Elucidation of the noncovalent interactions driving enzyme activity guides branching enzyme engineering for α-glucan modification.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
09 Oct 2024
09 Oct 2024
Historique:
received:
22
01
2024
accepted:
23
09
2024
medline:
10
10
2024
pubmed:
10
10
2024
entrez:
9
10
2024
Statut:
epublish
Résumé
Branching enzymes (BEs) confer to α-glucans, the primary energy-storage reservoir in nature, a variety of features, like slow digestion. The full catalytic cycle of BEs can be divided in six steps, namely two covalent catalytic steps involving glycosylation and transglycosylation, and four noncatalytic steps involving substrate binding and transfers (SBTs). Despite the ever-growing wealth of biochemical and structural information on BEs, clear mechanistic insights into SBTs from an industrial-performance perspective are still missing. Here, we report a Rhodothermus profundi BE (RpBE) endowed with twice as much enzymatic activity as the Rhodothermus obamensis BE currently used in industry. Furthermore, we focus on the SBTs for RpBE by means of large-scale computations supported by experiment. Engineering of the crucial positions responsible for the initial substrate-binding step improves enzymatic activity significantly, while offering a possibility to customize product types. In addition, we show that the high-efficiency substrate-transfer steps preceding glycosylation and transglycosylation are the main reason for the remarkable enzymatic activity of RpBE, suggestive of engineering directions for the BE family.
Identifiants
pubmed: 39384762
doi: 10.1038/s41467-024-53018-6
pii: 10.1038/s41467-024-53018-6
doi:
Substances chimiques
Glucans
0
1,4-alpha-Glucan Branching Enzyme
EC 2.4.1.18
Bacterial Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8760Subventions
Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 22005157
Informations de copyright
© 2024. The Author(s).
Références
Ban, X. et al. Bacterial 1,4-alpha-glucan branching enzymes: characteristics, preparation and commercial applications. Crit. Rev. Biotechnol. 40, 380–396 (2020).
pubmed: 31996051
doi: 10.1080/07388551.2020.1713720
Chakraborty, R., Kalita, P. & Sen, S. Natural starch in biomedical and food industry: perception and overview. Curr. Drug. Discov. Technol. 16, 355–367 (2019).
pubmed: 30280669
doi: 10.2174/1570163815666181003143732
Punia, S. Barley starch modifications: physical, chemical and enzymatic - A review. Int. J. Biol. Macromol. 144, 578–585 (2020).
pubmed: 31843612
doi: 10.1016/j.ijbiomac.2019.12.088
Li, X. et al. Partial branching enzyme treatment increases the low glycaemic property and α-1,6 branching ratio of maize starch. Food Chem. 164, 502–509 (2014).
pubmed: 24996363
doi: 10.1016/j.foodchem.2014.05.074
Tetlow, I. J. & Emes, M. J. A review of starch-branching enzymes and their role in amylopectin biosynthesis. IUBMB Life 66, 546–558 (2014).
pubmed: 25196474
doi: 10.1002/iub.1297
Bilyard, M. K. et al. Palladium-mediated enzyme activation suggests multiphase initiation of glycogenesis. Nature 563, 235–240 (2018).
pubmed: 30356213
doi: 10.1038/s41586-018-0644-7
Huynh, N., Ou, Q., Cox, P., Lill, R. & King-Jones, K. Glycogen branching enzyme controls cellular iron homeostasis via Iron Regulatory Protein 1 and mitoNEET. Nat. Commun. 10, 5463 (2019).
pubmed: 31784520
pmcid: 6884552
doi: 10.1038/s41467-019-13237-8
Bürgy, L. et al. Coalescence and directed anisotropic growth of starch granule initials in subdomains of Arabidopsis thaliana chloroplasts. Nat. Commun. 12, 6944 (2021).
pubmed: 34836943
pmcid: 8626487
doi: 10.1038/s41467-021-27151-5
Baecker, P. A., Greenberg, E. & Preiss, J. Biosynthesis of bacterial glycogen. primary structure of Escherichia coli 1,4-alpha-D-glucan:1,4-alpha-D-glucan 6-alpha-D-(1, 4-alpha-D-glucano)-transferase as deduced from the nucleotide sequence of the glg B gene. J. Biol. Chem. 261, 8738–8743 (1986).
pubmed: 3013861
doi: 10.1016/S0021-9258(19)84443-5
Svensson, B. Regional distant sequence homology between amylases, alpha-glucosidases and transglucanosylases. FEBS Lett. 230, 72–76 (1988).
pubmed: 2450787
doi: 10.1016/0014-5793(88)80644-6
Palomo, M. et al. Thermus thermophilus glycoside hydrolase family 57 branching enzyme: crystal structure, mechanism of action, and products formed. J. Biol. Chem. 286, 3520–3530 (2011).
pubmed: 21097495
doi: 10.1074/jbc.M110.179515
Bax, H. H. M., van der Maarel, M. & Jurak, E. Alpha-1,4-transglycosylation activity of GH57 glycogen branching enzymes is higher in the absence of a flexible loop with a conserved tyrosine residue. Polym. (Basel) 15, 2777 (2023).
doi: 10.3390/polym15132777
Xiang, G., Leemhuis, H. & van der Maarel, M. Structural elements determining the transglycosylating activity of glycoside hydrolase family 57 glycogen branching enzymes. Proteins 90, 155–163 (2022).
pubmed: 34346105
doi: 10.1002/prot.26200
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).
pubmed: 24270786
doi: 10.1093/nar/gkt1178
Ye, X. et al. Glycogen branching enzyme with a novel chain transfer mode derived from corallococcus sp. strain EGB and its potential applications. J. Agric. Food Chem. 70, 4735–4748 (2022).
pubmed: 35404056
doi: 10.1021/acs.jafc.2c01621
Janeček, Š. & Svensson, B. Amylolytic glycoside hydrolases. Cell. Mol. Life Sci. 73, 2601–2602 (2016).
pubmed: 27130913
pmcid: 11108492
doi: 10.1007/s00018-016-2240-z
Hayashi, M. et al. Bound substrate in the structure of cyanobacterial branching enzyme supports a new mechanistic model. J. Biol. Chem. 292, 5465–5475 (2017).
pubmed: 28193843
doi: 10.1074/jbc.M116.755629
Kelly, R. M., Leemhuis, H. & Dijkhuizen, L. Conversion of a cyclodextrin glucanotransferase into an alpha-amylase: assessment of directed evolution strategies. Biochemistry 46, 11216–11222 (2007).
pubmed: 17824673
doi: 10.1021/bi701160h
Ban, X. et al. Alternations in the chain length distribution of polysaccharides by adjusting the active sites of the 1,4-α-glucan branching enzyme. Food Res. Int. 162, 112119 (2022).
pubmed: 36461352
doi: 10.1016/j.foodres.2022.112119
Feng, L. et al. Crystal structures of Escherichia coli branching enzyme in complex with linear oligosaccharides. Biochemistry 54, 6207–6218 (2015).
pubmed: 26280198
doi: 10.1021/acs.biochem.5b00228
Feng, L. et al. Crystal structures of Escherichia coli branching enzyme in complex with cyclodextrins. Acta Crystallogr. D. Struct. Biol. 72, 641–647 (2016).
pubmed: 27139627
doi: 10.1107/S2059798316003272
Chaen, K., Noguchi, J., Omori, T., Kakuta, Y. & Kimura, M. Crystal structure of the rice branching enzyme I (BEI) in complex with maltopentaose. Biochem. Biophys. Res. Commun. 424, 508–511 (2012).
pubmed: 22771800
doi: 10.1016/j.bbrc.2012.06.145
Froese, D. S. et al. Structural basis of glycogen branching enzyme deficiency and pharmacologic rescue by rational peptide design. Hum. Mol. Genet. 24, 5667–5676 (2015).
pubmed: 26199317
pmcid: 4581599
doi: 10.1093/hmg/ddv280
Gavgani, H. N. et al. A structural explanation for the mechanism and specificity of plant branching enzymes I and IIb. J. Biol. Chem. 298, 101395 (2022).
pubmed: 34762912
doi: 10.1016/j.jbc.2021.101395
Fawaz, R. et al. The structure of maltooctaose-bound escherichia coli branching enzyme suggests a mechanism for donor chain specificity. Molecules 28, 4377 (2023).
pubmed: 37298853
pmcid: 10254366
doi: 10.3390/molecules28114377
Zhang, X., Leemhuis, H. & van der Maarel, M. Synthesis of highly branched alpha-glucans with different structures using GH13 and GH57 glycogen branching enzymes. Carbohydr. Polym. 216, 231–237 (2019).
pubmed: 31047062
doi: 10.1016/j.carbpol.2019.04.038
Li, D. et al. A cold-active 1,4-alpha-glucan branching enzyme from Bifidobacterium longum reduces the retrogradation and enhances the slow digestibility of wheat starch. Food Chem. 324, 126855 (2020).
pubmed: 32344341
doi: 10.1016/j.foodchem.2020.126855
Sorndech, W. et al. Synergistic amylomaltase and branching enzyme catalysis to suppress cassava starch digestibility. Carbohydr. Polym. 132, 409–418 (2015).
pubmed: 26256365
doi: 10.1016/j.carbpol.2015.05.084
Huang, J. et al. Discovery of deaminase functions by structure-based protein clustering. Cell 186, 3182–3195 (2023).
pubmed: 37379837
doi: 10.1016/j.cell.2023.05.041
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pubmed: 34265844
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50, D439–D444 (2022).
pubmed: 34791371
doi: 10.1093/nar/gkab1061
Laurents, D. V. AlphaFold 2 and NMR spectroscopy: partners to understand protein structure, dynamics and function. Front. Mol. Biosci. 9, 906437 (2022).
pubmed: 35655760
pmcid: 9152297
doi: 10.3389/fmolb.2022.906437
Yang, J., Wang, Y. & Zhang, Y. ResQ: an approach to unified estimation of B-factor and residue-specific error in protein structure prediction. J. Mol. Biol. 428, 693–701 (2016).
pubmed: 26437129
doi: 10.1016/j.jmb.2015.09.024
Honorato, R. V. et al. Structural biology in the clouds: the WeNMR-EOSC Ecosystem. Front Mol. Biosci. 8, 729513 (2021).
pubmed: 34395534
pmcid: 8356364
doi: 10.3389/fmolb.2021.729513
Honorato, R. V. et al. The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nat Protoc (2024).
Boittier, E. D., Burns, J. M., Gandhi, N. S. & Ferro, V. GlycoTorch Vina: docking designed and tested for glycosaminoglycans. J. Chem. Inf. Model 60, 6328–6343 (2020).
pubmed: 33152249
doi: 10.1021/acs.jcim.0c00373
Nivedha, A. K., Thieker, D. F., Makeneni, S., Hu, H. & Woods, R. J. Vina-Carb: Improving glycosidic angles during carbohydrate docking. J. Chem. Theory Comput. 12, 892–901 (2016).
pubmed: 26744922
pmcid: 5140039
doi: 10.1021/acs.jctc.5b00834
Rodrigues, J. P. et al. Clustering biomolecular complexes by residue contacts similarity. Proteins 80, 1810–7 (2012).
pubmed: 22489062
doi: 10.1002/prot.24078
Wang, Z. et al. Expression and characterization of an extremely thermophilic 1,4-α-glucan branching enzyme from Rhodothermus obamensis STB05. Protein Expr. Purif. 164, 105478 (2019).
pubmed: 31421223
doi: 10.1016/j.pep.2019.105478
Löffler, P., Schmitz, S., Hupfeld, E., Sterner, R. & Merkl, R. Rosetta:MSF: a modular framework for multi-state computational protein design. PLoS Comput. Biol. 13, e1005600 (2017).
pubmed: 28604768
pmcid: 5484525
doi: 10.1371/journal.pcbi.1005600
Ban, X. et al. The amino acid on the top of the active groove allosterically modulates product specificity of the 1,4-α-glucan branching enzyme. Food Chem. 384, 132458 (2022).
pubmed: 35219229
doi: 10.1016/j.foodchem.2022.132458
Jiang, H. et al. Flexible loop in carbohydrate-binding module 48 allosterically modulates substrate binding of the 1,4-alpha-glucan branching enzyme. J. Agric. Food Chem. 69, 5755–5763 (2021).
pubmed: 33988022
doi: 10.1021/acs.jafc.1c00293
Takata, H. et al. Properties and active center of the thermostable branching enzyme from Bacillus stearothermophilus. Appl. Environ. Microbiol. 60, 3096–3104 (1994).
pubmed: 7944355
pmcid: 201776
doi: 10.1128/aem.60.9.3096-3104.1994
Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).
pubmed: 27754618
doi: 10.1016/S0076-6879(97)76066-X
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
pubmed: 19461840
pmcid: 2483472
doi: 10.1107/S0021889807021206
Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D. Struct. Biol. 75, 861–877 (2019).
pubmed: 31588918
pmcid: 6778852
doi: 10.1107/S2059798319011471
Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D. Biol. Crystallogr. 68, 352–367 (2012).
pubmed: 22505256
pmcid: 3322595
doi: 10.1107/S0907444912001308
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D. Biol. Crystallogr. 60, 2126–2132 (2004).
pubmed: 15572765
doi: 10.1107/S0907444904019158
Brünger, A. T. et al. Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr. D. Biol. Crystallogr. 54, 905–921 (1998).
pubmed: 9757107
doi: 10.1107/S0907444998003254
Zong, Z. et al. Mechanism and biomass association of glucuronoyl esterase: an α/β hydrolase with potential in biomass conversion. Nat. Commun. 13, 1449 (2022).
pubmed: 35304453
pmcid: 8933493
doi: 10.1038/s41467-022-28938-w
Knott, B. C., Crowley, M. F., Himmel, M. E., Stahlberg, J. & Beckham, G. T. Carbohydrate-protein interactions that drive processive polysaccharide translocation in enzymes revealed from a computational study of cellobiohydrolase processivity. J. Am. Chem. Soc. 136, 8810–8819 (2014).
pubmed: 24869982
doi: 10.1021/ja504074g
Zong, Z. et al. Lysine mutation of the claw-arm-like loop accelerates catalysis by cellobiohydrolases. J. Am. Chem. Soc. 141, 14451–14459 (2019).
pubmed: 31432675
doi: 10.1021/jacs.9b08477
Zong, Z., Liu, X., Ye, Z. & Liu, D. A double-switch pHLIP system enables selective enrichment of circulating tumor microenvironment-derived extracellular vesicles. Proc. Natl Acad. Sci. 120, e2214912120 (2023).
pubmed: 36595702
pmcid: 9926244
doi: 10.1073/pnas.2214912120
Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).
pubmed: 16222654
pmcid: 2486339
doi: 10.1002/jcc.20289
Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010).
pubmed: 19575467
pmcid: 2888302
doi: 10.1002/jcc.21367
MacKerell, A. D. et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586–3616 (1998).
pubmed: 24889800
doi: 10.1021/jp973084f
MacKerell, A. D. Jr., Feig, M. & Brooks, C. L. 3rd Improved treatment of the protein backbone in empirical force fields. J. Am. Chem. Soc. 126, 698–699 (2004).
pubmed: 14733527
doi: 10.1021/ja036959e
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
doi: 10.1063/1.445869
Uhlenbeck, G. E. & Ornstein, L. S. On the theory of the Brownian motion. Phys. Rev. 36, 823–841 (1930).
doi: 10.1103/PhysRev.36.823
Feller, S. E., Zhang, Y., Pastor, R. W. & Brooks, B. R. Constant pressure molecular dynamics simulation: The langevin piston method. J. Chem. Phys. 103, 4613–4621 (1995).
doi: 10.1063/1.470648
Miyamoto, S. & Kollman, P. A. Settle: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13, 952–962 (1992).
doi: 10.1002/jcc.540130805
Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).
doi: 10.1016/0021-9991(77)90098-5
Andersen, H. C. Rattle: a “velocity” version of the shake algorithm for molecular dynamics calculations. J. Comput. Phys. 52, 24–34 (1983).
doi: 10.1016/0021-9991(83)90014-1
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–8 (1996).
Fu, H., Shao, X., Cai, W. & Chipot, C. Taming rugged free energy landscapes using an average force. Acc. Chem. Res. 52, 3254–3264 (2019).
pubmed: 31680510
doi: 10.1021/acs.accounts.9b00473
Marcos-Alcalde, I., Setoain, J., Mendieta-Moreno, J. I., Mendieta, J. & Gómez-Puertas, P. MEPSA: minimum energy pathway analysis for energy landscapes. Bioinformatics 31, 3853–5 (2015).
pubmed: 26231428
doi: 10.1093/bioinformatics/btv453