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Membrane proteins: molecular dynamics simulations
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Membrane proteins: molecular dynamics simulations Erik Lindahl1 and Mark SP Sansom2 Molecular dynamics simulations of membrane proteins are making rapid progress, because of new high-resolution structures, advances in computer hardware and atomistic simulation algorithms, and the recent introduction of coarsegrained models for membranes and proteins. In addition to several large ion channel simulations, recent studies have explored how individual amino acids interact with the bilayer or snorkel/anchor to the headgroup region, and it has been possible to calculate water/membrane partition free energies. This has resulted in a view of bilayers as being adaptive rather than purely hydrophobic solvents, with important implications, for example, for interaction between lipids and arginines in the charged S4 helix of voltage-gated ion channels. However, several studies indicate that the typical current simulations fall short of exhaustive sampling, and that even simple protein– membrane interactions require at least ca. 1 ms to fully sample their dynamics. One new way this is being addressed is coarse-grained models that enable mesoscopic simulations on multi-ms scale. These have been used to model interactions, self-assembly and membrane perturbations induced by proteins. While they cannot replace all-atom simulations, they are a potentially useful technique for initial insertion, placement, and low-resolution refinement. Addresses 1 Center for Biomembrane Research & Stockholm Bioinformatics Center, Department of Biochemistry & Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden 2 Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK Corresponding author: Lindahl, Erik ([email protected]) and Sansom, Mark SP ([email protected])
Current Opinion in Structural Biology 2008, 18:425–431 This review comes from a themed issue on Membranes Edited by Gunnar von Heijne and Douglas Rees Available online 10th April 2008 0959-440X/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. DOI 10.1016/j.sbi.2008.02.003
Introduction Membrane proteins account for 25% of proteins in eukaryotic genomes, and are responsible for interactions of cells with their surrounding environment. They also constitute 50% of current drug targets. Despite significant efforts, there are still only 100 distinct high-resolution membrane protein structures, of which just over half consist of bundles of hydrophobic transmembrane www.sciencedirect.com
(TM) a-helices. As the lipid bilayer environment is a complex two-dimensional liquid crystalline system it has proved difficult to map details of protein-membrane interactions using experimental techniques. This makes them good targets for computer simulations. However, because of their size and the simulation timescales involved it is only recently that simulations have enabled prediction of biological properties. Atomistic-detail (AT) approaches still generally fall short of timescales, for example, helix aggregation/folding, but recent simulations have shed light on functionally relevant local motions, and on interactions with the lipid bilayer. Furthermore, new developments in coarse-grained (CG) models potentially can probe multi-microsecond dynamics of extremely large systems. Since there exist fairly up-to-date surveys of simulations of, for example, channels and transporter proteins [1,2], we are focusing this review on protein–membrane interactions and advances in CG simulations techniques.
Advances in atomistic simulations Traditional molecular simulations retain virtually all atomic-level interactions and use time-steps in the femtosecond range. While, this makes them quite slow and expensive, the carefully tested and transferable parameters of these models make them reliable when it comes to quantitative prediction of properties such as motional timescales or interaction strengths. Several recent studies have reported on large-scale atomistic simulations of complex membrane proteins. One such system of considerable biophysical interest, which has formed a focus of simulation studies, is the voltage sensor domain (VSD) of potassium (Kv) channels. Atomic-detail simulations have been performed both of intact Kv ion channel tetramers [3,4] as well as isolated voltage sensors [5,6]. They reveal how a membrane can accommodate the significantly charged S4 helix (four arginines), through a combination of water penetration and hydrogen bonds to other parts of the protein as well as lipid carbonyl/phosphate groups. Other simulations on the basis of the crystal structure of the SecYEb protein-conducting translocon channel have revealed opening/closing motion of the ‘plug’ domain, and confirm the existence of a tight seal inside the channel that blocks water and ions in the closed state, and ions when open [7]. Several groups have also reported on observations of ion gating mechanisms in simulations of the MscS and MscL mechanosensitive channels, in particular how these proteins change conformation in response to different surface tensions in the membrane [8,9]. Current Opinion in Structural Biology 2008, 18:425–431
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Large sets of simulations combined with non-equilibrium or steered simulation methods have been used to calculate free energies and other experimental observables on the basis of the statistics from several independent runs rather than simply extending individual simulations. Potential of mean force (PMF) free energies have been calculated for the permeation of potassium ions in, for example, model gramicidin channels in reasonable agreement with experimental conductance [10]. PMFs have also been used to predict ion conductance barriers for the nicotinic acetylcholine receptor (nAChR) and related channels [11,12].
Figure 1
Simulations are also starting to reproduce spontaneous motions related to function, for instance the concerted global twisting motion and helix tilting proposed for nAChR activation [13], or initial conformational changes related to the transport mechanisms of the lactose permease (LacY) protein [14].
Coarse-grained membrane protein simulations Arguably, a new key methodological advance is the introduction of CG models for lipids and proteins [15– 17]. In these models, a single CG particle represents 2–5 heavy atoms, and new ‘artificial’ bonded and non-bonded interactions are parameterized to reproduce thermodynamic properties such as oil–water partition coefficients of building block molecules. Not only does this lead to an order-of-magnitude fewer interactions, but the removal of the fastest degrees of freedom additionally makes it possible to take much longer timesteps (typically 40 fs), which together with the reduced interaction density provides 2–3 orders of magnitude speedup compared to atomistic simulations [18]. While it is still under debate how quantitative the resulting predictions are, it is making entirely new spatial and temporal scales accessible to simulations. In addition to studies of protein–bilayer interactions (below), CG simulations have been used to study microsecond dynamics of forcing the translocon lateral gate to open to the lipid bilayer, without lipids flooding the interior of the channel [19]. For the MscL mechanosensitive channel, CG-MD likewise reveals a significant conformational change is seen which corresponds to ‘gating’ of the channel on a microsecond time scale [20]. When applied to the Kv1.2 voltage-gated ion channel (Figure 1) CG simulations suggest a possible gating mechanism involving S4-S5 linker displacement [21].
Protein–bilayer interactions Simulations have been efficient at probing localization of a membrane protein in a bilayer, and its interaction with the surrounding lipids. For example, simulation studies on several simple b-barrel outer membrane proteins (e.g. OmpA, OpcA) from bacteria have explored dynamics and Current Opinion in Structural Biology 2008, 18:425–431
A coarse-grained simulation of the Kv1.2 ion channel (blue/red/yellow) embedded in a DOPC lipid bilayer (grey) and solvated by coarsegrained water particles (not shown) [21]. The CG approach makes it possible to follow structural rearrangements over hundreds of nanoseconds. Illustration courtesy of Dr Mounir Tarek.
interactions within a crystal environment [22], and how the conformational dynamics of a protein may change when transplanted from a crystal to a bilayer environment [23,24]. Several simulation studies of amino acid side chains and their interactions with a lipid bilayer are relevant in the context of bioinformatics and experimental studies of residue distributions within membranes [25,26]. Recent simulation studies have explored solvation of amino acid side chains as a function of position along the bilayer normal [27] and also partition free energies [28,29]. Detailed studies of particular residues include those of tryptophan, which is thought to lock membrane proteins into place relative to the bilayer [30], and of arginine, which plays a key role in the voltage-sensing domain of channels (see below) [31,32]. An unexpected feature that has emerged is the degree of local water penetration into a bilayer when a charged side chain is buried away from the bilayer surface (Figure 2). With increasing duration, MD simulations are able to explore specific interactions of lipids with membrane proteins. Thus, the potassium channel KcsA has been shown to form selective interactions with anionic lipids [33], correlating with functional and structural data. Proteins may also form specific interactions with lipid tails, as seen for rhodopsin and polyunsaturated fatty acids [34]. This study was on the basis of an extensive www.sciencedirect.com
Molecular dynamics simulations of membrane proteins Lindahl and Sansom 427
Figure 2
Simulations of the Hessa et al. [26] model helix with substitutions five residues from the center [27]. Left: lysine snorkels significantly, H-bonding to carbonyls/water. Middle: aspartic acid residues on the same side induce bending distortion including headgroups/water inside the bilayer. Right: tyrosine orients to intercalate with the lipid chains. Illustration courtesy of Anna C.V. Johansson.
(26 ns 100 ns) set of simulations. However, statistical analysis emphasized the need for very long–large-scale atomistic simulations to yield convergence of lipid and protein dynamics [35]. Thus, there is interest in exploiting the longer timescales possible in CG simulations to provide sampling sufficient for more quantitative exploration of protein–lipid interactions. In addition to specific interactions between lipids and membrane proteins, several studies have used CG approaches to explore local deformations of bilayers by membrane proteins. Thus, simplified models of membrane proteins have been used in CG-MD [36] simulations to explore how bilayer–protein mismatch may be accommodated via protein tilting and/or local bilayer deformation. CG-MD has also been used to self-assemble lipid bilayers around membrane proteins, thus providing a measure of the localization and orientation of a protein within a lipid bilayer. Application of this method to a non-redundant set of 100 membrane proteins of known structure (Figure 3) [37] enabled comparative analysis of protein–bilayer interactions (essentially simulation-omics), revealing how local bilayer deformations are related to membrane protein class (e.g. a-helix bundle vs. b-barrel TM domains) and lipid species. CG-MD has also been used to explore large bilayer systems containing multiple (e.g. 16) rhodopsin molecules [38]. These studies indicated that local membrane deformation might influence protein–protein association within the membrane. As discussed above, atomistic simulations have probed lipid–protein interactions of isolated voltage sensor www.sciencedirect.com
domains (VSD) from K+ channels in detergent micelles [39], and lipid bilayers [5,6]. One key feature emerging from these simulations is local deformation of bilayer by the positively charged arginines of the transmembrane S4 helix of the VSD. A similar degree of bilayer deformation is also seen in CG-MD simulations of the VSD in lipid bilayers [40]. Thus, despite some scepticism concerning the utility of CG models [41], comparative analysis suggests the CG simulations at least qualitatively reproduce protein–lipid interactions seen in atomistic studies [42]. Local modulation of the bilayer structure by clusters of positively charged sidechains is not limited to the VSD and related domains. Many membrane active peptides contain significant numbers of positively charged side chains. Extended atomistic simulations of antimicrobial peptides [43] and of the TAT1 peptide from HIV-1 [44] both reveal interactions between positively charged side chains and lipid phosphates leading to rearrangements of the bilayer.
Folding, self-assembly Several studies have addressed insertion and/or folding of single TM helices into lipid bilayers. Atomistic simulations have employed either, for example, replica exchange [45] or extended MD simulations [46]. Various levels of approximation have also been employed to explore helix folding/insertion, including: atomistic protein with an implicit generalized Born (GB) model for the bilayer–water environment [47–49]); atomistic protein model with a knowledge-based membrane potential [50]; and CG-MD self-assembly [17]. All of these approaches reproduce, for example, NMR data on the Current Opinion in Structural Biology 2008, 18:425–431
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Figure 3
Montage of membrane protein (blue) structures inserted into lipid bilayers (lipid headgroups in yellow, red and blue) by coarse-grained selfassembly simulations [37]. Diagram courtesy of Dr Kathryn A. Scott.
localization and tilt [50] of simple model helices relative to a lipid bilayer. The second stage of (re-)folding of a-helical membrane proteins is self-assembly of inserted TM helices. A key test system, the focus of several MD simulation studies, is the TM helix of glycophorin A (GpA). This forms homodimers, stabilized by a key GxxxG sequence motif. Formation of GpA TM helix dimers has been simulated using: the GB membrane model [51]; CG-MD [52]; and a simplified implicit membrane model [53]. In all cases the dimers yielded by simulation are similar to those seen in NMR experiments. Atomistic simulations have been used to estimate the free energy of helix–helix association and the effects of mutations at the interface [54]. Extended AT-MD has been used to refine models of GpA dimers [55], in both lipid bilayer and detergent Current Opinion in Structural Biology 2008, 18:425–431
micelle environments. These results for GpA encourage applications to more complex membrane proteins [51], suggesting that MD simulations may form part of a ‘hybrid’ strategy for membrane protein structure prediction [56].
Proteins and peptides at the bilayer surface Predicting the location and interactions of a peripheral membrane protein at the surface of a bilayer is more complex than for integral membrane proteins, in part because the ‘surface’ of a lipid bilayer is a less welldefined region. Through replica-exchange techniques it has been possible to simulate how some peptides partition spontaneously into the bilayer, and fold to helices either simultaneously with or after insertion [45]. There have www.sciencedirect.com
Molecular dynamics simulations of membrane proteins Lindahl and Sansom 429
also been several simulation studies of 30-residue toxins that bind to membrane surfaces and subsequently inhibit ion channels, providing test systems for more complex proteins. AT and CG simulations of the Tarantula toxin SGTX1 have been compared [57], and found to predict a similar interfacial location and interactions. This provides further support for the validity of the CG approach to membrane/protein interactions. Atomistic simulations of the related Hanatoxin [58] suggested a second possible location, with deeper penetration into the bilayer associated with local membrane thinning. However, these atomistic simulations are relatively brief. One should be cautious as simulations of a simple peptide (WL5) at a bilayer surface [59] suggest a timescale of 500 ns for full sampling of peptide–bilayer interactions. This suggests that for more complex proteins one might use CG simulations to determine the preferred orientation and depth of penetration, followed by atomistic simulations to refine the detailed protein–lipid interactions. A pharmaceutically important class of multi-domain surface-bound proteins are the monotopic enzymes (e.g. cyclo-oxygenase, monoamine oxidase). Simulations may be used to position these proteins relative to a bilayer [60] and to compare interactions between the bilayer–water interface and the protein surface for various monotopic proteins [61]. Implicit bilayer methods have also been used [62], although these cannot reveal the details of protein–lipid interactions. Proteins may also interact with the membrane surface via non-protein anchors. Simulations of lipid-modified Ras protein [63] suggest that in addition to the anchor several direct lipid–protein contacts are formed. Simulations of the C2 domain from cytosolic phospholipase A2 [64] reveal, in addition to interactions mediated via the Ca2+ ions, local remodelling of the bilayer surface to match the protein. Another Ca2+ mediated anchor, that of the GLA domain [65], also seems to mediate protein penetration of the bilayer. Thus, MD simulations provide insights into the interactions of proteins at the membrane surface with the bilayer helping us to understand how proteins interacting with the membrane surface may perturb, for example, the local bilayer curvature, as in simulations of the BAR domain [66]
Concluding remarks Membrane protein simulations are rapidly advancing towards microsecond timescales for complex structures, which is necessary for pre- rather than post-diction studies. While we cannot yet fold large membrane proteins in silico, simulations are successfully used to refine structures of membrane proteins, to calculate interactions in helix dimers, to insert small peptides, and to evaluate free energy costs for amino acid insertion or for interactions inside ion channels. Perhaps most importantly, membrane protein simulations are starting to make new www.sciencedirect.com
and specific quantitative predictions about biological properties not yet reported from experiments.
Acknowledgements Research in MSPS’s group is supported by the Wellcome Trust, the BBSRC, and the EPSRC, and in EL’s group by the Swedish Research Council, Foundation for Strategic Research, and Carl Trygger foundation.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
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10. Allen TW, Andersen OS, Roux B: Ion permeation through a narrow channel: using gramicidin to ascertain all-atom molecular dynamics potential of mean force. Biophys J 2006, 90:3447-3468. 11. Beckstein O, Sansom MSP: A hydrophobic gate in an ion channel: the closed state of the nicotinic acetylcholine receptor. Phys Biol 2006, 3:147-159. 12. Ivanov I, Cheng X, Sine SM, McCammon JA: Barriers to ion translocation in cationic and anionic receptors from the Cys-loop family. J Am Chem Soc 2007, 129:8217-8224. 13. Cheng X, Ivanov I, Wang H, Sine SM, McCammon JA: Nanosecond-timescale conformational dynamics of the human a7 nicotinic acetylcholine receptor. Biophys J 2007, 93:2622-2634. 14. Holyoake J, Sansom MSP: Conformational change in an MFS Protein: MD simulations of LacY. Structure 2007, 15:873-884. 15. Marrink SJ, de Vries AH, Mark AE: Coarse grained model for semiquantitative lipid simulations. J Phys Chem B 2004, 108:750-760. 16. Nielsen SO, Lopez CF, Srinivas G, Klein ML: Coarse grain models and the computer simulation of soft materials. J Phys: Condens Matt 2004, 16:R481-R512. 17. Bond PJ, Holyoake J, Ivetac A, Khalid S, Sansom MSP: Coarsegrained molecular dynamics simulations of membrane proteins and peptides. J Struct Biol 2007, 157:593-605. Current Opinion in Structural Biology 2008, 18:425–431
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18. Marrink SJ, Risselada J, Yefimov S, Tieleman DP, de Vries AH: The MARTINI forcefield: coarse grained model for biomolecular simulations. J Phys Chem B 2007, 111:7812-7824. The latest complete version of the most widely used coarse-grained force field, supporting both lipids and proteins. 19. Gumbart J, Schulten K: Structural determinants of lateral gate opening in the protein translocon. Biochemistry 2007, 46:1114711157. A key example of steered atomistic and coarse-grained MD simulations, forcing the translocon channel to an open state and then letting it relax. 20. Yefimov S, van der Giessen E, Onck PR, Marrink SJ: Mechanosensitive membrane channels in action. Biophys J 2008, 94:2994-3002. 21. Treptow W, Marrink S-J, Tarek M: Gating motions in voltage gated potassium channels revealed by coarse-grained molecular dynamics simulations. J Phys Chem B 2008, 112:3277-3282. Possible gating motions of Kv1.2 observed in coarse-grained simulations. Although one may argue about timescales or quantitative prediction, it reveals the scales that are accessible with coarse-grained models.
35. Grossfield A, Feller SE, Pitman MC: Convergence of molecular dynamics simulations of membrane proteins. Proteins: Struct Funct Bioinf 2007, 67:31-40. 36. Nielsen SO, Ensing B, Ortiz V, Moore PB, Klein ML: Lipid bilayer perturbations around a transmembrane nanotube: a coarse grain molecular dynamics study. Biophys J 2005, 88:3822-3828. 37. Scott KA, Bond PJ, Ivetac A, Chetwynd AP, Khalid S, Sansom MSP: Coarse-grained MD simulations of membrane protein–bilayer self assembly. Structure 2008, 16:621-630. Coarse-grained simulations of all membrane protein folds inserted into a lipid bilayer—towards a simulation based structural bioinformatics of membrane proteins. 38. Periole X, Huber T, Marrink SJ, Sakmar TP: G protein-coupled receptors self-assemble in dynamics simulations of model bilayers. J Am Chem Soc 2007, 129:10126-10132. 39. Sands Z, Grottesi A, Sansom MSP: The intrinsic flexibility of the Kv voltage sensor and its implications for channel gating mechanisms. Biophys J 2005, 90:1598-1606.
22. Bond PJ, Faraldo-Go´mez JD, Deol SS, Sansom MSP: Membrane protein dynamics and detergent interactions within a crystal: a simulation study of OmpA. Proc Natl Acad Sci U S A 2006, 103:9518-9523.
40. Bond PJ, Sansom MSP: Bilayer deformation by the Kv channel voltage sensor domain revealed by self-assembly simulations. Proc Natl Acad Sci U S A 2007, 104:2631-2636. Local bilayer deformation around a complete voltage sensor domain revealed by coarse-grained simulations.
23. Bond PJ, Derrick JP, Sansom MSP: Membrane simulations of OpcA: gating in the loops? Biophys J 2007, 92:L23-L25.
41. Allen TW: Modeling charged protein side chains in lipid membranes. J Gen Physiol 2007, 130:237-240.
24. Luan BQ, Caffrey M, Aksimentiev A: Structure refinement of the OpcA adhesin using molecular dynamics. Biophys J 2007, 93:3058-3069.
42. Sansom MSP, Scott KA, Bond PJ: Coarse grained simulation: a high throughput computational approach to membrane proteins. Biochem Soc Trans 2008, 36:27-32.
25. Nilsson J, Persson B, von Heijne G: Comparative analysis of amino acid distributions in integral membrane proteins from 107 genomes. Proteins: Struct Funct Bioinf 2005, 60:606-616.
43. Leontiadou H, Mark AE, Marrink SJ: Antimicrobial peptides in action. J Am Chem Soc 2006, 128:12156-12161.
26. Hessa T, Kim H, Bihlmaier K, Lundin C, Boekel J, Andersson H, Nilsson I, White SH, von Heijne G: Recognition of transmembrane helices by the endoplasmic reticulum translocon. Nature 2005, 433:377-381. 27. Johansson ACV, Lindahl E: Amino-acid solvation structure in transmembrane helices from molecular dynamics simulations. Biophys J 2006, 91:4450-4463. An analysis of amino acid solvation as a function of sidechain type/ position in a model TM helix, showing that simulations can match systematic experimental surveys. 28. MacCallum JL, Bennett WFD, Tieleman DP: Partitioning of amino acid side chains into lipid bilayers: results from computer simulations and comparison to experiment. J Gen Physiol 2007, 129:371-377. 29. MacCallum JL, Bennett WFD, Tieleman DP: Distribution of amino acids in a lipid bilayer from computer simulations. Biophys J 2008, 94:3393-3404. Potential of mean force profiles for water/membrane partitioning of amino acid analogs. 30. Norman KE, Nymeyer H: Indole localization in lipid membranes revealed by molecular simulation. Biophys J 2006, 91:2046-2054. 31. Dorairaj S, Allen TW: On the thermodynamic stability of a charged arginine side chain in a transmembrane helix. Proc Natl Acad Sci U S A 2007, 104:4943-4948. The first study to show that the cost of transferring arginine residues from water to a bilayer center is much higher than the in vivo cost measured by Hessa et al. [26], suggesting there is something we do not understand about the insertion process. 32. Li L, Vorobyov I, MacKerell AD, Allen TW: Is arginine charged in a membrane? Biophys J 2008, 94:L11-L13. 33. Deol SS, Domene C, Bond PJ, Sansom MSP: Anionic phospholipids interactions with the potassium channel KcsA: simulation studies. Biophys J 2006, 90:822-830. 34. Grossfield A, Feller SE, Pitman MC: A role for direct interactions in the modulation of rhodopsin by omega-3 polyunsaturated lipids. Proc Natl Acad Sci U S A 2006, 103:4888-4893. Current Opinion in Structural Biology 2008, 18:425–431
44. Herce HD, Garcia AE: Molecular dynamics simulations suggest a mechanism for translocation of the HIV-1 TAT peptide across lipid bilayers. Proc Natl Acad Sci U S A 2007, 104:20805-20810. 45. Nymeyer H, Woolf TB, Garcia AE: Folding is not required for bilayer insertion: replica exchange simulations for an a-helical peptide with an explicit lipid bilayer. Proteins: Struct Funct Bioinf 2005, 59:783-790. 46. Esteban-Martin S, Salgado J: Self-assembling of peptide/ membrane complexes by atomistic molecular dynamics simulations. Biophys J 2007, 92:903-912. 47. Im W, Brooks CL: De novo folding of membrane proteins: an exploration of the structure and NMR properties of the fd coat protein. J Mol Biol 2004, 337: 531-519. 48. Im W, Brooks CL: Interfacial folding and membrane insertion of designed peptides studied by molecular dynamics simulations. Proc Nat Acad Sci U S A 2005, 102:6771-6776. 49. Ulmschneider JP, Ulmschneider MB: Folding simulations of the transmembrane helix of Virus Protein U in an implicit membrane model. J Chem Theory Comput 2007, 3:2335-2346. 50. Ulmschneider MB, Sansom MSP, Di Nola A: Evaluating tilt angles of membrane-associated helices: comparison of computational and NMR techniques. Biophys J 2006, 90:1650-1660. 51. Bu L, Im W, Brooks CL: Membrane assembly of simple helix homo-oligomers studied via molecular dynamics simulations. Biophys J 2007, 92:854-863. The use of generalized Born model in membrane MD simulations to predict the structure of TM helix oligomers for simple membrane proteins. 52. Bond PJ, Sansom MSP: Insertion and assembly of membrane proteins via simulation. J Am Chem Soc 2006, 128:2697-2704. 53. Mottamal M, Zhang J, Lazaridis T: Energetics of native and nonnative states of the glycophorin transmembrane helix dimer. Proteins: Struct Funct Bioinf 2006, 62:996-1009. 54. He´nin J, Pohorille A, Chipot C: Insights into the recognition and association of transmembrane a-helices. The free energy of a-helix dimerization in glycophorin A. J Am Chem Soc 2005, 127:8478-8484. www.sciencedirect.com
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55. Cuthbertson JM, Bond PJ, Sansom MSP: Transmembrane helix– helix interactions: comparative simulations of the glycophorin A dimer. Biochemistry 2006, 45:14298-14310.
61. Fowler PW, Balali-Mood K, Deol S, Coveney PV, Sansom MSP: Monotopic enzymes and lipid bilayers: a comparative study. Biochemistry 2007, 46:3108-3115.
56. Fleishman SJ, Unger VM, Ben-Tal N: Transmembrane protein structures without X-rays. Trends Biochem Sci 2006, 31:106-113.
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57. Wee CL, Bemporad D, Sands ZA, Gavaghan D, Sansom MSP: SGTx1, a Kv channel gating-modifier toxin, binds to the interfacial region of lipid bilayers. Biophys J 2007, 92:L07-L09. Coarse-grained and atomistic simulations compared for interaction of a toxin with the lipid–water interface.
63. Gorfe AA, Hanzal-Bayer M, Abankwa D, Hancock JF, McCammon JA: Structure and dynamics of the full-length lipidmodified H-Ras protein in a 1,2-dimyristoylglycero-3phosphocholine bilayer. J Med Chem 2007, 50:674-684.
58. Nishizawa M, Nishizawa K: Interaction between K+ channel gate modifier hanatoxin and lipid bilayer membranes analyzed by molecular dynamics simulation. Eur Biophys J 2006, 35:373-381.
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65. Ohkubo YZ, Tajkhorshid E: Distinct structural and adhesive roles of Ca2+ in membrane binding of blood coagulation factors. Structure 2008, 16:72-81. 66. Blood PD, Voth GA: Direct observation of Bin/amphiphysin/Rvs (BAR) domain-induced membrane curvature by means of molecular dynamics simulations. Proc Natl Acad Sci U S A 2007, 103:15068-15072. A first, and striking, example of proteins directly inducing large-scale conformational changes in a membrane in a simulation.
Current Opinion in Structural Biology 2008, 18:425–431
Membrane proteins: molecular dynamics simulations Erik Lindahl1 and Mark SP Sansom2 Molecular dynamics simulations of membrane proteins are making rapid progress, because of new high-resolution structures, advances in computer hardware and atomistic simulation algorithms, and the recent introduction of coarsegrained models for membranes and proteins. In addition to several large ion channel simulations, recent studies have explored how individual amino acids interact with the bilayer or snorkel/anchor to the headgroup region, and it has been possible to calculate water/membrane partition free energies. This has resulted in a view of bilayers as being adaptive rather than purely hydrophobic solvents, with important implications, for example, for interaction between lipids and arginines in the charged S4 helix of voltage-gated ion channels. However, several studies indicate that the typical current simulations fall short of exhaustive sampling, and that even simple protein– membrane interactions require at least ca. 1 ms to fully sample their dynamics. One new way this is being addressed is coarse-grained models that enable mesoscopic simulations on multi-ms scale. These have been used to model interactions, self-assembly and membrane perturbations induced by proteins. While they cannot replace all-atom simulations, they are a potentially useful technique for initial insertion, placement, and low-resolution refinement. Addresses 1 Center for Biomembrane Research & Stockholm Bioinformatics Center, Department of Biochemistry & Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden 2 Department of Biochemistry, University of Oxford, Oxford OX1 3QU, UK Corresponding author: Lindahl, Erik ([email protected]) and Sansom, Mark SP ([email protected])
Current Opinion in Structural Biology 2008, 18:425–431 This review comes from a themed issue on Membranes Edited by Gunnar von Heijne and Douglas Rees Available online 10th April 2008 0959-440X/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. DOI 10.1016/j.sbi.2008.02.003
Introduction Membrane proteins account for 25% of proteins in eukaryotic genomes, and are responsible for interactions of cells with their surrounding environment. They also constitute 50% of current drug targets. Despite significant efforts, there are still only 100 distinct high-resolution membrane protein structures, of which just over half consist of bundles of hydrophobic transmembrane www.sciencedirect.com
(TM) a-helices. As the lipid bilayer environment is a complex two-dimensional liquid crystalline system it has proved difficult to map details of protein-membrane interactions using experimental techniques. This makes them good targets for computer simulations. However, because of their size and the simulation timescales involved it is only recently that simulations have enabled prediction of biological properties. Atomistic-detail (AT) approaches still generally fall short of timescales, for example, helix aggregation/folding, but recent simulations have shed light on functionally relevant local motions, and on interactions with the lipid bilayer. Furthermore, new developments in coarse-grained (CG) models potentially can probe multi-microsecond dynamics of extremely large systems. Since there exist fairly up-to-date surveys of simulations of, for example, channels and transporter proteins [1,2], we are focusing this review on protein–membrane interactions and advances in CG simulations techniques.
Advances in atomistic simulations Traditional molecular simulations retain virtually all atomic-level interactions and use time-steps in the femtosecond range. While, this makes them quite slow and expensive, the carefully tested and transferable parameters of these models make them reliable when it comes to quantitative prediction of properties such as motional timescales or interaction strengths. Several recent studies have reported on large-scale atomistic simulations of complex membrane proteins. One such system of considerable biophysical interest, which has formed a focus of simulation studies, is the voltage sensor domain (VSD) of potassium (Kv) channels. Atomic-detail simulations have been performed both of intact Kv ion channel tetramers [3,4] as well as isolated voltage sensors [5,6]. They reveal how a membrane can accommodate the significantly charged S4 helix (four arginines), through a combination of water penetration and hydrogen bonds to other parts of the protein as well as lipid carbonyl/phosphate groups. Other simulations on the basis of the crystal structure of the SecYEb protein-conducting translocon channel have revealed opening/closing motion of the ‘plug’ domain, and confirm the existence of a tight seal inside the channel that blocks water and ions in the closed state, and ions when open [7]. Several groups have also reported on observations of ion gating mechanisms in simulations of the MscS and MscL mechanosensitive channels, in particular how these proteins change conformation in response to different surface tensions in the membrane [8,9]. Current Opinion in Structural Biology 2008, 18:425–431
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Large sets of simulations combined with non-equilibrium or steered simulation methods have been used to calculate free energies and other experimental observables on the basis of the statistics from several independent runs rather than simply extending individual simulations. Potential of mean force (PMF) free energies have been calculated for the permeation of potassium ions in, for example, model gramicidin channels in reasonable agreement with experimental conductance [10]. PMFs have also been used to predict ion conductance barriers for the nicotinic acetylcholine receptor (nAChR) and related channels [11,12].
Figure 1
Simulations are also starting to reproduce spontaneous motions related to function, for instance the concerted global twisting motion and helix tilting proposed for nAChR activation [13], or initial conformational changes related to the transport mechanisms of the lactose permease (LacY) protein [14].
Coarse-grained membrane protein simulations Arguably, a new key methodological advance is the introduction of CG models for lipids and proteins [15– 17]. In these models, a single CG particle represents 2–5 heavy atoms, and new ‘artificial’ bonded and non-bonded interactions are parameterized to reproduce thermodynamic properties such as oil–water partition coefficients of building block molecules. Not only does this lead to an order-of-magnitude fewer interactions, but the removal of the fastest degrees of freedom additionally makes it possible to take much longer timesteps (typically 40 fs), which together with the reduced interaction density provides 2–3 orders of magnitude speedup compared to atomistic simulations [18]. While it is still under debate how quantitative the resulting predictions are, it is making entirely new spatial and temporal scales accessible to simulations. In addition to studies of protein–bilayer interactions (below), CG simulations have been used to study microsecond dynamics of forcing the translocon lateral gate to open to the lipid bilayer, without lipids flooding the interior of the channel [19]. For the MscL mechanosensitive channel, CG-MD likewise reveals a significant conformational change is seen which corresponds to ‘gating’ of the channel on a microsecond time scale [20]. When applied to the Kv1.2 voltage-gated ion channel (Figure 1) CG simulations suggest a possible gating mechanism involving S4-S5 linker displacement [21].
Protein–bilayer interactions Simulations have been efficient at probing localization of a membrane protein in a bilayer, and its interaction with the surrounding lipids. For example, simulation studies on several simple b-barrel outer membrane proteins (e.g. OmpA, OpcA) from bacteria have explored dynamics and Current Opinion in Structural Biology 2008, 18:425–431
A coarse-grained simulation of the Kv1.2 ion channel (blue/red/yellow) embedded in a DOPC lipid bilayer (grey) and solvated by coarsegrained water particles (not shown) [21]. The CG approach makes it possible to follow structural rearrangements over hundreds of nanoseconds. Illustration courtesy of Dr Mounir Tarek.
interactions within a crystal environment [22], and how the conformational dynamics of a protein may change when transplanted from a crystal to a bilayer environment [23,24]. Several simulation studies of amino acid side chains and their interactions with a lipid bilayer are relevant in the context of bioinformatics and experimental studies of residue distributions within membranes [25,26]. Recent simulation studies have explored solvation of amino acid side chains as a function of position along the bilayer normal [27] and also partition free energies [28,29]. Detailed studies of particular residues include those of tryptophan, which is thought to lock membrane proteins into place relative to the bilayer [30], and of arginine, which plays a key role in the voltage-sensing domain of channels (see below) [31,32]. An unexpected feature that has emerged is the degree of local water penetration into a bilayer when a charged side chain is buried away from the bilayer surface (Figure 2). With increasing duration, MD simulations are able to explore specific interactions of lipids with membrane proteins. Thus, the potassium channel KcsA has been shown to form selective interactions with anionic lipids [33], correlating with functional and structural data. Proteins may also form specific interactions with lipid tails, as seen for rhodopsin and polyunsaturated fatty acids [34]. This study was on the basis of an extensive www.sciencedirect.com
Molecular dynamics simulations of membrane proteins Lindahl and Sansom 427
Figure 2
Simulations of the Hessa et al. [26] model helix with substitutions five residues from the center [27]. Left: lysine snorkels significantly, H-bonding to carbonyls/water. Middle: aspartic acid residues on the same side induce bending distortion including headgroups/water inside the bilayer. Right: tyrosine orients to intercalate with the lipid chains. Illustration courtesy of Anna C.V. Johansson.
(26 ns 100 ns) set of simulations. However, statistical analysis emphasized the need for very long–large-scale atomistic simulations to yield convergence of lipid and protein dynamics [35]. Thus, there is interest in exploiting the longer timescales possible in CG simulations to provide sampling sufficient for more quantitative exploration of protein–lipid interactions. In addition to specific interactions between lipids and membrane proteins, several studies have used CG approaches to explore local deformations of bilayers by membrane proteins. Thus, simplified models of membrane proteins have been used in CG-MD [36] simulations to explore how bilayer–protein mismatch may be accommodated via protein tilting and/or local bilayer deformation. CG-MD has also been used to self-assemble lipid bilayers around membrane proteins, thus providing a measure of the localization and orientation of a protein within a lipid bilayer. Application of this method to a non-redundant set of 100 membrane proteins of known structure (Figure 3) [37] enabled comparative analysis of protein–bilayer interactions (essentially simulation-omics), revealing how local bilayer deformations are related to membrane protein class (e.g. a-helix bundle vs. b-barrel TM domains) and lipid species. CG-MD has also been used to explore large bilayer systems containing multiple (e.g. 16) rhodopsin molecules [38]. These studies indicated that local membrane deformation might influence protein–protein association within the membrane. As discussed above, atomistic simulations have probed lipid–protein interactions of isolated voltage sensor www.sciencedirect.com
domains (VSD) from K+ channels in detergent micelles [39], and lipid bilayers [5,6]. One key feature emerging from these simulations is local deformation of bilayer by the positively charged arginines of the transmembrane S4 helix of the VSD. A similar degree of bilayer deformation is also seen in CG-MD simulations of the VSD in lipid bilayers [40]. Thus, despite some scepticism concerning the utility of CG models [41], comparative analysis suggests the CG simulations at least qualitatively reproduce protein–lipid interactions seen in atomistic studies [42]. Local modulation of the bilayer structure by clusters of positively charged sidechains is not limited to the VSD and related domains. Many membrane active peptides contain significant numbers of positively charged side chains. Extended atomistic simulations of antimicrobial peptides [43] and of the TAT1 peptide from HIV-1 [44] both reveal interactions between positively charged side chains and lipid phosphates leading to rearrangements of the bilayer.
Folding, self-assembly Several studies have addressed insertion and/or folding of single TM helices into lipid bilayers. Atomistic simulations have employed either, for example, replica exchange [45] or extended MD simulations [46]. Various levels of approximation have also been employed to explore helix folding/insertion, including: atomistic protein with an implicit generalized Born (GB) model for the bilayer–water environment [47–49]); atomistic protein model with a knowledge-based membrane potential [50]; and CG-MD self-assembly [17]. All of these approaches reproduce, for example, NMR data on the Current Opinion in Structural Biology 2008, 18:425–431
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Figure 3
Montage of membrane protein (blue) structures inserted into lipid bilayers (lipid headgroups in yellow, red and blue) by coarse-grained selfassembly simulations [37]. Diagram courtesy of Dr Kathryn A. Scott.
localization and tilt [50] of simple model helices relative to a lipid bilayer. The second stage of (re-)folding of a-helical membrane proteins is self-assembly of inserted TM helices. A key test system, the focus of several MD simulation studies, is the TM helix of glycophorin A (GpA). This forms homodimers, stabilized by a key GxxxG sequence motif. Formation of GpA TM helix dimers has been simulated using: the GB membrane model [51]; CG-MD [52]; and a simplified implicit membrane model [53]. In all cases the dimers yielded by simulation are similar to those seen in NMR experiments. Atomistic simulations have been used to estimate the free energy of helix–helix association and the effects of mutations at the interface [54]. Extended AT-MD has been used to refine models of GpA dimers [55], in both lipid bilayer and detergent Current Opinion in Structural Biology 2008, 18:425–431
micelle environments. These results for GpA encourage applications to more complex membrane proteins [51], suggesting that MD simulations may form part of a ‘hybrid’ strategy for membrane protein structure prediction [56].
Proteins and peptides at the bilayer surface Predicting the location and interactions of a peripheral membrane protein at the surface of a bilayer is more complex than for integral membrane proteins, in part because the ‘surface’ of a lipid bilayer is a less welldefined region. Through replica-exchange techniques it has been possible to simulate how some peptides partition spontaneously into the bilayer, and fold to helices either simultaneously with or after insertion [45]. There have www.sciencedirect.com
Molecular dynamics simulations of membrane proteins Lindahl and Sansom 429
also been several simulation studies of 30-residue toxins that bind to membrane surfaces and subsequently inhibit ion channels, providing test systems for more complex proteins. AT and CG simulations of the Tarantula toxin SGTX1 have been compared [57], and found to predict a similar interfacial location and interactions. This provides further support for the validity of the CG approach to membrane/protein interactions. Atomistic simulations of the related Hanatoxin [58] suggested a second possible location, with deeper penetration into the bilayer associated with local membrane thinning. However, these atomistic simulations are relatively brief. One should be cautious as simulations of a simple peptide (WL5) at a bilayer surface [59] suggest a timescale of 500 ns for full sampling of peptide–bilayer interactions. This suggests that for more complex proteins one might use CG simulations to determine the preferred orientation and depth of penetration, followed by atomistic simulations to refine the detailed protein–lipid interactions. A pharmaceutically important class of multi-domain surface-bound proteins are the monotopic enzymes (e.g. cyclo-oxygenase, monoamine oxidase). Simulations may be used to position these proteins relative to a bilayer [60] and to compare interactions between the bilayer–water interface and the protein surface for various monotopic proteins [61]. Implicit bilayer methods have also been used [62], although these cannot reveal the details of protein–lipid interactions. Proteins may also interact with the membrane surface via non-protein anchors. Simulations of lipid-modified Ras protein [63] suggest that in addition to the anchor several direct lipid–protein contacts are formed. Simulations of the C2 domain from cytosolic phospholipase A2 [64] reveal, in addition to interactions mediated via the Ca2+ ions, local remodelling of the bilayer surface to match the protein. Another Ca2+ mediated anchor, that of the GLA domain [65], also seems to mediate protein penetration of the bilayer. Thus, MD simulations provide insights into the interactions of proteins at the membrane surface with the bilayer helping us to understand how proteins interacting with the membrane surface may perturb, for example, the local bilayer curvature, as in simulations of the BAR domain [66]
Concluding remarks Membrane protein simulations are rapidly advancing towards microsecond timescales for complex structures, which is necessary for pre- rather than post-diction studies. While we cannot yet fold large membrane proteins in silico, simulations are successfully used to refine structures of membrane proteins, to calculate interactions in helix dimers, to insert small peptides, and to evaluate free energy costs for amino acid insertion or for interactions inside ion channels. Perhaps most importantly, membrane protein simulations are starting to make new www.sciencedirect.com
and specific quantitative predictions about biological properties not yet reported from experiments.
Acknowledgements Research in MSPS’s group is supported by the Wellcome Trust, the BBSRC, and the EPSRC, and in EL’s group by the Swedish Research Council, Foundation for Strategic Research, and Carl Trygger foundation.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
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57. Wee CL, Bemporad D, Sands ZA, Gavaghan D, Sansom MSP: SGTx1, a Kv channel gating-modifier toxin, binds to the interfacial region of lipid bilayers. Biophys J 2007, 92:L07-L09. Coarse-grained and atomistic simulations compared for interaction of a toxin with the lipid–water interface.
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