GENESIS: a hybrid‐parallel and multi‐scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

Software Focus

Jaewoon Jung, Takaharu Mori, Chigusa Kobayashi, Yasuhiro Matsunaga, Takao Yoda, Michael Feig, Yuji Sugita

Published Online: May 07 2015

DOI: 10.1002/wcms.1220

Full article on Wiley Online Library: HTML PDF

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GENESIS (Generalized‐Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all‐atom force‐field models as well as coarse‐grained Go‐like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large‐scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T‐REMD, REUS, multi‐dimensional REMD for both all‐atom and Go‐like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three‐dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real‐space and reciprocal‐space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods

The hybrid (MPI + OpenMP) parallelization scheme in SPDYN. (a) Design of the hybrid parallelization of real space interaction in SPDYN. Adjacent cells for send/receive communication are colored by gray, and MPI communications are shown as black arrows. Midpoint candidate cells for cell pairs (a,b) and (c,d) are colored by green. (b) Two‐dimensional views of charge‐grid assignment in SPDYN and other MD programs using slab or pencil decomposition FFT.

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REMD simulations of POPC/DMPC mixed lipid bilayers. (a) Snapshots in the MD simulation at T = 303.15 K and P = 1 atm (upper panels), and snapshots in the T‐REMD simulation at T = 303.15 K and P = 1 atm (lower panels). POPC and DMPC are coloured by blue and cyan, respectively. Structures in unit and image cells are shown together. (b) Mean‐square displacements (MSD) of POPC lipids (left panel) and DMPC lipids (right panel) in MD, T‐REMD, γ‐REMD, and γT‐REMD. MSD is computed for each replica and averaged over all replicas. (c) Histogram of degree of mixing (number of POPC–DMPC contact pairs) at T = 303.15 K and γ = 0 dyn/cm in MD, T‐REMD, γ‐REMD, and γT‐REMD.

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Benchmark performance of MD simulations of (a) DHFR, (b) ApoA1, and (c) STMV on PC clusters, and (d) STMV, macromolecular crowding systems consisting of (e) 11.7 million atoms and (f) 103.7 million atoms on K computer.

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File input/output in GENESIS. (a) Scheme in standard MD simulations. PDB, Protein Data Bank; PSF, Protein Structure File; PAR, Parameter; RST, restart file. Coordinates and velocities are generated in the standard DCD file format. (b) Scheme in REMD simulations. REM, parameter index file. (c) Scheme in large‐scale MD simulations. PRST, parallel restart files; PCRD, parallel coordinates files.

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References

McCammon, JA, Gelin, BR, Karplus, M. Dynamics of folded proteins. Nature 1977, 267:585–590.
Levitt, M, Sharon, R. Accurate simulation of protein dynamics in solution. Proc Natl Acad Sci USA 1988, 85:7557–7561.
Duan, Y, Kollman, PA. Pathways to a protein folding intermediate observed in a 1‐microsecond simulation in aqueous solution. Science 1998, 282:740–744.
Berneche, S, Roux, B. Energetics of ion conduction through the K⁺ channel. Nature 2001, 414:73–77.
de Groot, BL, Grubmuller, H. Water permeation across biological membranes: mechanism and dynamics of aquaporin‐1 and GlpF. Science 2001, 294:2353–2357.
Tajkhorshid, E, Nollert, P, Jensen, MO, Miercke, LJW, O`Connell, J, Stroud, RM, Schulten, K. Control of the selectivity of the aquaporin water channel family by global orientational tuning. Science 2002, 296:525–530.
Gumbart, J, Trabuco, LG, Schreiner, E, Villa, E, Schulten, K. Regulation of the protein‐conducting channel by a bound ribosome. Structure 2009, 17:1453–1464.
Feig, M, Burton, ZF. RNA polymerase II with open and closed trigger loops: active site dynamics and nucleic acid translocation. Biophys J 2010, 99:2577–2586.
Sanbonmatsu, KY. Computational studies of molecular machines: the ribosome. Curr Opin Struct Biol 2012, 22:168–174.
Brooks, BR, Bruccoleri, RE, Olafson, BD, States, DJ, Swaminathan, S, Karplus, M. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J Comput Chem 1983, 4:187–217.
Brooks, BR, Brooks, CL III, Mackerell, AD Jr, Nilsson, L, Petrella, RJ, Roux, B, Won, Y, Archontis, G, Bartels, C, Boresch, S, et al. CHARMM: the biomolecular simulation program. J Comput Chem 2009, 30:1545–1614.
Salomon‐Ferrer, R, Case, DA, Walker, RC. An overview of the Amber biomolecular simulation package. WIREs Comput Mol Sci 2013, 3:198–210.
Phillips, JC, Braun, R, Wang, W, Gumbart, J, Tajkhorshid, E, Villa, E, Chipot, C, Skeel, RD, Kale, L, Schulten, K. Scalable molecular dynamics with NAMD. J Comput Chem 2005, 26:1781–1802.
Pronk, S, Pall, S, Schulz, R, Larsson, P, Bjelkmar, P, Apostolov, R, Shirts, MR, Smith, JC, Kasson, PM, van der Spoel, D, et al. GROMACS 4.5: a high‐throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29:845–854.
Bowers, KJ, Chow, E, Xu, H, Dror, RO, Eastwood, MP, Gregersen, BA, Klepeis, JL, Kolossvary, I, Moraes, MA, Sacerdoti, FD, et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC`06), Tampa, FL, 2006.
Ikeguchi, M. Partial rigid‐body dynamics in NPT, NPAT and NPγT ensembles for proteins and membranes. J Comput Chem 2004, 25:529–541.
Andoh, Y, Yoshii, N, Fujimoto, K, Mizutani, K, Kojima, H, Yamada, A, Okazaki, S, Kawaguchi, K, Nagao, H, Iwahashi, K, et al. MODYLAS: a highly parallelized general‐purpose molecular dynamics simulation program for large‐scale systems with long‐range forces calculated by fast multipole method (FMM) and highly scalable fine‐grained new parallel processing algorithms. J Chem Theory Comput 2013, 9:3201–3209.
Service, RF. Who will step up to exascale? Science 2013, 339:264–266.
Owens, JD, Houston, M, Luebke, D, Green, S, Stone, JE, Phillips, JC. GPU computing. Proc IEEE 2008, 96:879–899.
Narumi, T, Ohno, Y, Okimoto, N, Koishi, T, Suenaga, A, Futatsugi, N, Yanai, R, Himeno, R, Fujikawa, S, Taiji, M. A 55 TFLOPS simulation of amyloid‐forming peptides from yeast prion Sup35 with the special‐purpose computer system MDGRAPE‐3. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC`06), Tampa, FL, 2006.
Ohmura, I, Morimoto, G, Ohno, Y, Hasegawa, A, Taiji, M. MDGRAPE‐4: a special‐purpose computer system formolecular dynamics simulations. Philos Trans A Math Phys Eng Sci 2014, 372:20130387.
Shaw, DE, Dror, RO, Salmon, JK, Grossman, JP, Mackenzie, KM, Bank, JA, Young, C, Deneroff, MM, Batson, B, Bowers, KJ, et al. Millisecond‐scale molecular dynamics simulations on Anton. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC`09), Portland OR, 2009.
Shaw, DE, Bank, JA, Batson, B, Butts, JA, Chao, JC, Deneroff, MM, Dror, RO, Even, A, Fenton, CH, Forte, A, et al. Anton 2: raising the bar for performance and programmability in a special‐purpose molecular dynamics supercomputer. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC`14), New Orleans, LA, 2014.
Shaw, DE, Maragakis, P, Lindorff‐Larsen, K, Piana, S, Dror, RO, Eastwood, MP, Bank, JA, Jumper, JM, Salmon, JK, Shan, YB, et al. Atomic‐level characterization of the structural dynamics of proteins. Science 2010, 330:341–346.
Ponder, JW, Case, DA. Force fields for protein simulations. Adv Protein Chem 2003, 66:27–85.
Zhu, X, Lopes, PEM, MacKerell, AD. Recent developments and applications of the CHARMM force fields. WIREs Comput Mol Sci 2012, 2:167–185.
Piana, S, Lindorff‐Larsen, K, Shaw, DE. How robust are protein folding simulations with respect to force field parameterization? Biophys J 2011, 100:L47–L49.
McGuffee, SR, Elcock, AH. Diffusion, crowding %26 protein stability in a dynamic molecular model of the bacterial cytoplasm. PLoS Comput Biol 2010, 6:e1000694.
Ando, T, Skolnick, J. Crowding and hydrodynamic interactions likely dominate in vivo macromolecular motion. Proc Natl Acad Sci USA 2010, 107:18457–18462.
Feig, M, Sugita, Y. Reaching new levels of realism in modeling biological macromolecules in cellular environments. J Mol Graph Model 2013, 45:144–156.
Jung, J, Mori, T, Sugita, Y. Efficient lookup table using a linear function of inverse distance squared. J Comput Chem 2013, 34:2412–2420.
Jung, J, Mori, T, Sugita, Y. Midpoint cell method for hybrid (MPI + OpenMP) parallelization of molecular dynamics simulations. J Comput Chem 2014, 35:1064–1072.
Eleftheriou, M, Moreira, JE, Fitch, BG, Germain, RS. A volumetric FFT for BlueGene/L. High Performance Computing—Hipc 2003, 2913:194–203.
Darden, T, York, D, Pedersen, L. Particle mesh Ewald: An N · log(N) method for Ewald sums in large systems. J Chem Phys 1993, 98:10089–10092.
Essmann, U, Perera, L, Berkowitz, ML, Darden, T, Lee, H, Pedersen, LG. A smooth particle mesh Ewald method. J Chem Phys 1995, 103:8577–8593.
Mitsutake, A, Sugita, Y, Okamoto, Y. Generalized‐ensemble algorithms for molecular simulations of biopolymers. Biopolymers 2001, 60:96–123.
Hamelberg, D, Mongan, J, McCammon, JA. Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J Chem Phys 2004, 120:11919–11929.
Liwo, A, Czaplewski, C, Ołdziej, S, Scheraga, HA. Computational techniques for efficient conformational sampling of proteins. Curr Opin Struct Biol 2008, 18:134–139.
Huang, XH, Bowman, GR, Bacallado, S, Pande, VS. Rapid equilibrium sampling initiated from nonequilibrium data. Proc Natl Acad Sci U S A 2009, 106:19765–19769.
Barducci, A, Bonomi, M, Parrinello, M. Metadynamics. WIREs Comput Mol Sci 2011, 1:826–843.
Sutto, L, Marsili, S, Gervasio, FL. New advances in metadynamics. WIREs Comput Mol Sci 2012, 2:771–779.
Kenzaki, H, Koga, N, Hori, N, Kanada, R, Li, WF, Okazaki, K, Yao, XQ, Takada, S. CafeMol: a coarse‐grained biomolecular simulator for simulating proteins at work. J Chem Theory Comput 2011, 7:1979–1989.
Takada, S. Coarse‐grained molecular simulations of large biomolecules. Curr Opin Struct Biol 2012, 22:130–137.
Saunders, MG, Voth, GA. Coarse‐graining methods for computational biology. Annu Rev Biophys 2013, 42:73–93.
Kar, P, Gopal, SM, Cheng, YM, Predeus, A, Feig, M. PRIMO: a transferable coarse‐grained force field for proteins. J Chem Theory Comput 2013, 9:3769–3788.
Ingolfsson, HI, Lopez, CA, Uusitalo, JJ, de Jong, DH, Gopal, SM, Periole, X, Marrink, SJ. The power of coarse graining in biomolecular simulations. WIREs Comput Mol Sci 2014, 4:225–248.
Sugita, Y, Okamoto, Y. Replica‐exchange molecular dynamics method for protein folding. Chem Phys Lett 1999, 314:141–151.
Sugita, Y, Okamoto, Y. Replica‐exchange multicanonical algorithm and multicanonical replica‐exchange method for simulating systems with rough energy landscape. Chem Phys Lett 2000, 329:261–270.
Sugita, Y, Kitao, A, Okamoto, Y. Multidimensional replica‐exchange method for free‐energy calculations. J Chem Phys 2000, 113:6042–6051.
Fukunishi, H, Watanabe, O, Takada, S. On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: application to protein structure prediction. J Chem Phys 2002, 116:9058–9067.
Park, S, Kim, T, Im, W. Transmembrane helix assembly by window exchange umbrella sampling. Phys Rev Lett 2012, 108:108102.
Mori, T, Jung, J, Sugita, Y. Surface‐tension replica‐exchange molecular dynamics method for enhanced sampling of biological membrane systems. J Chem Theory Comput 2013, 9:5629–5640.
Taketomi, H, Ueda, Y, Go, N. Studies on protein folding, unfolding and fluctuations by computer‐simulation, 1: effect of specific amino‐acid sequence represented by specific inter‐unit interactions. Int J Pept Protein Res 1975, 7:445–459.
Clementi, C, Nymeyer, H, Onuchic, JN. Topological and energetic factors: what determines the structural details of the transition state ensemble and "en‐route" intermediates for protein folding? An investigation for small globular proteins. J Mol Biol 2000, 298:937–953.
Karanicolas, J, Brooks, CL. Improved Go‐like models demonstrate the robustness of protein folding mechanisms towards non‐native interactions. J Mol Biol 2003, 334:309–325.
Whitford, PC, Noel, JK, Gosavi, S, Schug, A, Sanbonmatsu, KY, Onuchic, JN. An all‐atom structure‐based potential for proteins: bridging minimal models with all‐atom empirical forcefields. Proteins 2009, 75:430–441.
Zhang, YH, Feller, SE, Brooks, BR, Pastor, RW. Computer‐simulation of liquid/liquid interfaces, I: theory and application to octane/water. J Chem Phys 1995, 103:10252–10266.
Takahashi, D. An implementation of parallel 3‐D FFT with 2‐D decomposition on a massively parallel cluster of multi‐core processors. In: Wyrzykowski, R, Dongarra, J, Karczewski, K, Wasniewski, J, eds. PPAM 2009. LNCS, vol. 6067. Berlin Heidelberg: Springer; 2010, 606–614.
Neria, E, Fischer, S, Karplus, M. Simulation of activation free energies in molecular systems. J Chem Phys 1996, 105:1902–1921.
MacKerell, AD Jr, Bashford, D, Bellott, M, Dunbrack, RL Jr, Evanseck, JD, Field, MJ, Fischer, S, Gao, J, Guo, H, Ha, S, et al. All‐atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 1998, 102:3586–3616.
MacKerell, AD Jr, Feig, M, Brooks, CL III. Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem 2004, 25:1400–1415.
MacKerell, AD, Banavali, N, Foloppe, N. Development and current status of the CHARMM force field for nucleic acids. Biopolymers 2001, 56:257–265.
Klauda, JB, Venable, RM, Freites, JA, O`Connor, JW, Tobias, DJ, Mondragon‐Ramirez, C, Vorobyov, I, MacKerell, AD Jr, Pastor, RW. Update of the CHARMM all‐atom additive force field for lipids: validation on six lipid types. J Phys Chem B 2010, 114:7830–7843.
Best, RB, Zhu, X, Shim, J, Lopes, PEM, Mittal, J, Feig, M, MacKerell, AD. Optimization of the additive CHARMM all‐atom protein force field targeting improved sampling of the backbone phi, psi and side‐chain chi(1) and chi(2) dihedral angles. J Chem Theory Comput 2012, 8:3257–3273.
Raman, EP, Guvench, O, MacKerell, AD. CHARMM additive all‐atom force field for glycosidic linkages in carbohydrates involving furanoses. J Phys Chem B 2010, 114:12981–12994.
Feig, M, Karanicolas, J, Brooks, CL III. MMTSB tool set: enhanced sampling and multiscale modeling methods for applications in structural biology. J Mol Graph Model 2004, 22:377–395.
Available at: http://mmtsb.org/webservices/gomodel.html. (Accessed March 9, 2015).
Kitao, A, Hirata, F, Go, N. The effects of solvent on the conformation and the collective motions of protein—normal mode analysis and molecular‐dynamics simulations of melittin in water and in vacuum. Chem Phys 1991, 158:447–472.
Lindahl, E, Hess, B, van der Spoel, D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 2001, 7:306–317.
Nilsson, L. Efficient table lookup without inverse square roots for calculation of pair wise atomic interactions in classical simulations. J Comput Chem 2009, 30:1490–1498.
Available at: http://www.aics.riken.jp. (Accessed March 9, 2015).
Plimpton, S. Fast parallel algorithms for short‐range molecular‐dynamics. J Comput Phys 1995, 117:1–19.
Crowley, MF, Darden, TA, Cheatham, TE, Deerfield, DW. Adventures in improving the scaling and accuracy of a parallel molecular dynamics program. J Supercomput 1997, 11:255–278.
Bowers, KJ, Dror, RO, Shaw, DE. The midpoint method for parallelization of particle simulations. J Chem Phys 2006, 124:184109.
Shaw, DE. A fast, scalable method for the parallel evaluation of distance‐limited pairwise particle interactions. J Comput Chem 2005, 26:1803.
Hansmann, UHE, Okamoto, Y. Generalized‐ensemble Monte Carlo method for systems with rough energy landscape. Phys Rev E 1997, 56:2228–2233.
Martyna, GJ, Tobias, DJ, Klein, ML. Constant pressure molecular dynamics algorithms. J Chem Phys 1994, 101:4177–4189.
Mori, Y, Okamoto, Y. Replica‐exchange molecular dynamics simulations for various constant temperature algorithms. J Phys Soc Jpn 2010, 79:074001.
Mori, Y, Okamoto, Y. Generalized‐ensemble algorithms for the isobaric‐isothermal ensemble. J Phys Soc Jpn 2010, 79:074003.
Paschek, D, Garcia, AE. Reversible temperature and pressure denaturation of a protein fragment: a replica exchange molecular dynamics simulation study. Phys Rev Lett 2004, 93:238105.
Liu, P, Kim, B, Friesner, RA, Berne, BJ. Replica exchange with solute tempering: a method for sampling biological systems in explicit water. Proc Natl Acad Sci USA 2005, 102:13749–13754.
Itoh, SG, Okumura, H, Okamoto, Y. Replica‐exchange method in van der Waals radius space: overcoming steric restrictions for biomolecules. J Chem Phys 2010, 132:134105.
Itoh, SG, Okumura, H. Coulomb replica‐exchange method: handling electrostatic attractive and repulsive forces for biomolecules. J Comput Chem 2013, 34:622–639.
Okabe, T, Kawata, M, Okamoto, Y, Mikami, M. Replica‐exchange Monte Carlo method for the isobaric‐isothermal ensemble. Chem Phys Lett 2001, 335:435–439.
Humphrey, W, Dalke, A, Schulten, K. VMD: visual molecular dynamics. J Mol Graph Model 1996, 14:33–38.
Hynninen, AP, Crowley, MF. New faster CHARMM molecular dynamics engine. J Comput Chem 2014, 35:406–413.
Available at: http://www.ks.uiuc.edu/Research/namd. (Accessed March 9, 2015).
Ryckaert, JP, Ciccotti, G, Berendsen, HJC. Numerical integration of cartesian equations of motion of a system with constraints: molecular‐dynamics of n‐alkanes. J Comput Phys 1977, 23:327–341.
Miyamoto, S, Kollman, PA. SETTLE: an analytical version of the shake and rattle algorithm for rigid water models. J Comput Chem 1992, 13:952–962.
Tuckerman, M, Berne, BJ, Martyna, GJ. Reversible multiple time scale molecular dynamics. J Chem Phys 1992, 97:1990–2001.
Fraser, CM, Gocayne, JD, White, O, Adams, MD, Clayton, RA, Fleischmann, RD, Bult, CJ, Kerlavage, AR, Sutton, G, Kelley, JM, et al. The minimal gene complement of Mycoplasma‐genitalium. Science 1995, 270:397–403.
Feig, M, Harada, R, Mori, T, Yu, I, Takahashi, K, Sugita, Y. Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology. J Mol Graph Model 2015, 58:1–9.
Vanommeslaeghe, K, Raman, EP, MacKerell, AD. Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Info Model 2012, 52:3155–3168.
Mukherjee, S, Maxfield, FR. Membrane domains. Annu Rev Cell Dev Biol 2004, 20:839–866.
Galla, HJ, Hartmann, W, Theilen, U, Sackmann, E. 2‐Dimensional passive random‐walk in lipid bilayers and fluid pathways in biomembranes. J Membr Biol 1979, 48:215–236.
Li, L, Cheng, JX. Coexisting stripe‐ and patch‐shaped domains in giant unilamellar vesicles. Biochemistry 2006, 45:11819–11826.
Akimov, SA, Kuzmin, PI, Zimmerberg, J, Cohen, FS. Lateral tension increases the line tension between two domains in a lipid bilayer membrane. Phys Rev E 2007, 75:011919.
Ayuyan, AG, Cohen, FS. Raft composition at physiological temperature and pH in the absence of detergents. Biophys J 2008, 94:2654–2666.
Hamada, T, Kishimoto, Y, Nagasaki, T, Takagi, M. Lateral phase separation in tense membranes. Soft Matter 2011, 7:9061–9068.
Uline, MJ, Schick, M, Szleifer, I. Phase behavior of lipid bilayers under tension. Biophys J 2012, 102:517–522.
Portet, T, Gordon, SE, Keller, SL. Increasing membrane tension decreases miscibility temperatures: an experimental demonstration via micropipette aspiration. Biophys J 2012, 103:L35–L37.
Aydin, F, Ludford, P, Dutt, M. Phase segregation in bio‐inspired multi‐component vesicles encompassing double tail phospholipid species. Soft Matter 2014, 10:6096–6108.
Chen, D, Santore, MM. Large effect of membrane tension on the fluid–solid phase transitions of two‐component phosphatidylcholine vesicles. Proc Natl Acad Sci USA 2014, 111:179–184.

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