We applied the transition path sampling (TPS) method to study the translocation step of the catalytic mechanism of galactofuranosyl transferase 2 (GlfT2). Using TPS in the field of enzymatic reactions is still relatively rare, and we show its effectiveness on this enzymatic system. We decipher an unknown mechanism of the translocation step and, thus, provide a complete understanding of the catalytic mechanism of GlfT2 at the atomistic level. The GlfT2 enzyme is involved in the formation of the mycobacterial cell wall and transfers galactofuranose (Galf) from UDP-Galf onto a growing acceptor Galf chain. The biosynthesis of the galactan chain is accomplished in a processive manner, with the growing acceptor substrate remaining bound to GlfT2. The glycosidic bond formed by GlfT2 between the two Galf residues alternates between β-(1-6) and β-(1-5) linkages. The translocation of the growing galactan between individual additions of Galf residues is crucial for the function of GlfT2. Analysis of unbiased trajectory ensembles revealed that the translocation proceeds differently depending on the glycosidic linkage between the last two Galf residues. We also showed that the protonation state of the catalytic residue Asp372 significantly influences the translocation. Approximate transition state structures and potential energy reaction barriers of the translocation process were determined. The calculated potential reaction barriers in the range of 6-14 kcal/mol show that the translocation process is not the rate-limiting step in galactan biosynthesis.

Central European Institute of Technology Masaryk University Brno 601 77 Czech Republic

Faculty of Physics University of Vienna Wien 1090 Austria

Faculty of Science National Centre for Biomolecular Research Masaryk University Brno 601 77 Czech Republic

Institute of Chemistry Slovak Academy of Sciences Bratislava 84536 Slovak Republic

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May J. F.; Splain R. A.; Brotschi C.; Kiessling L. L. A Tethering Mechanism for Length Control in a Processive Carbohydrate Polymerization. Proc. Natl. Acad. Sci. USA 2009, 106, 11851–11856. 10.1073/pnas.0901407106. PubMed DOI PMC

Levengood M. R.; Splain R. A.; Kiessling L. L. Monitoring Processivity and Length Control of a Carbohydrate Polymerase. J. Am. Chem. Soc. 2011, 133, 12758–12766. 10.1021/ja204448t. PubMed DOI PMC

May J. F.; Levengood M. R.; Splain R. A.; Brown C. D.; Kiessling L. L. A Processive Carbohydrate Polymerase That Mediates Bifunctional Catalysis Using a Single Active Site. Biochemistry 2012, 51, 1148–1159. 10.1021/bi201820p. PubMed DOI PMC

Rose N. L.; Completo G. C.; Lin S.-J.; McNeil M.; Palcic M. M.; Lowary T. L. Expression, Purification, and Characterization of a Galactofuranosyltransferase Involved in Mycobacterium tuberculosis Arabinogalactan Biosynthesis. J. Am. Chem. Soc. 2006, 128, 6721–6729. 10.1021/ja058254d. PubMed DOI

Completo G. C.; Lowary T. L. Synthesis of Galactofuranose-containing Acceptor Substrates for Mycobacterial Galactofuranosyl transferases. J. Org. Chem. 2008, 73, 4513–4525. 10.1021/jo800457j. PubMed DOI

Wheatley R. W.; Zheng R. B.; Richards M. R.; Lowary T. L.; Ng K. K. S. Tetrameric Structure of the GlfT2 Galactofuranosyltransferase Reveals a Scaffold for the Assembly of Mycobacterial Arabinogalactan. J. Biol. Chem. 2012, 287, 28132–28143. 10.1074/jbc.M112.347484. PubMed DOI PMC

Szczepina M. G.; Zheng R. B.; Completo G. C.; Lowary T. L.; Pinto B. M. STD-NMR Studies of Two Acceptor Substrates of GlfT2, a Galactofuranosyltransferase from Mycobacterium Tuberculosis: Epitope Mapping Studies. Bioorg. Med. Chem. 2010, 18, 5123–5128. 10.1016/j.bmc.2010.05.069. PubMed DOI

Janoš P.; Kozmon S.; Tvaroška I.; Koča J. How Mycobacterium Tuberculosis Galactofuranosyl Transferase 2 (GlfT2) Generates Alternating β-(1–6) and β-(1–5) Linkages: A QM/MM Molecular Dynamics Study of the Chemical Steps. Chem. – Eur. J. 2018, 24, 7051–7059. 10.1002/chem.201800558. PubMed DOI

Dellago C.; Bolhuis P.; Geissler P. L. Transition path sampling. Adv. Chem. Phys. 2002, 123, 1–78. 10.1002/0471231509.ch1. PubMed DOI

Brotzakis Z. F.; Bolhuis P. G. A One-way Shooting Algorithm for Transition Path Sampling of Asymmetric Barriers. J. Chem. Phys. 2016, 145, 16411210.1063/1.4965882. PubMed DOI

Knott B. C.; Crowley M. F.; Himmel M. E.; Ståhlberg 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. 2014, 136, 8810–8819. 10.1021/ja504074g. PubMed DOI

Maier J. A.; Martinez C.; Kasavajhala K.; Wickstrom L.; Hauser K. E.; Simmerling C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696–3713. 10.1021/acs.jctc.5b00255. PubMed DOI PMC

Kirschner K. N.; Yongye A. B.; Tschampel S. M.; González-Outeiriño J.; Daniels C. R.; Foley B. L.; Woods R. J. GLYCAM06: A Generalizable Biomolecular Force Field. Carbohydrates. J. Comput. Chem. 2008, 29, 622–655. 10.1002/jcc.20820. PubMed DOI PMC

Dupradeau F. Y.; Cézard C.; Lelong R.; Stanislawiak E.; Pêcher J.; Delepine J. C.; Cieplak P. RE DD. B.: A Database for RESP and ESP Atomic Charges, and Force Field Libraries. Nucleic Acids Res. 2007, 36, D360–D367. 10.1093/nar/gkm887. PubMed DOI PMC

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. 1983, 79, 926.10.1063/1.445869. DOI

Joung I. S.; Cheatham T. E. Determination of Alkali and Halide Monovalent Ion Parameters for Use in Explicitly Solvated Biomolecular Simulations. J. Phys. Chem. B 2008, 112, 9020–9041. 10.1021/jp8001614. PubMed DOI PMC

Allnér O.; Nilsson L.; Villa A. Magnesium Ion–Water Coordination and Exchange in Biomolecular Simulations. J. Chem. Theory Comput. 2012, 8, 1493–1502. 10.1021/ct3000734. PubMed DOI

Salomon-Ferrer R.; Götz A. W.; Poole D.; Le Grand S.; Walker R. C. Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald. J. Chem. Theory Comput. 2013, 9, 3878–3888. 10.1021/ct400314y. PubMed DOI

Case D. A.; Babin V.; Berryman J. T.; Betz R. M.; Cai Q.; Cerutti D. S.; Cheatham T. E. III; Darden T. A.; Duke R. E.; Gohlke H.; et al.AMBER 14; University of California:San Francisco, 2014.

Kulhánek P.; Bouchal T.; Durník I.; Štěpán J.; Fuxreiter M.; Mones L.; Petřek M.; Střelcová Z.. PMFLib - A Toolkit for Free Energy Calculations. https://pmflib.ncbr.muni.cz, 2018.

Swenson D. W. H.; Prinz J.-H.; Noe F.; Chodera J. D.; Bolhuis P. G. OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics. J. Chem. Theory Comput. 2019, 15, 813–836. 10.1021/acs.jctc.8b00626. PubMed DOI PMC

Swenson D. W. H.; Prinz J.-H.; Noe F.; Chodera J. D.; Bolhuis P. G. OpenPathSampling: A Python Framework for Path Sampling Simulations. 2. Building and Customizing Path Ensembles and Sample Schemes. J. Chem. Theory Comput. 2019, 15, 837–856. 10.1021/acs.jctc.8b00627. PubMed DOI PMC

Eastman P.; Swails J.; Chodera J. D.; McGibbon R. T.; Zhao Y.; Beauchamp K. A.; Wang L. P.; Simmonett A. C.; Harrigan M. P.; Stern C. D.; et al. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLOS Comput. Biol. 2017, 13, e100565910.1371/journal.pcbi.1005659. PubMed DOI PMC

Sivak D. A.; Chodera J. D.; Crooks G. E. Time Step Rescaling Recovers Continuous-Time Dynamical Properties for Discrete-Time Langevin Integration of Nonequilibrium Systems. J. Phys. Chem. B 2014, 118, 6466–6474. 10.1021/jp411770f. PubMed DOI PMC

Jonsson H.; Mills G.; Jacobsen K.. Nudged Elastic Band Method for Finding Minimum Energy Paths of Transitions. In Classical and Quantum Dynamics in Condensed Phase Simulations; Berne B. J.; Ciccotti G.; Coker D. F.; World Scientific: Singapore, 1998; pp 385–404.

Kolsbjerg E. L.; Groves M. N.; Hammer B. An Automated Nudged Elastic Band Method. J. Chem. Phys. 2016, 145, 09410710.1063/1.4961868. PubMed DOI

Larsen A. H.; Mortensen J. J.; Blomqvist J.; Castelli I. E.; Christensen R.; Dułak M.; Friis J.; Groves M. N.; Hammer B.; Hargus C.; et al. The Atomic Simulation Environment—a Python Library for Working with Atoms. J. Phys. Condens. Matter 2017, 29, 273002. PubMed

Onufriev A.; Bashford D.; Case D. A. Exploring Protein Native States and Large-scale Conformational Changes with a Modified Generalized Born Model. Proteins: Struct., Funct., Bioinf. 2004, 55, 383–394. 10.1002/prot.20033. PubMed DOI