(2021)100014. [112] J.G. Greener, S.M. Kandathil, D.T. Jones, Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints, Nat. Commun. 10(1)(2019)3977. [113] A.W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C.L. Qin, A. Žídek, A.W.R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D.T. Jones, D. Silver, K. Kavukcuoglu, D. Hassabis, Improved protein structure prediction using potentials from deep learning, Nature 577 (7792)(2020)706-710. [114] N. Guex, M.C. Peitsch, SWISS-MODEL and the Swiss-PdbViewer:an environment for comparative protein modeling, Electrophoresis 18(15) (1997)2714-2723. [115] S. Bienert, A. Waterhouse, T.A. de Beer, G. Tauriello, G. Studer, L. Bordoli, T. Schwede, The SWISS-MODEL Repository-new features and functionality, Nucl. Acids Res. 45(D1)(2017) D313-D319. [116] J. Yang, Y. Zhang, I-TASSER server:new development for protein structure and function predictions, Nucleic Acids Res. 43(W1)(2015) W174-W181. [117] K.T. Simons, C. Kooperberg, E. Huang, D. Baker, Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions, J. Mol. Biol. 268(1)(1997)209-225. [118] D. Xu, Y. Zhang, Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field, Proteins: Struct., Funct., Bioinform. 80(7)(2012)1715-1735. [119] J. Lee, S.-Y. Kim, K. Joo, I. Kim, J. Lee, Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing, Proteins 56(4)(2004)704-714. [120] D.T. Jones, Predicting novel protein folds by using FRAGFOLD, Proteins:Struct., Funct., Bioinform. 45(S5)(2001)127-132. [121] A. Kryshtafovych, T. Schwede, M. Topf, K. Fidelis, J. Moult, Critical assessment of methods of protein structure prediction (CASP)-Round XIII, Proteins 87(12) (2019)1011-1020. [122] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S.A. A. Kohl, A.J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A.W. Senior, K. Kavukcuoglu, P. Kohli, D. Hassabis, Highly accurate protein structure prediction with AlphaFold, Nature 596(7873)(2021)583-589. [123] K. Tunyasuvunakool, J. Adler, Z. Wu, T. Green, M. Zielinski, A. Žídek, A. Bridgland, A. Cowie, C. Meyer, A. Laydon, S. Velankar, G.J. Kleywegt, A. Bateman, R. Evans, A. Pritzel, M. Figurnov, O. Ronneberger, R. Bates, S.A.A. Kohl, A. Potapenko, A.J. Ballard, B. Romera-Paredes, S. Nikolov, R. Jain, E. Clancy, D. Reiman, S. Petersen, A.W. Senior, K. Kavukcuoglu, E. Birney, P. Kohli, J. Jumper, D. Hassabis, Highly accurate protein structure prediction for the human proteome, Nature 596(7873)(2021)590-596. [124] M. Baek, F. DiMaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G.R. Lee, J. Wang, Q. Cong, L.N. Kinch, R.D. Schaeffer, C. Millán, H. Park, C. Adams, C.R. Glassman, A. DeGiovanni, J.H. Pereira, A.V. Rodrigues, A.A. van Dijk, A.C. Ebrecht, D.J. Opperman, T. Sagmeister, C. Buhlheller, T. Pavkov-Keller, M.K. Rathinaswamy, U. Dalwadi, C.K. Yip, J.E. Burke, K.C. Garcia, N.V. Grishin, P.D. Adams, R.J. Read, D. Baker, Accurate prediction of protein structures and interactions using a three-track neural network, Science 373(6557)(2021) 871-876. [125] L.D. Andrews, T.D. Fenn, D. Herschlag, Ground state destabilization by anionic nucleophiles contributes to the activity of phosphoryl transfer enzymes, PLoS Biol. 11(7)(2013) e1001599. [126] D. Roston, Q. Cui, QM/MM analysis of transition states and transition state analogues in metalloenzymes, in:Methods in Enzymology, Elsevier, Amsterdam, 2016, pp. 213-250. [127] F. Himo, Recent trends in quantum chemical modeling of enzymatic reactions, J. Am. Chem. Soc. 139(20)(2017)6780-6786. [128] J. Desmet, M. De Maeyer, B. Hazes, I. Lasters, The dead-end elimination theorem and its use in protein side-chain positioning, Nature 356(1992) 539-542. [129] L. Jiang, E.A. Althoff, F.R. Clemente, L. Doyle, D. Röthlisberger, A. Zanghellini, J. L. Gallaher, J.L. Betker, F. Tanaka, C.F. Barbas, D. Hilvert, K.N. Houk, B.L. Stoddard, D. Baker, De Novo Computational Design of Retro-Aldol Enzymes, Science 319(5868)(2008)1387. [130] N. Nagano, EzCatDB:the enzyme catalytic-mechanism database, Nucleic Acids Res. 33(Database issue)(2004) D407-D412. [131] G.L. Holliday, C. Andreini, J.D. Fischer, S.A. Rahman, D.E. Almonacid, S.T. Williams, W.R. Pearson, MACiE:exploring the diversity of biochemical reactions, Nucleic Acids Res. 40(Database issue)(2012) D783-D789. [132] M. Lin, F. Wang, Y.S. Zhu, Modeled structure-based computational redesign of a glycosyltransferase for the synthesis of rebaudioside D from rebaudioside A, Biochem. Eng. J. 159(2020)107626. [133] J.K. Lassila, H.K. Privett, B.D. Allen, S.L. Mayo, Combinatorial methods for small-molecule placement in computational enzyme design, PNAS 103(45) (2006)16710-16715. [134] D.B. Gordon, S.A. Marshall, S.L. Mayot, Energy functions for protein design, Curr. Opin. Struct. Biol. 9(4)(1999)509-513. [135] D.B. Gordon, G.K. Hom, S.L. Mayo, N.A. Pierce, Exact rotamer optimization for protein design, J. Comput. Chem. 24(2)(2003)232-243. [136] B. Kuhlman, D. Baker, Native protein sequences are close to optimal for their structures, PNAS 97(19)(2000)10383-10388. [137] I.W. Davis, D. Baker, RosettaLigand docking with full ligand and receptor flexibility, J. Mol. Biol. 385(2)(2009)381-392. [138] Y.S. Zhu, Mixed-integer linear programming algorithm for a computational protein design problem, Ind. Eng. Chem. Res. 46(3)(2007)839-845. [139] A.L. Pinto, H.W. Hellinga, J.P. Caradonna, Construction of a catalytically active iron superoxide dismutase by rational protein design, PNAS 94(11)(1997) 5562-5567. [140] D.E. Benson, M.S. Wisz, H.W. Hellinga, Rational design of nascent metalloenzymes, PNAS 97(12)(2000)6292-6297. [141] M. Suarez, P. Tortosa, M.M. Garcia-Mira, D. Rodríguez-Larrea, R. Godoy-Ruiz, B. Ibarra-Molero, J.M. Sanchez-Ruiz, A. Jaramillo, Using multi-objective computational design to extend protein promiscuity, Biophys. Chem. 147 (1-2)(2010)13-19. [142] D.N. Bolon, S.L. Mayo, Enzyme-like proteins by computational design, Proc. Natl. Acad. Sci. USA 98(25)(2001)14274-14279. [143] H.K. Privett, G. Kiss, T.M. Lee, R. Blomberg, R.A. Chica, L.M. Thomas, D. Hilvert, K.N. Houk, S.L. Mayo, Iterative approach to computational enzyme design, PNAS 109(10)(2012)3790-3795. [144] A.D. St-Jacques, M.È.C. Eyahpaise, R.A. Chica, Computational design of multisubstrate enzyme specificity, ACS Catal. 9(6)(2019)5480-5485. [145] D. Röthlisberger, O. Khersonsky, A.M. Wollacott, L. Jiang, J. DeChancie, J. Betker, J.L. Gallaher, E.A. Althoff, A. Zanghellini, O. Dym, S. Albeck, K.N. Houk, D.S. Tawfik, D. Baker, Kemp elimination catalysts by computational enzyme design, Nature 453(7192)(2008)190-195. [146] J.B. Siegel, A. Zanghellini, H.M. Lovick, G. Kiss, A.R. Lambert, J.L. St Clair, J.L. Gallaher, D. Hilvert, M.H. Gelb, B.L. Stoddard, K.N. Houk, F.E. Michael, D. Baker, Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction, Science 329(5989)(2010)309-313. [147] M.J. Grisewood, N.P. Gifford, R.J. Pantazes, Y. Li, P.C. Cirino, M.J. Janik, C.D. Maranas, OptZyme:computational enzyme redesign using transition state analogues, PLoS ONE 8(10)(2013) e75358. [148] O. Buß, D. Muller, S. Jager, J. Rudat, K.S. Rabe, Corrigendum:improvement in the thermostability of a β-amino acid converting x-transaminase by using FoldX, ChemBioChem 19(20)(2018)2241. [149] H.J. Wijma, R.J. Floor, P.A. Jekel, D. Baker, S.J. Marrink, D.B. Janssen, Computationally designed libraries for rapid enzyme stabilization, Protein Eng. Des. Sel. 27(2)(2014)49-58. [150] H.J. Wijma, R.J. Floor, S. Bjelic, S.J. Marrink, D. Baker, D.B. Janssen, Enantioselective enzymes by computational design and in silico screening, Angew. Chem. Int. Ed. Engl. 54(12)(2015)3726-3730. [151] D. Bednar, K. Beerens, E. Sebestova, J. Bendl, S. Khare, R. Chaloupkova, Z. Prokop, J. Brezovsky, D. Baker, J. Damborsky, FireProt:energy-and evolutionbased computational design of thermostable multiple-point mutants, PLoS Comput. Biol. 11(11)(2015) e1004556. [152] A. Goldenzweig, M. Goldsmith, S.E. Hill, O. Gertman, P. Laurino, Y. Ashani, O. Dym, T. Unger, S. Albeck, J. Prilusky, R.L. Lieberman, A. Aharoni, I. Silman, J.L. Sussman, D.S. Tawfik, S.J. Fleishman, Automated structure-and sequencebased design of proteins for high bacterial expression and stability, Mol. Cell 63(2)(2016)337-346. [153] Y. Tian, X.Q. Huang, Q. Li, Y.S. Zhu, Computational design of variants for cephalosporin C acylase from Pseudomonas strain N176 with improved stability and activity, Appl. Microbiol. Biotechnol. 101(2)(2017)621-632. [154] J.W. He, X.Q. Huang, J. Xue, Y.S. Zhu, Computational redesign of penicillin acylase for cephradine synthesis with high kinetic selectivity, Green Chem. 20 (24)(2018)5484-5490. [155] O. Khersonsky, R. Lipsh, Z. Avizemer, Y. Ashani, M. Goldsmith, H. Leader, O. Dym, S. Rogotner, D.L. Trudeau, J. Prilusky, P. Amengual-Rigo, V. Guallar, D.S. Tawfik, S.J. Fleishman, Automated design of efficient and functionally diverse enzyme repertoires, Mol. Cell 72(1)(2018)178-186.e5. [156] M.D. Toscano, K.J. Woycechowsky, D. Hilvert, Minimalist active-site redesign: teaching old enzymes new tricks, Angew. Chem. Int. Ed. Engl. 46(18)(2007) 3212-3236. [157] R. Li, H.J. Wijma, L. Song, Y. Cui, M. Otzen, Y.E. Tian, J. Du, T. Li, D. Niu, Y. Chen, J. Feng, J. Han, H. Chen, Y. Tao, D.B. Janssen, B. Wu, Computational redesign of enzymes for regio-and enantioselective hydroamination, Nat. Chem. Biol. 14 (7)(2018)664-670. [158] X. Liu, Y. Yu, C. Hu, W. Zhang, Y. Lu, J. Wang, Significant increase of oxidase activity through the genetic incorporation of a tyrosine-histidine cross-link in a myoglobin model of heme-copper oxidase, Angew. Chem. Int. Ed. Engl. 51 (18)(2012)4312-4316. [159] N. Yeung, Y.W. Lin, Y.-G. Gao, X. Zhao, B.S. Russell, L. Lei, K.D. Miner, H. Robinson, Y. Lu, Rational design of a structural and functional nitric oxide reductase, Nature 462(7276)(2009)1079-1082. [160] T. Heinisch, T.R. Ward, Design strategies for the creation of artificial metalloenzymes, Curr. Opin. Chem. Biol. 14(2)(2010)184-199. [161] C. Mayer, D.G. Gillingham, T.R. Ward, D. Hilvert, An artificial metalloenzyme for olefin metathesis, Chem. Commun.(Camb.)47(44)(2011)12068-12070. [162] T.K. Hyster, L. Knörr, T.R. Ward, T. Rovis, Biotinylated Rh (III) complexes in engineered streptavidin for accelerated asymmetric C-H activation, Science 338(6106)(2012)500-503. [163] D.N. Woolfson, G.J. Bartlett, A.J. Burton, J.W. Heal, A. Niitsu, A.R. Thomson, C. W. Wood, De novo protein design:how do we expand into the universe of possible protein structures?, Curr Opin. Struct. Biol. 33(2015)16-26. [164] P.S. Huang, S.E. Boyken, D. Baker, The coming of age of de novo protein design, Nature 537(7620)(2016)320-327. [165] B. Koepnick, J. Flatten, T. Husain, A. Ford, D.A. Silva, M.J. Bick, A. Bauer, G.H. Liu, Y. Ishida, A. Boykov, R.D. Estep, S. Kleinfelter, T. Nørgård-Solano, L.D. Wei, F. Players, G.T. Montelione, F. DiMaio, Z. Popović, F. Khatib, S. Cooper, D. Baker, De novo protein design by citizen scientists, Nature 570(7761)(2019) 390-394. [166] I.V. Korendovych, W.F. DeGrado, De novo protein design, a retrospective, Q. Rev. Biophys. 53(2020) e3. [167] H. Kries, R. Blomberg, D. Hilvert, De novo enzymes by computational design, Curr. Opin. Chem. Biol. 17(2)(2013)221-228. [168] A. Bhowmick, S.C. Sharma, T. Head-Gordon, The importance of the scaffold for de novo enzymes:a case study with kemp eliminase, J. Am. Chem. Soc. 139 (16)(2017)5793-5800. [169] D.J. Tantillo, J. Chen, K.N. Houk, Theozymes and compuzymes:theoretical models for biological catalysis, Curr. Opin. Chem. Biol. 2(6)(1998)743-750. [170] S. Osuna, G. Jiménez-Osés, E.L. Noey, K.N. Houk, Molecular dynamics explorations of active site structure in designed and evolved enzymes, Acc. Chem. Res. 48(4)(2015)1080-1089. [171] U.T. Bornscheuer, G. Huisman, R. Kazlauskas, S. Lutz, J. Moore, K. Robins, Engineering the third wave of biocatalysis, Nature 485(7397)(2012)185- 194. [172] V. Vaissier Welborn, T. Head-Gordon, Computational design of synthetic enzymes, Chem. Rev. 119(11)(2018)6613-6630. [173] H.W. Hellinga, F.M. Richards, Construction of new ligand binding sites in proteins of known structure. I. Computer-aided modeling of sites with predefined geometry, J. Mol. Biol. 222(3)(1991)763-785. [174] B.I. Dahiyat, S.L. Mayo, De novo protein design:fully automated sequence selection, Science 278(5335)(1997)82-87. [175] A. Zanghellini, L. Jiang, A.M. Wollacott, G. Cheng, J. Meiler, E.A. Althoff, D. Röthlisberger, D. Baker, New algorithms and an in silico benchmark for computational enzyme design, Protein Sci. 15(12)(2006)2785-2794. [176] F. Richter, A. Leaver-Fay, S.D. Khare, S. Bjelic, D. Baker, De novo enzyme design using Rosetta3, PLoS ONE 6(5)(2011) e19230. [177] J.D. Keasling, Manufacturing molecules through metabolic engineering, Science 330(6009)(2010)1355-1358. [178] J. Kaplan, W.F. DeGrado, De novo design of catalytic proteins, PNAS 101(32) (2004)11566-11570. [179] F. Richter, R. Blomberg, S.D. Khare, G. Kiss, A.P. Kuzin, A.J. Smith, J. Gallaher, Z. Pianowski, R.C. Helgeson, A. Grjasnow, R. Xiao, J. Seetharaman, M. Su, S. Vorobiev, S. Lew, F. Forouhar, G.J. Kornhaber, J.F. Hunt, G.T. Montelione, L. Tong, K.N. Houk, D. Hilvert, D. Baker, Computational design of catalytic dyads and oxyanion holes for ester hydrolysis, J. Am. Chem. Soc. 134(39)(2012) 16197-16206. [180] I.V. Korendovych, D.W. Kulp, Y.B. Wu, H. Cheng, H. Roder, W.F. DeGrado, Design of a switchable eliminase, PNAS 108(17)(2011)6823-6827. [181] Y.S. Moroz, T.T. Dunston, O.V. Makhlynets, O.V. Moroz, Y.B. Wu, J.H. Yoon, A.B. Olsen, J.M. McLaughlin, K.L. Mack, P.M. Gosavi, N.A. van Nuland, I.V. Korendovych, New tricks for old proteins:single mutations in a nonenzymatic protein give rise to various enzymatic activities, J. Am. Chem. Soc. 137(47)(2015)14905-14911. [182] A.J. Burton, A.R. Thomson, W.M. Dawson, R.L. Brady, D.N. Woolfson, Installing hydrolytic activity into a completely de novo protein framework, Nat. Chem. 8 (9)(2016)837-844. [183] H. Fazelinia, P.C. Cirino, C.D.J.P.S. Maranas, OptGraft:a computational procedure for transferring a binding site onto an existing protein scaffold, Protein Sci. 18(1)(2009)180-195. [184] C. Malisi, O. Kohlbacher, B.J.P.S. Höcker, Automated scaffold selection for enzyme design, Proteins:Struct., Funct., Bioinform. 77(1)(2009)74-83. [185] Y. Lei, W. Luo, Y. Zhu, A matching algorithm for catalytic residue site selection in computational enzyme design, Protein Sci. 20(9)(2011)1566-1575. [186] J. Xue, X.Q. Huang, M. Lin, Y.S. Zhu, A fast loop-closure algorithm to accelerate residue matching in computational enzyme design, J. Mol. Model. 22(2) (2016)1-13. [187] S.Y. Zhang, J. Zhang, Y.S. Zhu, ProdaMatch:a fast and accurate active site matching algorithm for de novo enzyme design, Comput. Chem. Eng. 140 (2020)106921. [188] C.S. Zhang, L.H. Lai, Automatch:Target-binding protein design and enzyme design by automatic pinpointing potential active sites in available protein scaffolds, Proteins:Struct. Funct.Bioinform. 80(4)(2012)1078-1094. [189] G.R. Nosrati, K.N. Houk, SABER:a computational method for identifying active sites for new reactions, Protein Sci. 21(5)(2012)697-706. [190] B.D. Weitzner, Y. Kipnis, A.G. Daniel, D. Hilvert, D. Baker, A computational method for design of connected catalytic networks in proteins, Protein Sci. 28 (12)(2019)2036-2041. [191] J. Ludwiczak, A. Jarmula, S. Dunin-Horkawicz, Combining Rosetta with molecular dynamics (MD):a benchmark of the MD-based ensemble protein design, J. Struct. Biol. 203(1)(2018)54-61. [192] V.A. Feher, J.D. Durrant, A.T. van Wart, R.E. Amaro, Computational approaches to mapping allosteric pathways, Curr. Opin. Struct. Biol. 25(2014)98-103. [193] S.J. Wodak, E. Paci, N.V. Dokholyan, I.N. Berezovsky, A. Horovitz, J. Li, V.J. Hilser, I. Bahar, J. Karanicolas, G. Stock, P. Hamm, R.H. Stote, J. Eberhardt, Y. Chebaro, A. Dejaegere, M. Cecchini, J.P. Changeux, P.G. Bolhuis, J. Vreede, P. Faccioli, S. Orioli, R. Ravasio, L. Yan, C. Brito, M. Wyart, P. Gkeka, I. Rivalta, G. Palermo, J.A. McCammon, J. Panecka-Hofman, R.C. Wade, A. Di Pizio, M.Y. Niv, R. Nussinov, C.J. Tsai, H. Jang, D. Padhorny, D. Kozakov, T. McLeish, Allostery in its many disguises:from theory to applications, Structure 27(4)(2019) 566-578. |
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