RetroPath2.0: A retrosynthesis workflow for metabolic engineers

Metabolic Engineering

Volumn 45, Issue , 2018, Pages 158-170

  Delépine, Baudoin ^a,b   Duigou, Thomas ^a   Carbonell, Pablo ^c   Faulon, Jean Loup ^a,b,c  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b CENTRE NATIONAL DE SÉQUENÇAGE   (France)

^c UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

CAD software; Metabolic space; Pathways prediction; Retrosynthesis; Workflow

Indexed keywords

BIOSYNTHESIS; BIOTECHNOLOGY; COST ENGINEERING; INDUSTRIAL CHEMICALS; METABOLIC ENGINEERING; METABOLISM; OPEN SOURCE SOFTWARE;

BIOLOGICAL ENGINEERS; CAD SOFTWARES; CONTROLLED PROTOCOLS; INDUSTRIAL BIOTECHNOLOGY; METABOLIC SPACE; RESEARCH AND DEVELOPMENT; RETROSYNTHESIS; WORKFLOW;

COMPUTER AIDED DESIGN;

ARTICLE; AUTOMATION; BIOINFORMATICS; METABOLIC ENGINEERING; PIPELINE; PRIORITY JOURNAL; RETROPATH2.0; WORKFLOW; PROCEDURES; SOFTWARE;

METABOLIC ENGINEERING; SOFTWARE;

EID: 85038207858     PISSN: 10967176     EISSN: 10967184     Source Type: Journal    
DOI: 10.1016/j.ymben.2017.12.002     Document Type: Article

References (70)

1. Altman, T., Travers, M., Kothari, A., Caspi, R., Karp, P.D., A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinform., 14, 2013, 112, 10.1186/1471-2105-14-112.
2. Arita, M., In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. Genome Res. 13 (2003), 2455–2466, 10.1101/gr.1212003.
3. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B., 2008. KNIME: The Konstanz Information Miner. In: Data Analysis, Machine Learning and Applications. Springer, Berlin, Heidelberg, pp. 319–326.
4. Bramucci, M.G., McCutchen, C.M., Nagarajan, V., Thomas, S.M., 2001. Microbial production of terephthalic acid and isophthalic acid. US6187569 B1.
5. Campodonico, M.A., Andrews, B.A., Asenjo, J.A., Palsson, B.O., Feist, A.M., Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. Metab. Eng. 25 (2014), 140–158, 10.1016/j.ymben.2014.07.009.
6. Carbonell, P., Lecointre, G., Faulon, J.-L., Origins of Specificity and Promiscuity in Metabolic Networks. J. Biol. Chem. 286 (2011), 43994–44004, 10.1074/jbc.M111.274050.
7. Carbonell, P., Planson, A.-G., Fichera, D., Faulon, J.-L., A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst. Biol., 5, 2011, 122, 10.1186/1752-0509-5-122.
8. Carbonell, P., Fichera, D., Pandit, S.B., Faulon, J.-L., Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms. BMC Syst. Biol., 6, 2012, 10, 10.1186/1752-0509-6-10.
9. Carbonell, P., Carlsson, L., Faulon, J.-L., Stereo signature molecular descriptor. J. Chem. Inf. Model. 53 (2013), 887–897, 10.1021/ci300584r.
10. Carbonell, P., Parutto, P., Baudier, C., Junot, C., Faulon, J.-L., Retropath: automated pipeline for embedded metabolic circuits. ACS Synth. Biol. 3 (2014), 565–577, 10.1021/sb4001273.
11. Carbonell, P., Parutto, P., Herisson, J., Pandit, S.B., Faulon, J.-L., XTMS: pathway design in an eXTended metabolic space. Nucleic Acids Res. 42 (2014), W389–W394, 10.1093/nar/gku362.
12. Chang, A., Schomburg, I., Placzek, S., Jeske, L., Ulbrich, M., Xiao, M., Sensen, C.W., Schomburg, D., BRENDA in 2015: exciting developments in its 25th year of existence. Nucleic Acids Res. 43 (2015), D439–D446, 10.1093/nar/gku1068.
13. Chen, W.L., Chen, D.Z., Taylor, K.T., Automatic reaction mapping and reaction center detection. Wiley Interdiscip. Rev. Comput. Mol. Sci. 3 (2013), 560–593, 10.1002/wcms.1140.
14. Cho, A., Yun, H., Park, J.H., Lee, S.Y., Park, S., Prediction of novel synthetic pathways for the production of desired chemicals. BMC Syst. Biol., 4, 2010, 35, 10.1186/1752-0509-4-35.
15. Copeland, W.B., Bartley, B.A., Chandran, D., Galdzicki, M., Kim, K.H., Sleight, S.C., Maranas, C.D., Sauro, H.M., Computational tools for metabolic engineering. Metab. Eng. 14 (2012), 270–280, 10.1016/j.ymben.2012.03.001.
16. Daylight, 2017. Daylight Theory Manual [WWW Document]. URL 〈http://www.daylight.com/dayhtml/doc/theory/〉 (Accessed 14 April 2017).
17. Delépine, B., Libis, V., Carbonell, P., Faulon, J.-L., SensiPath: computer-aided design of sensing-enabling metabolic pathways. Nucleic Acids Res. 44 (2016), W226–W231, 10.1093/nar/gkw305.
18. Dugundji, J., Ugi, I., 1973. An algebraic model of constitutional chemistry as a basis for chemical computer programs. In: Computers in Chemistry. Springer, Berlin, Heidelberg, pp. 19–64.
19. Faulon, J.-L., Misra, M., Martin, S., Sale, K., Sapra, R., Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor. Bioinformatics 24 (2008), 225–233, 10.1093/bioinformatics/btm580.
20. Finley, S.D., Broadbelt, L.J., Hatzimanikatis, V., Computational framework for predictive biodegradation. Biotechnol. Bioeng. 104 (2009), 1086–1097, 10.1002/bit.22489.
21. Guzmán, G.I., Utrilla, J., Nurk, S., Brunk, E., Monk, J.M., Ebrahim, A., Palsson, B.O., Feist, A.M., Model-driven discovery of underground metabolic functions in Escherichia coli. Proc. Natl. Acad. Sci. USA 112 (2015), 929–934, 10.1073/pnas.1414218112.
22. Hadadi, N., Hatzimanikatis, V., Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Curr. Opin. Chem. Biol. 28 (2015), 99–104, 10.1016/j.cbpa.2015.06.025.
23. Hadadi, N., Hafner, J., Shajkofci, A., Zisaki, A., Hatzimanikatis, V., ATLAS of Biochemistry: a repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synth. Biol. 5 (2016), 1155–1166, 10.1021/acssynbio.6b00054.
24. Hadadi, N., Hafner, J., Soh, K.C., Hatzimanikatis, V., Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites. Biotechnol. J., 2016, 10.1002/biot.201600464.
25. Hatzimanikatis, V., Li, C., Ionita, J.A., Henry, C.S., Jankowski, M.D., Broadbelt, L.J., Exploring the diversity of complex metabolic networks. Bioinformatics 21 (2005), 1603–1609, 10.1093/bioinformatics/bti213.
26. Haug, K., Salek, R.M., Steinbeck, C., Global open data management in metabolomics. Curr. Opin. Chem. Biol. 36 (2017), 58–63, 10.1016/j.cbpa.2016.12.024.
27. Henry, C.S., Broadbelt, L.J., Hatzimanikatis, V., Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol. Bioeng. 106 (2010), 462–473, 10.1002/bit.22673.
28. Hou, B.K., Wackett, L.P., Ellis, L.B.M., Microbial pathway prediction: a functional group approach. J. Chem. Inf. Comput. Sci. 43 (2003), 1051–1057, 10.1021/ci034018f.
29. Hou, B.K., Ellis, L.B.M., Wackett, L.P., Encoding microbial metabolic logic: predicting biodegradation. J. Ind. Microbiol. Biotechnol. 31 (2004), 261–272, 10.1007/s10295-004-0144-7.
30. International Union of Biochemistry and Molecular Biology Nomenclature Committee, Webb, E.C, 1992. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes. International Union of Biochemistry and Molecular Biology by Academic Press. ed. San Diego.
31. Isikgor, F.H., Becer, C.R., Lignocellulosic biomass: a sustainable platform for the production of bio-based chemicals and polymers. Polym. Chem. 6 (2015), 4497–4559, 10.1039/C5PY00263J.
32. Jeffryes, J.G., Colastani, R.L., Elbadawi-Sidhu, M., Kind, T., Niehaus, T.D., Broadbelt, L.J., Hanson, A.D., Fiehn, O., Tyo, K.E.J., Henry, C.S., MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J. Cheminform., 7, 2015, 44, 10.1186/s13321-015-0087-1.
33. Kayala, M.A., Azencott, C.-A., Chen, J.H., Baldi, P., Learning to predict chemical reactions. J. Chem. Inf. Model. 51 (2011), 2209–2222, 10.1021/ci200207y.
34. Keasling, 2014. Hearing on Policies to Spur Innovative Medical Breakthroughs from Laboratories to Patients.
35. Keseler, I.M., Mackie, A., Peralta-Gil, M., Santos-Zavaleta, A., Gama-Castro, S., Bonavides-Martínez, C., Fulcher, C., Huerta, A.M., Kothari, A., Krummenacker, M., Latendresse, M., Muñiz-Rascado, L., Ong, Q., Paley, S., Schröder, I., Shearer, A.G., Subhraveti, P., Travers, M., Weerasinghe, D., Weiss, V., Collado-Vides, J., Gunsalus, R.P., Paulsen, I., Karp, P.D., EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 41 (2013), D605–D612, 10.1093/nar/gks1027.
36. Khersonsky, O., Tawfik, D.S., Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 79 (2010), 471–505, 10.1146/annurev-biochem-030409-143718.
37. Landrum, 2016. RDKit: Open-source Cheminformatics [WWW Document]. URL 〈http://www.rdkit.org/〉 (Accessed 2 August 2016).
38. Latino, D.A.R.S., Aires-de-Sousa, J., Classification of chemical reactions and chemoinformatic processing of enzymatic transformations. Methods Mol. Biol. 672 (2011), 325–340, 10.1007/978-1-60761-839-3_13.
39. Lee, S.Y., Kim, H.U., Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 33 (2015), 1061–1072, 10.1038/nbt.3365.
40. Li, W., Godzik, A., Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22 (2006), 1658–1659, 10.1093/bioinformatics/btl158.
41. Liu, M., Bienfait, B., Sacher, O., Gasteiger, J., Siezen, R.J., Nauta, A., Geurts, J.M.W., Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach “reverse pathway engineering”. PLoS One, 9, 2014, e84769, 10.1371/journal.pone.0084769.
42. Marchant, C.A., Briggs, K.A., Long, A., In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic. Toxicol. Mech. Methods 18 (2008), 177–187, 10.1080/15376510701857320.
43. McKenna, R., Nielsen, D.R., Styrene biosynthesis from glucose by engineered E. coli. Metab. Eng. 13 (2011), 544–554, 10.1016/j.ymben.2011.06.005.
44. McKenna, R., Thompson, B., Pugh, S., Nielsen, D.R., Rational and combinatorial approaches to engineering styrene production by Saccharomyces cerevisiae. Microb. Cell Factor., 13, 2014, 123, 10.1186/s12934-014-0123-2.
45. McKenna, R., Moya, L., McDaniel, M., Nielsen, D.R., Comparing in situ removal strategies for improving styrene bioproduction. Bioprocess Biosyst. Eng. 38 (2015), 165–174, 10.1007/s00449-014-1255-9.
46. McNutt, M., Lehnert, K., Hanson, B., Nosek, B.A., Ellison, A.M., King, J.L., RESEARCH INTEGRITY. Liberating field science samples and data. Science 351 (2016), 1024–1026, 10.1126/science.aad7048.
47. Medema, M.H., van Raaphorst, R., Takano, E., Breitling, R., Computational tools for the synthetic design of biochemical pathways. Nat. Rev. Microbiol. 10 (2012), 191–202, 10.1038/nrmicro2717.
48. Mellor, J., Grigoras, I., Carbonell, P., Faulon, J.-L., Semisupervised Gaussian process for automated enzyme search. ACS Synth. Biol. 5 (2016), 518–528, 10.1021/acssynbio.5b00294.
49. Moretti, S., Martin, O., Van Du Tran, T., Bridge, A., Morgat, A., Pagni, M., MetaNetX/MNXref–reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res. 44 (2016), D523–D526, 10.1093/nar/gkv1117.
50. Moriya, Y., Shigemizu, D., Hattori, M., Tokimatsu, T., Kotera, M., Goto, S., Kanehisa, M., PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res. 38 (2010), W138–W143, 10.1093/nar/gkq318.
51. Nam, H., Lewis, N.E., Lerman, J.A., Lee, D.-H., Chang, R.L., Kim, D., Palsson, B.O., Network context and selection in the evolution to enzyme specificity. Science 337 (2012), 1101–1104, 10.1126/science.1216861.
52. Oh, M., Yamada, T., Hattori, M., Goto, S., Kanehisa, M., Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. J. Chem. Inf. Model. 47 (2007), 1702–1712, 10.1021/ci700006f.
53. Orth, J.D., Palsson, B., Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions. BMC Syst. Biol., 6, 2012, 30, 10.1186/1752-0509-6-30.
54. Orth, J.D., Conrad, T.M., Na, J., Lerman, J.A., Nam, H., Feist, A.M., Palsson, B.Ø., A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol. Syst. Biol., 7, 2011, 535, 10.1038/msb.2011.65.
55. Paddon, C.J., Westfall, P.J., Pitera, D.J., Benjamin, K., Fisher, K., McPhee, D., Leavell, M.D., Tai, A., Main, A., Eng, D., Polichuk, D.R., Teoh, K.H., Reed, D.W., Treynor, T., Lenihan, J., Fleck, M., Bajad, S., Dang, G., Dengrove, D., Diola, D., Dorin, G., Ellens, K.W., Fickes, S., Galazzo, J., Gaucher, S.P., Geistlinger, T., Henry, R., Hepp, M., Horning, T., Iqbal, T., Jiang, H., Kizer, L., Lieu, B., Melis, D., Moss, N., Regentin, R., Secrest, S., Tsuruta, H., Vazquez, R., Westblade, L.F., Xu, L., Yu, M., Zhang, Y., Zhao, L., Lievense, J., Covello, P.S., Keasling, J.D., Reiling, K.K., Renninger, N.S., Newman, J.D., High-level semi-synthetic production of the potent antimalarial artemisinin. Nature 496 (2013), 528–532, 10.1038/nature12051.
56. Planson, A.-G., Carbonell, P., Paillard, E., Pollet, N., Faulon, J.-L., Compound toxicity screening and structure-activity relationship modeling in Escherichia coli. Biotechnol. Bioeng. 109 (2012), 846–850, 10.1002/bit.24356.
57. Rahman, S.A., Cuesta, S.M., Furnham, N., Holliday, G.L., Thornton, J.M., EC-BLAST: a tool to automatically search and compare enzyme reactions. Nat. Methods 11 (2014), 171–174, 10.1038/nmeth.2803.
58. Rahman, S.A., Torrance, G., Baldacci, L., Martínez Cuesta, S., Fenninger, F., Gopal, N., Choudhary, S., May, J.W., Holliday, G.L., Steinbeck, C., Thornton, J.M., Reaction Decoder Tool (RDT): extracting features from chemical reactions. Bioinformatics 32 (2016), 2065–2066, 10.1093/bioinformatics/btw096.
59. Rodrigo, G., Carrera, J., Prather, K.J., Jaramillo, A., DESHARKY: automatic design of metabolic pathways for optimal cell growth. Bioinformatics 24 (2008), 2554–2556, 10.1093/bioinformatics/btn471.
60. Schofield, P.N., Bubela, T., Weaver, T., Portilla, L., Brown, S.D., Hancock, J.M., Einhorn, D., Tocchini-Valentini, G., Hrabe de Angelis, M., Rosenthal, N., CASIMIR Rome Meeting participants, Post-publication sharing of data and tools. Nature 461 (2009), 171–173, 10.1038/461171a.
61. Schuster, S., Fell, D.A., Dandekar, T., A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat. Biotechnol. 18 (2000), 326–332, 10.1038/73786.
62. Sheehan, R.J., 2000. Terephthalic Acid, Dimethyl Terephthalate, and Isophthalic Acid. In: Ullmann's Encyclopedia of Industrial Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA.
63. Sivakumar, T.V., Giri, V., Park, J.H., Kim, T.Y., Bhaduri, A., ReactPRED: a tool to predict and analyze biochemical reactions. Bioinformatics, 2016, 10.1093/bioinformatics/btw491.
64. Thodey, K., Galanie, S., Smolke, C.D., A microbial biomanufacturing platform for natural and semisynthetic opioids. Nat. Chem. Biol. 10 (2014), 837–844, 10.1038/nchembio.1613.
65. Wang, J., Tian, J., Xu, J., A method for producing terephthalic acid by Comamonas testosteroni DSM6577. Chin. J. Catal., 27, 2006, 297.
66. Warr, W.A., Scientific workflow systems: Pipeline Pilot and KNIME. J. Comput. Aided Mol. Des. 26 (2012), 801–804, 10.1007/s10822-012-9577-7.
67. Winkler, J.D., Halweg-Edwards, A.L., Gill, R.T., The LASER database: Formalizing design rules for metabolic engineering. Metab. Eng. Commun. 2 (2015), 30–38, 10.1016/j.meteno.2015.06.003.
68. Winkler, J.D., Halweg-Edwards, A.L., Gill, R.T., Quantifying complexity in metabolic engineering using the LASER database. Metab. Eng. Commun. 3 (2016), 227–233, 10.1016/j.meteno.2016.07.002.
69. Yang, K., Metcalf, W.W., A new activity for an old enzyme: Escherichia coli bacterial alkaline phosphatase is a phosphite-dependent hydrogenase. Proc. Natl. Acad. Sci. USA 101 (2004), 7919–7924, 10.1073/pnas.0400664101.
70. Yim, H., Haselbeck, R., Niu, W., Pujol-Baxley, C., Burgard, A., Boldt, J., Khandurina, J., Trawick, J.D., Osterhout, R.E., Stephen, R., Estadilla, J., Teisan, S., Schreyer, H.B., Andrae, S., Yang, T.H., Lee, S.Y., Burk, M.J., Van Dien, S., Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat. Chem. Biol. 7 (2011), 445–452, 10.1038/nchembio.580.