Effective engineering of a ketoreductase for the biocatalytic synthesis of an ipatasertib precursor.
Journal
Communications chemistry
ISSN: 2399-3669
Titre abrégé: Commun Chem
Pays: England
ID NLM: 101725670
Informations de publication
Date de publication:
28 Feb 2024
28 Feb 2024
Historique:
received:
25
08
2023
accepted:
15
02
2024
medline:
29
2
2024
pubmed:
29
2
2024
entrez:
28
2
2024
Statut:
epublish
Résumé
Semi-rational enzyme engineering is a powerful method to develop industrial biocatalysts. Profiting from advances in molecular biology and bioinformatics, semi-rational approaches can effectively accelerate enzyme engineering campaigns. Here, we present the optimization of a ketoreductase from Sporidiobolus salmonicolor for the chemo-enzymatic synthesis of ipatasertib, a potent protein kinase B inhibitor. Harnessing the power of mutational scanning and structure-guided rational design, we created a 10-amino acid substituted variant exhibiting a 64-fold higher apparent k
Identifiants
pubmed: 38418529
doi: 10.1038/s42004-024-01130-5
pii: 10.1038/s42004-024-01130-5
doi:
Types de publication
Journal Article
Langues
eng
Pagination
46Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
ID : 180544
Informations de copyright
© 2024. The Author(s).
Références
Patel, R. N. Biocatalytic synthesis of chiral alcohols and amino acids for development of pharmaceuticals. Biomolecules 3, 741–777 (2013).
pubmed: 24970190
pmcid: 4030968
doi: 10.3390/biom3040741
Hashiguchi, S. et al. Kinetic resolution of racemic secondary alcohols by Ru(II)-catalyzed hydrogen transfer. Angew. Chem. Int. Ed. 36, 288–290 (1997).
doi: 10.1002/anie.199702881
Noyori, R. Asymmetric catalysis: science and opportunities (Nobel Lecture 2001). Adv. Synth. Catal. 345, 15–32 (2003).
doi: 10.1002/adsc.200390002
Klingler, F. D. Asymmetric hydrogenation of prochiral amino ketones to amino alcohols for pharmaceutical use. Acc. Chem. Res. 40, 1367–1376 (2007).
pubmed: 18052332
doi: 10.1021/ar700100e
Bornscheuer, U. T. et al. Engineering the third wave of biocatalysis. Nature 485, 185–194 (2012).
pubmed: 22575958
doi: 10.1038/nature11117
Rodriguez, S. et al. Amine‐tunable ruthenium catalysts for asymmetric reduction of ketones. Adv. Synth. Catal. 356, 301–307 (2014).
doi: 10.1002/adsc.201300727
Hayler, J. D., Leahy, D. K. & Simmons, E. M. A pharmaceutical industry perspective on sustainable metal catalysis. Organometallics 38, 36–46 (2019).
doi: 10.1021/acs.organomet.8b00566
Seo, C. S. G. & Morris, R. H. Catalytic homogeneous asymmetric hydrogenation: successes and opportunities. Organometallics 38, 47–65 (2019).
doi: 10.1021/acs.organomet.8b00774
Wu, S., Snajdrova, R., Moore, J. C., Baldenius, K. & Bornscheuer, U. T. Biocatalysis: enzymatic synthesis for industrial applications. Angew. Chem. Int. Ed. 60, 88–119 (2021).
doi: 10.1002/anie.202006648
Buller, R. et al. From nature to industry: harnessing enzymes for biocatalysis. Science 382, eadh8615 (2023).
pubmed: 37995253
doi: 10.1126/science.adh8615
Huisman, G. W., Liang, J. & Krebber, A. Practical chiral alcohol manufacture using ketoreductases. Curr. Opin. Chem. Biol. 14, 122–129 (2010).
pubmed: 20071211
doi: 10.1016/j.cbpa.2009.12.003
Buller, R., Hecht, K., Mirata, M. A. & Meyer, H.-P. An appreciation of biocatalysis in the Swiss manufacturing environment. in Biocatalysis: An Industrial Perspective (eds de Gonzalo, G. & Domínguez de María, P.) 3–43 (The Royal Society of Chemistry, 2017).
Jez, J. M., Bennett, M. J., Schlegel, B. P., Lewis, M. & Penning, T. M. Comparative anatomy of the aldo-keto reductase superfamily. Biochem. J. 326, 625–636 (1997).
pubmed: 9307009
pmcid: 1218714
doi: 10.1042/bj3260625
Hughes, D. L. Biocatalysis in drug development—highlights of the recent patent literature. Org. Process Res. Dev. 22, 1063–1080 (2018).
doi: 10.1021/acs.oprd.8b00232
Ma, S. K. et al. A green-by-design biocatalytic process for atorvastatin intermediate. Green. Chem. 12, 81–86 (2010).
doi: 10.1039/B919115C
Gong, X. M. et al. Development of an engineered ketoreductase with simultaneously improved thermostability and activity for making a bulky atorvastatin precursor. ACS Catal. 9, 147–153 (2019).
doi: 10.1021/acscatal.8b03382
Liang, J. et al. Development of a biocatalytic process as an alternative to the (-)-DIP-Cl-mediated asymmetric reduction of a key intermediate of montelukast. Org. Process Res. Dev. 14, 193–198 (2010).
doi: 10.1021/op900272d
Depré, D. P. M. et al. Processes and intermediates for preparing a macrocyclic protease inhibitor of HCV. US20180093943A1 (2018).
Slagman, S. & Fessner, W. D. Biocatalytic routes to anti-viral agents and their synthetic intermediates. Chem. Soc. Rev. 50, 1968–2009 (2021).
pubmed: 33325938
doi: 10.1039/D0CS00763C
Han, C. et al. Asymmetric synthesis of Akt kinase inhibitor ipatasertib. Org. Lett. 19, 4806–4809 (2017).
pubmed: 28858516
doi: 10.1021/acs.orglett.7b02228
Bachmann, S. et al. Development of the commercial manufacturing process for ipatasertib. CHIMIA 75, 605–613 (2021).
pubmed: 34523401
doi: 10.2533/chimia.2021.605
Blake, J. F. et al. Discovery and preclinical pharmacology of a selective ATP-competitive Akt inhibitor (GDC-0068) for the treatment of human tumors. J. Med. Chem. 55, 8110–8127 (2012).
Shi, Z. et al. Biomarker analysis of the phase III IPATential150 trial of first-line ipatasertib (Ipat) plus abiraterone (Abi) in metastatic castration-resistant prostate cancer (mCRPC). J. Clin. Oncol. 38, 182 (2020).
doi: 10.1200/JCO.2020.38.6_suppl.182
Dent, R. et al. Final results of the double-blind placebo-controlled randomized phase 2 LOTUS trial of first-line ipatasertib plus paclitaxel for inoperable locally advanced/metastatic triple-negative breast cancer. Breast Cancer Res. Treat. 189, 377–386 (2021).
pubmed: 34264439
doi: 10.1007/s10549-021-06143-5
Eichenberger, M. et al. Asymmetric cation‐olefin monocyclization by engineered squalene hopene cyclases. Angew. Chem. Int. Ed. 60, 26080–26086 (2021).
doi: 10.1002/anie.202108037
Meyer, F. et al. Modulating chemoselectivity in a Fe(II)/α-ketoglutarate-dependent dioxygenase for the oxidative modification of a nonproteinogenic amino acid. ACS Catal. 11, 6261–6269 (2021).
doi: 10.1021/acscatal.1c00678
Papadopoulou, A., Peters, C., Borchert, S., Steiner, K. & Buller, R. Development of an Ene reductase-based biocatalytic process for the production of flavor compounds. Org. Process Res. Dev. 26, 2102–2110 (2022).
doi: 10.1021/acs.oprd.2c00096
Voss, M. et al. Multi-faceted set-up of a diverse ketoreductase library enables the synthesis of pharmaceutically-relevant secondary alcohols. ChemCatChem 13, 1538–1545 (2021).
doi: 10.1002/cctc.202001871
Kita, K., Nakase, K. I., Yanase, H., Kataoka, M. & Shimizu, S. Purification and characterization of new aldehyde reductases from Sporobolomyces salmonicolor AKU4429. J. Mol. Catal. B Enzym. 6, 305–313 (1999).
doi: 10.1016/S1381-1177(98)00108-8
Kita, K. et al. Cloning, overexpression, and mutagenesis of the Sporobolomyces salmonicolor AKU4429 gene encoding a new aldehyde reductase, which catalyzes the stereoselective reduction of ethyl 4-chloro-3-oxobutanoate to ethyl (S)-4-chloro-3-hydroxybutanoate. Appl. Environ. Microbiol. 65, 5207–5211 (1999).
pubmed: 10583966
pmcid: 91706
doi: 10.1128/AEM.65.12.5207-5211.1999
Kamitori, S., Iguchi, A., Ohtaki, A., Yamada, M. & Kita, K. X-ray structures of NADPH-dependent carbonyl reductase from Sporobolomyces salmonicolor provide insights into stereoselective reductions of carbonyl compounds. J. Mol. Biol. 352, 551–558 (2005).
pubmed: 16095619
doi: 10.1016/j.jmb.2005.07.011
Zhu, D. et al. Inverting the enantioselectivity of a carbonyl reductase via substrate-enzyme docking-guided point mutation. Org. Lett. 10, 525–528 (2008).
pubmed: 18205368
doi: 10.1021/ol702638j
Li, H., Zhu, D., Hua, L. & Biehl, E. R. Enantioselective reduction of diaryl ketones catalyzed by a carbonyl reductase from Sporobolomyces salmonicolor and its mutant enzymes. Adv. Synth. Catal. 351, 583–588 (2009).
doi: 10.1002/adsc.200900045
Li, H., Yang, Y., Zhu, D., Hua, L. & Kantardjieff, K. Highly enantioselective mutant carbonyl reductases created via structure-based site-saturation mutagenesis. J. Org. Chem. 75, 7559–7564 (2010).
pubmed: 20964397
doi: 10.1021/jo101541n
Li, J. et al. Structure-guided directed evolution of a carbonyl reductase enables the stereoselective synthesis of (2S,3S)-2,2-disubstituted-3-hydroxycyclopentanones via desymmetric reduction. Org. Lett. 22, 3444–3448 (2020).
pubmed: 32319785
doi: 10.1021/acs.orglett.0c00892
Iding, H., Reents, R., Scalone, M. & Gosselin, F. Processes for the preparation of pyrimidinylcyclopentane compounds. US20160297773A1 (2016).
Büchler, J. et al. Algorithm-aided engineering of aliphatic halogenase WelO5* for the asymmetric late-stage functionalization of soraphens. Nat. Commun. 13, 371 (2022).
pubmed: 35042883
pmcid: 8766452
doi: 10.1038/s41467-022-27999-1
Kawashima, S. & Kanehisa, M. AAindex: amino acid index database. Nucleic Acids Res. 28, 374 (2000).
pubmed: 10592278
pmcid: 102411
doi: 10.1093/nar/28.1.374
Kawashima, S. et al. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36, 202–205 (2008).
doi: 10.1093/nar/gkm998
Saito, Y. et al. Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration. ACS Catal. 11, 14615–14624 (2021).
doi: 10.1021/acscatal.1c03753
Lu, H. et al. Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662–667 (2022).
pubmed: 35478237
doi: 10.1038/s41586-022-04599-z
Ma, E. J. et al. Machine-directed evolution of an imine reductase for activity and stereoselectivity. ACS Catal. 11, 12433–12445 (2021).
doi: 10.1021/acscatal.1c02786
Vornholt, T. et al. Systematic engineering of artificial metalloenzymes for new-to-nature reactions. Sci. Adv. 7, 1–12 (2021).
doi: 10.1126/sciadv.abe4208
Greenhalgh, J. C., Fahlberg, S. A., Pfleger, B. F. & Romero, P. A. Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production. Nat. Commun. 12, 1–10 (2021).
doi: 10.1038/s41467-021-25831-w
Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl Acad. Sci. USA 110, E193–E201 (2013).
pubmed: 23277561
doi: 10.1073/pnas.1215251110
Domínguez de María, P. Biocatalysis, sustainability, and industrial applications: show me the metrics. Curr. Opin. Green Sustain. Chem. 31, 100514 (2021).
doi: 10.1016/j.cogsc.2021.100514
Reetz, M. T. & Carballeira, J. D. Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes. Nat. Protoc. 2, 891–903 (2007).
pubmed: 17446890
doi: 10.1038/nprot.2007.72
Banta, S., Swanson, B. A., Wu, S., Jarnagin, A. & Anderson, S. Alteration of the specificity of the cofactor-binding pocket of Corynebacterium 2,5-diketo-D-gluconic acid reductase A. Protein Eng. 15, 131–140 (2002).
pubmed: 11917149
doi: 10.1093/protein/15.2.131
Kokkonen, P., Bednar, D., Pinto, G., Prokop, Z. & Damborsky, J. Engineering enzyme access tunnels. Biotechnol. Adv. 37, 107386 (2019).
pubmed: 31026496
doi: 10.1016/j.biotechadv.2019.04.008
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).
pubmed: 31308553
doi: 10.1038/s41592-019-0496-6
Reetz, M. T. The importance of additive and non‐additive mutational effects in protein engineering. Angew. Chem. Int. Ed. 52, 2658–2666 (2013).
doi: 10.1002/anie.201207842
Mazurenko, S., Prokop, Z. & Damborsky, J. Machine learning in enzyme engineering. ACS Catal. 10, 1210–1223 (2020).
doi: 10.1021/acscatal.9b04321
Hie, B., Bryson, B. D. & Berger, B. Leveraging uncertainty in machine learning accelerates biological discovery and design. Cell Syst. 11, 461–477.e9 (2020).
pubmed: 33065027
doi: 10.1016/j.cels.2020.09.007
Wittmann, B. J., Johnston, K. E., Wu, Z. & Arnold, F. H. Advances in machine learning for directed evolution. Curr. Opin. Struct. Biol. 69, 11–18 (2021).
pubmed: 33647531
doi: 10.1016/j.sbi.2021.01.008
Hie, B. L. & Yang, K. K. Adaptive machine learning for protein engineering. Curr. Opin. Struct. Biol. 72, 145–152 (2022).
pubmed: 34896756
doi: 10.1016/j.sbi.2021.11.002
Markus, B. et al. Accelerating biocatalysis discovery with machine learning: a paradigm shift in enzyme engineering, discovery, and design. ACS Catal. 13, 14454–14469 (2023).
pubmed: 37942268
pmcid: 10629211
doi: 10.1021/acscatal.3c03417
Patsch, D. & Buller, R. Improving enzyme fitness with machine learning. CHIMIA 77, 116–121 (2023).
pubmed: 38047813
doi: 10.2533/chimia.2023.116
Chen, X., Zhang, H., Feng, J., Wu, Q. & Zhu, D. Molecular basis for the high activity and enantioselectivity of the carbonyl reductase from Sporobolomyces salmonicolor toward α-Haloacetophenones. ACS Catal. 8, 3525–3531 (2018).
doi: 10.1021/acscatal.8b00591
Kille, S. et al. Reducing codon redundancy and screening effort of combinatorial protein libraries created by saturation mutagenesis. ACS Synth. Biol. 2, 83–92 (2013).
pubmed: 23656371
doi: 10.1021/sb300037w
Xia, Y. et al. T5 exonuclease-dependent assembly offers a low-cost method for efficient cloning and site-directed mutagenesis. Nucleic Acids Res. 47, 1–11 (2019).
doi: 10.1093/nar/gky1169
Miyazaki, K. & Takenouchi, M. Creating random mutagenesis libraries using PCR of whole plasmid. Biotechniques 33, 1033–1034 (2002).
pubmed: 12449380
doi: 10.2144/02335st03
Rasmussen, C. E. & Williams, C. K. I. Gaussian processes for machine learning. In Advanced Lectures on Machine Learning: ML Summer Schools (eds Bousquet, O., von Luxburg, U. & Raetsch, G.) (MIT, 2006).
Molecular Operating Environment (MOE), 2019.01. (Chemical Computing Group ULC, 2019).
Gerber, P. R. & Müller, K. MAB, a generally applicable molecular force field for structure modelling in medicinal chemistry. J. Comput. Aided Mol. Des. 9, 251–268 (1995).
pubmed: 7561977
doi: 10.1007/BF00124456
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
pubmed: 33885785
pmcid: 8265157
doi: 10.1093/nar/gkab301