Structure and Dynamics of Large Biological Molecules: ATP-Binding Cassette (ABC) Transporters Jung-Hsin Lin1 , Jaakko Akola2,3 , and R. O. Jones2 1
Division of Mechanics, Research Center for Applied Sciences and Institute of Biomedical Sciences, Academia Sinica, Taipei 11529, Taiwan and School of Pharmacy, National Taiwan University, Taipei 10051, Taiwan E-mail:
[email protected],
[email protected] 2
3
Institut f¨ur Festk¨orperforschung, Forschungszentrum J¨ulich D-52425 J¨ulich, Germany E-mail: {j.akola, r.jones}@fz-juelich.de
Nanoscience Center, Department of Physics, FI-40014 University of Jyv¨askyl¨a, Finland and Department of Physics, Tampere University of Technology, FI-33101 Tampere, Finland Large-scale molecular dynamics simulations (200,000 atoms for over 1 microsecond) have been performed on the JuGene supercomputer for Sav1866, an ATP-binding cassette transporter originally derived from Staphylococcus aureus. The overall architecture of Sav1866 is remarkably stable during the simulation, while the nucleotide binding domains appear to undergo a series of conformational transitions on a quasi-logarithmic time scale.
1 Introduction Search engines give many thousands of websites when asked for the combination of “grand challenges” and “computational biology” or “bioinformatics”. The combination of biology with information technology is one of the great growth areas in science, and the availability of massively parallel computers (such as the IBM Blue Gene series) is making an essential contribution. Well known applications include drug design, the identification of genes in DNA sequences or determining the structure of a protein given its sequence (the “protein folding problem”). Nevertheless, the use of computers in understanding the atomistic details of reactions in biological molecules is still at an early stage. In principle, a means for studying such problems is provided by the density functional (DF) formalism, which is free of adjustable parameters and has the advantage of allowing chemical bonds to form and be broken. We have performed DF calculations on the hydrolysis of ATP (adenosine 5’-triphosphate)1–3 , the basic mechanism of energy production in mammalian (and other) cells. However, even the most powerful computers limit DF calculations of energy surfaces to systems with up to ca. 1000 atoms over some hundreds of picoseconds. These are severe limitations for most problems of biological interest, where we often need to study systems with tens of thousands of atoms over microseconds or even longer. Such studies still require simulations using classical force fields, and we have implemented these in the present work on ATP-binding cassette (ABC) transporters. Active transport is one of the basic mechanisms to mediate traffic across cellular membranes. ABC transporters are membrane proteins that actively transport substrates through lipid bilayers4 . They form the largest transporter gene family and are vital to many cellular processes in animals, plants, and prokaryotes (organisms without a cell nucleus). The
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transported substrates range from ions and small organic molecules to lipids, carbohydrates, and even whole proteins. Some (including the human P-glycoprotein) contribute to multidrug resistance of cancer cells, and ABC proteins are also associated with cystic fibrosis (mutations in the CFTR protein) and other genetic disorders5, 6.
Figure 1. Sav1866 structure. (a) Backbone of the homodimeric protein, (b) View rotated by 90◦ . TMD denotes the transmembrane domains, NBD the nucleotide (ATP/ADP) binding domains, ICL the intracellular loops, and ECL the extracellular loops between transmembrane helices.
Selective uptake and efflux by ABC transporters is driven against concentration gradients using energy derived from the binding and hydrolysis of ATP, but the mechanism is not well understood. All ABC transporters have an ATP binding subunit whose sequence and structure is relatively well conserved. Together with the nucleotide binding domains (NBD), functional ABC proteins have two transmembrane domains (TMD) [see Figure 1] that have a more variable sequence and provide a passageway for many kinds of substrates. Structural information of crystals of these biomolecules can be obtained by using x-ray diffraction techniques, and atomistic simulations of such in vivo processes provide complementary information.
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Questions concerning ABC-transporters include: • How are ATP hydrolysis and the conformational changes in the protein related? Do the latter trigger the former by a “Brownian motor”7? • What can we learn about the details of the atomic and electronic structures? • What is the reaction pathway, which energies are involved, and how does the conformation change after hydrolysis? • How does the phosphate group migrate? Does migration result in a conformational change, or is the removal of the whole nucleotide required? What is the role of the ATP-bound cation (usually Mg2+ or Ca2+ )? The ATP-binding cassette (ABC) transporter Sav1866 is a large protein with 1156 residues (578x2, a homodimer, Figure 1). It was discovered from the whole genome analysis of meticillin-resistant Staphylococcus aureus8 , and its structure was first determined ˚ 5 . Apart from differences in the ATP-binding by X-ray crystallography (resolution 3.0 A) sites, no significant conformational changes were observed in complex with adenosine-5’˚ 9. (β,γ-imido)triphosphate (AMP-PNP, resolution 3.4 A) The two-nucleotide binding domains (NBDs) are in close contact in this structure, and a central cavity is formed by the two transmembrane domains (TMDs). This structure is an outward-facing conformation, i.e., the cavity opens wider towards the periplasmic side of the cell membrane than towards the cytoplasmic side. A distinctive feature of this ABC exporter is that both NBDs are in contact with both TMDs, which had not been found in the structures of three known importers10. The helices are strongly bent, so that the transmembrane helices lining up the cavity differ at different levels of the lipid bilayer.
2 Computational Methods and Performance on JuGene We have performed molecular dynamics simulations of Sav1866 in the POPC lipid bilayer membrane, using the AMBER parm99SB force field11 and the TIP3P water model. The parameters of the POPC lipid force field were adopted from a previous simulation12 . The protonation state of the Sav1866 was assigned by the PDB2PQR web server13, with a pH value of 7. Version 2.6 of the highly scalable NAMD (NAnoscale Molecular Dynamics) program14 , implemented and optimized for the Blue Gene/P architecture, was used for the 1 µs simulation. NAMD, a parallel molecular dynamics code for large biomolecular systems, has been developed and maintained at the University of Illinois at Urbana-Champaign (UIUC) and is available as freeware with source code. A recent study15 described several techniques to scale NAMD to 8192 processors on the Blue Gene/L computer, achieving ∼ 1 TFlop on an ATPase benchmark. Other programs have been developed for performing massively parallel simulations on biological systems: The “Blue Matter” MD program, developed by IBM specifically for the Blue Gene architecture16, has similar performance to NAMD in test cases, and scalable algorithms have been implemented in the “Desmond” program17. Extensive tests of all program and execution parameters (including number of task groups) showed that the NAMD program runs well with 4096 processors (one rack) on
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the Blue Gene/P. For our first benchmark we chose P-glycoprotein (365 000 atoms), which was also run under NAMD (version 2.6) on the IBM Blade at the Academia Sinica. Benchmarks for an ATPase system with 327 000 atoms15 had shown that it was possible to perform one time step in 21.8 ms using 2048 Blue Gene/L processors (1 rack), and we obtained somewhat better performance in our tests on the 365 000 atom system on this machine.
3 Simulations on ABC-Transporter Sav1866 The following changes were implemented after our first simulations on Sav1866: • The system size was reduced to ca. 200 000 atoms by removing water molecules that seemed to be extraneous. • The atomic mobility was enhanced by raising the temperature to 340 K (67◦ C).
Figure 2. RMS displacement in early stages of the simulation. Black: all atoms, red: transmembrane domains, TMD).
The root-mean-square displacement of the atoms in the early stages of the simulation are shown in Figure 2. It is quite apparent that the motion of atoms in the nucleotide binding domains is already more pronounced than that of atoms in the TMD. The simulation was continued for 1 µs, making it one of the longest performed to date with an all-atom force field for a system of this size. Figure 3 shows that important structural changes occurred: the NBD and TMD have tilted and separated, causing a scissor-like movement of the whole protein. At the same time, the ICLs are exposed to the solvent. While simulations of 1 µs do not allow us to understand all details of the action of the transporter induced by ATP release, this motion is simply not visible in shorter simulations.
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Figure 3. Upper: starting configuration, lower: structure after 870 ns simulation.
Figure 4 depicts the root-mean-square deviation of the two nucleotide binding domains (NBDs) of Sav1866 from their initial X-ray crystallographic structures. The approximately logarithmic behaviour is a sobering result for those seeking the ultimate products of the transformation, but we note that the time-dependence of conformational change of the NBDs is similar to the oscillations in “logarithmic periodic” systems18 .
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In contrast to the renormalization group theory of critical phenomena, where continuous scale invariance (CSI) is observed, discrete scale invariance (DSI) is considered the physical origin of logarithmic periodicity of complex systems. In disordered systems, DSI may be associated with the spontaneous breaking of replica symmetry, which involves a hierarchical structure of the phase space19 . It is also characterized by a discrete set of complex fractal dimension18. An early example of DSI was the diffusion on a one-dimensional lattice with random asymmetric transition rate20 . This was extended to a three-dimensional lattice by Stauffer and Sornette21 , who concluded that the intermittent random walk, similar to the quasi log-periodic conformational transition of NBD in Figure 4, was punctuated by the encounter with increasingly large clusters of trapping sites.
Figure 4. Root-mean-square deviation of the two nucleotide binding domains (NBDs) of Sav1866 from its initial X-ray crystallographic structure. The time axis is logarithmic.
4 Concluding Remarks and Future Challenges Molecular dynamics simulations have been performed with a classical force field for the ABC-transporter Sav1866. A simulation of 200 000 atoms over 1 µs is large-scale by any standards, and significant atomic motion is clearly evident. Figure 4 shows, however, that longer simulations are necessary to provide a full picture of the reaction. The temperature of the simulation has been increased to 340 K (67◦ C) to facilitate the atomic motion, and it essential to check that this does not lead to artificial results. We shall discuss the results of a simulation on an intermediate structure at 300 K elsewhere. Future work could implement a parallel tempering strategy.
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There are several interesting candidates for future studies. The combination of DF methods for the “active” part of the system with classical force field descriptions of the “inactive” surroundings (“QM/MM methods”) extends the scope of DF calculations. The combination with a history-dependent algorithm (metadynamics) allows complex reactions to be studied, and our previous DF experience provides a good starting point for selecting reaction coordinates and the size of the QM region. Other systems of interest include: • Maltose / maltodextrin ABC transporter (MalK, source: Thermococcus litoralis) is a subunit of the trehalosed/maltose ABC transporter of the archeon Thermococcus litoralis. It contains 372 residues and there are x-ray measurements of resolution ˚ (PDBID: 1g29, 1q12, 1q1b, 1q1e, 2aw0)22 . These structures can be used as 1.9-2.9 A starting points for studying the details of its reactions. • QM/MM studies of ATP hydrolysis in ATPase23, 24 would be a logical extension of our earlier DF work. Another important system involving ATP hydrolysis is phosphoryl transfer and calcium ion occlusion in the calcium pump studied by Nissen and coworkers25. This system is larger than those mentioned above, but significantly smaller than Sav1866. It may be difficult to calculate free energy landscapes accurately, but replica exchange MD should work well for finding the most probable states, and parallel tempering should also be possible.
Acknowledgements We thank S. Kumar (IBM, Yorktown Heights, USA) for advice on optimizing the NAMD program for Blue Gene computers. The calculations were performed on IBM Blue Gene/L, Blue Gene/P, and p690 computers in the FZ J¨ulich with grants from the FZJ and the John von Neumann Institute for Computing (NIC). JHL was supported by the thematic program of Academia Sinica under grant AS-95-TP-A07.
References 1. J. Akola and R. O. Jones, ATP hydrolysis in water – a density functional study, J. Phys. Chem. B 107, 11774-11783, 2003. 2. J. Akola and R. O. Jones, Density functional calculations of ATP systems I: Crystalline ATP hydrates and related molecules, J. Phys. Chem. B 110, 8110-8120, 2006. 3. J. Akola and R. O. Jones, Density functional calculations of ATP systems II: ATP hydrolysis at the active site of actin, J. Phys. Chem. B 110, 8121-8129, 2006. 4. For a recent review of bacterial ABC systems, see A. L. Davidson et al., Structure, Function, and Evolution of Bacterial ATP-Binding Cassette Systems, Microbiol. and Molec. Biol. Rev. 72, 317-364, 2008. 5. R. J. P. Dawson and K. P. Locher, Structure of a bacterial multidrug ABC transporter, Nature 443, 180-185, 2006. 6. G. Lu, J. M. Westbrooks, A. L. Davidson, and J. Chen, ATP hydrolysis is required to reset the ATP-binding cassette dimer into the resting-state conformation, Proc. Natl. Acad. Sci. (USA) 102, 17969-17974, 2005.
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7. See, for example, P. H¨anggi and F. Marchesoni, Artificial Brownian Motors: Controlling Transport on the Nanoscale, Rev. Mod. Phys. 81, 387-442, 2009. 8. M. Kuroda et al., Whole genome sequencing of meticillin-resistant Staphylococcus aureus, Lancet 357, 1225-1240, 2001. 9. R. J. P. Dawson and K. P. Locher, Structure of the multidrug ABC transporter Sav1866 from Staphylococcus aureus in complex with AMP-PNP, FEBS Letters 581, 935-938, 2007. 10. J. K. Zolnerciks, C. Wooding, and K. J. Linton, Evidence for a Sav1866-like architecture for the human multidrug transporter P-glycoprotein, FASEB J. 21, 3937-3948, 2007. 11. C. Simmerling, B. Strockbine, and A. E. Roitberg, All-atom structure prediction and folding simulations of a stable protein, J. Amer. Chem. Soc. 124, 11258-11259, 2002. 12. J. H. Lin, N. A. Baker, J. A. McCammon, Bridging implicit and explicit solvent approaches for membrane electrostatics, Biophys. J. 83, 1374-1379, 2002. 13. T. Dolinsky et al., PDB2PQR: an automated pipeline for the setup of PoissonBoltzmann electrostatics calculations, Nucleic Acids Res. 32, W665-W667, 2004. 14. J. C. Phillips et al., Scalable molecular dynamics with NAMD, J. Comput. Chem. 26, 1781-1802, 2005. See also http://www.ks.uiuc.edu/Research/namd. 15. S. Kumar, C. Huang, G. Almasi, L. V. Kal´e, Achieving Strong Scaling with NAMD on Blue Gene/L, IBM document, 2006. See http://iffwww.iff.kfa-juelich.de/˜jones/NAMDIPDPS06.pdf. 16. B. G. Fitch et al., Blue Matter: Strong scaling of molecular dynamics on Blue Gene/L, ICCS 2006 , 846-854, 2006. http://iffwww.iff.kfa-juelich.de/˜jones/Fetal06.pdf 17. K. J. Bowers et al. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters, Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 11–17 November 2006. 18. D. Sornette, Discrete-scale invariance and complex dimensions, Phys. Rep. 297, 238-270, 1998. 19. D. Sornette et al., Complex fractal dimensions describe the hierarchical structure of diffusion-limited aggregate clusters, Phys. Rev. Lett. 76, 251-254, 1996. 20. J. Bernasconi and W. R. Schneider, Diffusion on a One-dimensional Lattice with Random Asymmetric Transition Rates, J. Phys. A: Math. Gen. 15, L729-L734, 1982. 21. D. Stauffer and D. Sornette, Log-periodic oscillations for biased diffusion on random lattice, Physica A 252, 271-277, 1998. 22. See, for example, K. Diederichs et al., Crystal Structure of MalK, the ATP subunit of the trehalose/maltose ABC transporter of the archeon Thermococcus literalis, EMBO J. 19, 5951-5961, 2000. 23. P. D. Boyer, A Research Journey with ATP synthase, J. Biol. Chem. 277, 39045-39061, 2002. 24. J. Zimmer, Y. Nam, and T. Rapoport, Structure of a complex of the ATPase SecA and the protein-translocation channel, Nature 455, 936-943, 2008. 25. T. L. Sørensen, J. V. Møller, and P. Nissen, Phosphoryl Transfer and Calcium Ion Occlusion in the Calcium Pump, Science 304, 1672-1675, 2004.
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