Molecular Dynamics Simulations of Membrane Proteins

Current Physical Chemistry, 2012, 2, 363-378

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Molecular Dynamics Simulations of Membrane Proteins: Building Starting Structures and Example Applications Thomas H. Schmidt1, Megan L. O’Mara2 and Christian Kandt1,* 1

Computational Structural Biology, Department of Life Science Informatics B-IT, Life & Medical Sciences Center, University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany; 2School of Chemistry & Molecular Biosciences, Building 76 Coopers Rd, University of Queensland, St Lucia Campus, Brisbane Qld, Australia 4072 Abstract: Located at the interface between the cell and organelle interior and exterior, membrane proteins are key players in a number of fundamental biological processes. In recent years, molecular dynamics simulations have become an increasingly important tool in the study of membrane proteins. Increases in computer power and the ongoing development in atomistic and coarse-grained MD techniques now permit simulations of membrane protein systems on a size and time scale that would have been impossible only a few years ago. At the beginning of each membrane protein simulation stands the generation of a suitable starting structure which can be done by either constructing the bilayer around the protein or by inserting the protein into a pre-equilibrated membrane patch. Here we review the current state of the art of the available techniques and carry out application benchmarks using five example membrane proteins of different size and transmembrane structure. We conclude this paper reviewing recent examples of molecular dynamics studies representing three major classes of membrane proteins: G protein-coupled receptors, channels and transporters.

Keywords: Membrane protein, Molecular dynamics simulation, Protein insertion, Lipid bilayer, Hydrophobic belt. 1. INTRODUCTION 1.1. Membrane Proteins Embedded in or associated with cell or organelle membranes, membrane proteins are key players in a number of fundamental biological processes including energy conversion, transport, signal recognition and transduction. Although they account for 20 to 30% of the encoded proteins in the genome of all organisms [1] and are estimated to be relevant for 60% of all drug targets [2], currently only 1% of the protein structures deposited in the RCSB Protein Data Bank [3-5] are membrane proteins. Though impressive progress has been made since the first X-ray structure of a membrane protein was published 26 years ago [6], leading to an exponential increase of solved membrane protein crystal structures in recent years [7, 8], gaining deeper insight into the architecture and function of membrane proteins remains a major goal in modern structural biology. Over the years, computational methods like homology modeling, quantumand molecular mechanical simulations have become a valuable tool in (membrane) protein research, playing an increasingly important role in complementing experimental data and sparking new investigations [9-15]. 1.2. Molecular Dynamics Simulations Molecular dynamics (MD) simulation is a molecular mechanics technique that numerically investigates the

*Address correspondence to this author at Computational Structural Biology, Department of Life Science Informatics B-IT, Life & Medical Sciences Center, University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany; Tel: #49 228 2699 324; Fax: #49 228 2699 341; E-mail: [email protected] 1877-9476/12 $58.00+.00

motion of a system of particles under the influence of internal – interactions between atoms – and external forces – temperature, pressure and optional additional forces in steered or targeted MD [16]. At the core of MD simulation is the empirical potential energy function that relates structure to energy and describes the forces between atoms using harmonic and periodic potentials to model covalent bondmediated interactions, as well as Coulomb and Lennard Jones-like potentials to represent electrostatic and van der Waals interactions [17-20]. Commonly referred to as “force field” in conjunction with its empirical parameters, the potential energy function allows the forces acting on each atom in a system to be computed and employs Newton’s second law of motion to predict how each atom will move during a time step of a few femtoseconds. Repeating the time step millions of times, a trajectory of all atoms in the system over time is generated that permits studying the dynamics of the (membrane) protein of interest and its microenvironment at a level of detail not accessible by experiments. Since the first MD simulations were performed at the end of the 1950s [21, 22] the first simulations of liquid water [23], a watersoluble protein [24], and the first simulations of fatty acids [25, 26], a lipid bilayer [27] and the first bilayer-embedded membrane protein 16 years ago [28], MD now covers a range of system sizes up to 105 to 106 atoms simulated on a nanoseconds to millisecond time scale [29-31]. Several force fields have been developed for biomolecular simulations [17, 32], among which AMBER [33, 34], CHARMM [35-37], GROMOS [38] and OPLS [39] are the most common and wide-spread ones. These force fields currently represent two different abstraction levels in modeling the simulation system. Whereas all-atom force fields like AMBER, CHARMM and OPLS consider every atom in the system, united-atom force fields like GROMOS © 2012 Bentham Science Publishers

364 Current Physical Chemistry, 2012, Vol. 2, No. 4

employ a reduced representation where only the polar hydrogen atoms are represented explicitly while non-polar hydrogens are subsumed into the atom they are bound to [16]. That way a methyl, methylene or methine group is then modeled as a single “united atom” with adjusted mass and van der Waals characteristics. Initially developed in the 1970s [40], coarse-grained (CG) simulations represent a further abstraction level of atom representation where groups of heavy atoms are clustered into CG interaction sites [41]. Due to the reduced number of particles, CG simulations substantially increase the accessible scales of system size and simulation time at the cost of lower molecular resolution. CG force fields are semi-quantitative in nature and the parameterization of the force field reflects the physical property of the system that is to be examined. An example of one of the numerous biomolecular CG models published in recent years is the MARTINI force field [42-46]. 1.3. MD Simulations of Membrane Proteins Molecular dynamics simulations of membrane proteins pose special challenges. In particular, this regards the representation of the protein’s environment and the generation of starting structures for simulations. The membrane protein environment is characterized by two different chemical milieus that have to be modelled correctly by the force field. This is done using implicit [47, 48] or, more commonly and the focus of this review, explicit representations of the lipid and water phases. Given that biomolecular force fields have been classically parameterized for aqueous solutions [49], an essential amount of work was and is necessary to update or re-parameterize existing force fields and develop lipid models (molecular topologies) that reproduce the known experimental data [5054] sufficiently well. In addition, due to the paucity of experimental high resolution data on lipid or membrane structure, 1D density profiles and fluidity measurements [55] remain the main reference data for parameterization making the development of new lipid topologies a demanding task. Nevertheless this area is also a vivid field of research where substantial advances have been made over the recent years. Currently there are two types of lipid force fields available for atomistic MD: all atom as in [56-58] or united atom as in

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[59-64]. Whereas in most cases the same abstraction level is used for lipid and protein, a combination of united atom lipid with all atom protein was introduced by Tieleman and coworkers [63]. With the increasing number of topologies available for different lipid species, simulation studies have started taking the heterogeneity of biological membranes using lipid mixtures into account, rather than using uniform lipid compositions [65-67]. Furthermore, the first molecular topologies have been developed that allow the modelling of membrane components like the eubacterial peptidoglycan layer [68-70] or the lipopolysaccharide (LPS) layer of the outer membrane in Gram-negative bacteria [71-74]. At the beginning of each MD simulation stands the building of a suitable starting structure, which, next to the actual protein structure, includes a suitable model of the protein’s micro-environment. For water soluble proteins, generation of the initial system is completed by solvating the system with a water / ion solution. Membrane protein simulations require the additional working step of accommodating the protein in the bilayer. To achieve this, two general strategies are employed today: either the bilayer is constructed around the protein, or the protein is inserted into a pre-equilibrated bilayer. Depending on the protein’s shape (Fig. 1a), its cavity structure in the trans-membrane section (Fig. 1b), the number of membranes spanned by the protein (Fig. 1c, left) and the membrane curvature – plane bilayer versus vesicles or micelles – (Fig. 1c, right), individual challenges arise that have to be taken into account by both accommodation strategies. In this paper we review the current state of common techniques available for both preparation approaches and carry out application benchmarks of different membrane accommodation techniques using five example membrane proteins of different size and transmembrane structure. Application benchmarks were restricted to methods that are either MD package-independent or compatible with the GroMACS suite, and do not require the installation of simulation software for which license fees are charged. We conclude this paper reviewing selected examples of recent simulation studies of membrane proteins in an explicit bilayer representing three main classes of membrane proteins: channels, transporters and G-protein-coupled receptors (GPCRs).

Fig. (1). Types of trans-membrane protein topology. Depending on the shape of the protein trans-membrane section (A), its cavity structure (B), the amount of membranes / leaflets spanned by the protein and the curvature of the membrane (C), individual challenges arise in building simulation starting structures.

Membrane Protein Simulations

Current Physical Chemistry, 2012, Vol. 2, No. 4

2. BUILDING SIMULATION STARTING STRUCTURES Creating a starting structure for membrane protein simulations requires essentially three major working steps. (I) Suitable 3D structures of the protein and lipid(s) (or the membrane) of interest have to be obtained ensuring that lipid topologies that are compatible with the MD engine Table 1.

of choice are at hand. Like the experimentally determined protein structures that can be downloaded directly from the Protein Data Bank, or other online sources focused on membrane proteins (Table 1), a number of research groups now provide freely accessible online repositories of lipid structures and molecular topologies (Table 2). (II) The membrane-exposed regions of the protein must be

Sources for Membrane Protein Structures (Apart from the RCSB PDB 3-5)

Source

URL

Database for “Membrane Proteins of Known Structure”

http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html

PDBTM – Protein Data Bank of Trans-membrane Proteins [154, 155]

http://pdbtm.enzim.hu/

MPDB – Membrane Protein Data Bank [156]

http://www.mpdb.tcd.ie/

Sansoms CGDB – Coarse-Grained Simulation Studies [97]

http://sbcb.bioch.ox.ac.uk/cgdb/

OPM database – Orientations of Proteins in Membranes [86]

http://opm.phar.umich.edu/

Table 2.

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Sources for Pre-equilibrated Membrane Patches & Force Field Parameters

Lab

Lipid Types

URL

Comment

Roland Faller

POPA, Texas Red DHPE/DPPC mixture

http://www.chms.ucdavis.edu/research/ web/faller/

Membrane patches and topologies for GROMACS compatible united atom force fields

GROMACS website

Cholesterol, Cholesterol/DPPC mixture, DLPC, DMPC, DMSO, DOPC, DPPC, POPC, POPG, SM

http://www.gromacs.org/Downloads/Us er_contributions/Molecule_topologies

Membrane patches and topologies for several GROMACS compatible force fields

Helmut Heller

POPC

http://www.lrz.de/~heller/membrane/m embrane.html

Only membrane patches, no force field topologies

Mikko Karttunen

DMPC, DMTAP, DPPC, POPC, POPE/POPG mixture, POPG

http://www.apmaths.uwo.ca/~mkarttu/d ownloads.shtml

Membrane patches and topologies for GROMACS compatible united atom force fields

Andreas Kukol

DMPC, DPPC, POPC, D/L-POPG

https://sites.google.com/site/bioherts/ho me/lipid-topologies

Membrane patches and topologies for the GROMOS 53a6 united atom force field

Alan E. Mark

ABPC, ALPC, AMPC, AOPC, APPC, ASPC, BAPC, Beta-Sitosterol, BetaStigmasterol, BLPC, BMPC, BOPC, BPPC, BSPC, Campesterol, Cholesterol, DAPC, DBPC, DLPC, DMPC, DOPC, DPPC, DSPC, Ergosterol, Lanosterol, LAPC, LBPC, LMPC, LOPC, LPPC, LSPC, MAPC, MBPC, MLPC, MOPC, MPPC, MSPC, OAPC, OBPC, OLPC, OMPC, OPPC, OSPC, PAPC, PBPC, PLPC, PMPC, POPC, PSPC, SAPC, SBPC, SLPC, SMPC, SOPC, SPPC

http://compbio.biosci.uq.edu.au/atb/?ta b=existing_tab&display_moltype=lipid

Membrane patches and topologies for the GROMOS 53a6/54a7 united atom force field

Siewert-Jan Marrink (CG scale)

Bi-lamellar DOPE, DOPC, DPC micelle, DPPC, DPPC vesicle, DPPC/choline mixture, DPPC/Di-C18:2-PC/choline raft bilayer, POPE, POPE vesicle

http://md.chem.rug.nl/cgmartini/

Membrane patches and topologies for the MARTINI coarse-grain force field

Mark S.P. Sansom [153]

1-octanol, Cholesterol, DLPC, DMPC, DOPC, DPG, DPhPG, DPhPGP-Me, DPhPGS, DphyPC, DPPC, POPC, POPC, POPG, POPG

http://lipidbook.bioch.ox.ac.uk/

Membrane patches and topologies for the AMBER GAFF, Bondini, CHARMM27, GROMOS 43a1/53a6 & MARTINI force field

D. Peter Tieleman

DMPC, DPC micelles, DPPC, PLPC, POPC, POPE

http://moose.bio.ucalgary.ca/index.php ?page=Structures_and_Topologies

Membrane patches and topologies for the GROMOS87 & GROMOS96 force field


determined from the hydrophobic belt of the protein [75, 76]. (III) The protein is accommodated in the membrane using the hydrophobic belt information to: (a) construct the membrane around the protein, which has the advantage that membrane sizes can be tailored to the protein of interest; or (b) insert the protein into a pre-equilibrated bilayer which might be larger than actually necessary for the protein to be investigated. Before the MD production runs can be started, both approaches require additional MD simulations to equilibrate the membrane properly around the protein, which is usually position-restrained to keep it close to its starting conformation. Here we provide an overview and example applications of commonly used modeling techniques available for both accommodation approaches. The recommended procedures for the preceding and subsequent working steps in setting up and running membrane protein simulations have been reviewed elsewhere [49]. For the application benchmarks we selected five membrane proteins representing different transmembrane structures as introduced in Fig. (1). The trimeric autotransporter Hia (PDB-ID 3EMO) [77] serves as an example for cylindrical transmembrane structure and the lipid A-modifying outer membrane enzyme LpxR (PDB-ID 3FID) [78] represents a tilted TM shape. The potassium ion channel KcsA (PDB-ID 1R3J)[79] was chosen for the conical shape of its TM section. Both the cell toxin alphahemolysin (PDB-ID 7AHL)[80]and the multi-drug efflux pump AcrB (PDB-ID 2GIF) [81] exemplify large protein complexes with irregularly shaped TM sections and water or lipid-filled central cavities. Fig. (2) shows the five test proteins after insertion into a palmitoyl-oleoyl-phosphatidylcholin (POPC) bilayer. Except for the CHARMM GUI membrane builder web interface [82, 83], all benchmarks were performed using a single Intel® Core™2 Duo CPU T8300 (2x2.4 GHz) on a 4 GB RAM DELL Vostro 1710 Notebook running Ubuntu Linux 9.10. Where a locally installed MD package was required, GroMACS 4.5.3 [84] was used.

Schmidt et al.

via simulation set up tools like editconf contained in the GROMACS package [84, 85]. Instead of identifying the hydrophobic belt by visual inspection, the orientation of protein in membranes (OPM) database [86] provides the predicted location of their membrane exposed sections for numerous membrane proteins. Identification of the hydrophobic belt by the OPM database is based on precomputed minimum transfer energy calculations where each protein was systematically transferred from a hydrophilic to a hydrophobic milieu [86]. Each PDB entry of the OPM holds the hydrophobic belt information encoded as two planes of pseudo atoms located at lipid head groups level. While this usage of the OPM still requires a manual protein membrane alignment, the CHARMM-GUI Membrane Builder [82] resorts to the OPM information to automatically align the protein with the membrane. In addition to that IMPALA, the algorithm implemented in GARLIC since version 1.6 and TMDET rely on protein structure analysis alone to detect the hydrophobic belt and predict orientation in the membrane [87-89]. Complementing the approaches described above we have developed a novel method to align membrane and protein that is independent from any pre-calculated data. LAMBADA employs a coarse-grained, grid-based cast of the protein surface to detect the hydrophobic belt and align the protein

2.1. Identify Membrane-Exposed Regions to Align Protein with the Membrane 2.1.1. Molecular Editors, OPM Database & CHARMMGUI Membrane Builder To incorporate a membrane protein into a bilayer, knowledge of the membrane-exposed protein regions is essential. For every transmembrane protein this information is encoded in a characteristic distribution of charged surface residues where protein areas in contact with the lipid tails are devoid of charged surface residues. Known as the hydrophobic belt [75, 76] this structural feature reflects the location of the membrane and needs to be aligned with the hydrophobic section of the membrane. This can be done manually or using automated methods. Common molecular viewers can be used to locate the hydrophobic belt by visual inspection and manually align the protein with the membrane when the viewer supports coordinate modification. If this is not the case, protein translocation along the membrane normal can be carried out

Fig. (2). Example proteins used for the application benchmarks after insertion into a POPC bilayer using InflateGRO. Whereas the trimeric autotransporter Hia serves as an example for cylindrical transmembrane structure and the potassium ion channel KcsA exhibits a conical transmembrane shape, the lipid A-modifying outer membrane enzyme LpxR was selected for its tilted transmembrane region. Both the cell toxin alpha-hemolysin and the multi-drug efflux pump AcrB exemplify large protein complexes with irregularly shaped TM sections and water (alopha-hemolysin) or lipid-filled central cavities (AcrB). Charged protein surface residues and lipid head groups have been highlighted in red and green.


(Schmidt & Kandt, manuscript in preparation) and will be available shortly free of charge for academical users at URL: http://csb.bit.uni-bonn.de/lambada.html. 2.2. Accommodate Protein Into the Membrane 2.2.1. Membrane Construction Around the Protein 2.2.1.1. Replacing Head Group-Sized Pseudo Atoms I: Woolf & Roux Membrane Builder The method introduced by Woolf & Roux [90, 91] uses hydrated lipid molecules randomly selected from a conformation library of pre-equilibrated bilayers derived from Monte Carlo simulations to replace head group-sized pseudo atoms that are initially placed in two parallel layers around the protein at the average head group location of reference bilayers. To reduce steric clashes, the lipids are subjected to rigid body vector operations of random rotations around the membrane normal axis or translations within the membrane plane. The resulting simulation starting structures require subsequent solvation and adequately long membrane equilibration times to optimize lipid packing and membrane structure, bearing in mind that lateral lipid diffusion in membranes occurs on a time scale of tens to hundreds of nanoseconds [92]. As all cavities in the protein’s transmembrane section that are large enough to accomodate head group pseudo atoms will be filled with lipids, special attention should be paid when preparing membrane proteins containing TM cavities (Fig. 1b). For membrane proteins with lipid-filled TM cavities like AcrB (Fig. 1b right, Fig. (2) bottom row, right), the resulting lipid packing should be checked and corrected manually if necessary. In contrast, membrane proteins with water-filled TM-cavities like porins or alpha-hemolysin (Fig. 1b center, Fig. 2 bottom row, left) require the subsequent removal of misplaced lipids from the channel or mould interior. The membrane builder offers the option to generate membranes of heterogeneous lipid composition and is available free of charge for academical users at: URL: http://thallium.bsd.uchicago.edu/RouxLab/membrane. html As usage of the downloadable shell script is restricted to the CHARMM MD package, for which at the time of writing a license fee of $600 is charged for academical users, the membrane builder was not included in our application benchmarks. We focus instead on its successor application the CHARMM-GUI membrane builder (see below) for which no locally pre-installed MD package is necessary. 2.2.1.2. Replacing Head Group-Sized Pseudo Atoms II: CHARMM-GUI Membrane Builder The CHARMM-GUI Membrane Builder [82, 83] allows a full system setup to be carried out via a platformindependent web-frontend [93] that permits numerous userdefined adjustments. These include the choice of protonation or phosphorylation states of protein residues, the addition of K, Na, Ca or Cl ions in defined concentrations and the option to download run input files from the server to perform locally a short 375 ps equilibration MD of the fully solvated system using six different combinations of position restraints and statistical ensemble schemes. For membrane protein


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accommodation, either an insertion (see 2.2.2.4) or a bilayer construction approach is available. The latter represents an extension of the Woolf & Roux method described above where the randomly picked lipid conformations stem from MD-generated reference bilayers. Furthermore rigid body translations of lipids are now also possible in the direction of the membrane normal. Cavities in the protein transmembrane region can also be interactively excluded during lipid placement and a larger library of 32 different lipid species is available. Whereas the included membrane editor permits an easy and straight forward way to generate heterogeneous membranes of user-specified lipid compositions, new lipid mixtures should be checked first in membrane-only simulations to determine wether the available experimental data are reproduced correctly. In principle the CHARMM membrane builder offers a userfriendly way to generate starting structures for membrane protein simulations, though we found that the lack of any units of measurement for the length and area data presented during the set up process was confusing. On the other hand the comfortable setup process binds the user to a predefined workflow. Working steps not included in that procedure such as changing the protonation state of arginines, using other than the four available ion species, the inclusion of compounds like protein substrates or cofactors not yet parameterized in the CHARMM force field, protein accommodation in multiple membranes, employing different schemes of position restraint application and release or the necessary conduction of longer membrane equilibration runs have to be performed outside the membrane builder. The web interface can be accessed at: URL: http://www.charmm-gui.org/?doc=input/membrane In practice, usage of the CHARMM GUI membrane builder is structured in 10 working steps organized in six stages of (1) reading the protein structure, (2) orienting protein and optionally solvating any water-filled TM cavities, (3) determining system size, (4) generating lipid, water and ion components, (5) assembling system components and (6) generating input files for equilibration runs [82]. During this process the user can download structure files and progress logs at different stages. For our benchmarks we determined the pure server computation time required to (a) produce a first solvated membrane-embedded protein structure obtainable during stage 4 (Table 3, left column) and (b) to complete the entire process including Monte Carlo-based ion placement, component assembly and the generation of equilibration run input files (Table 3, left column, times in brackets). Whereas generating the first downloadable membrane-embedded protein structure took between 3’50s (for 2,294 non-hydrogen atoms LpxR) and 7h25’ (for 23,627 atom AcrB), the remaining working steps of stages 4 - 6 required between 50% (Hia, alpha-hemolysin) and 75 % (KcsA, LpxR) of the entire set up process. In the case of AcrB, the setup was cancelled after a total running time of 125h. With exception of the overall preparation times of Hia (7’47s) and LpxR (13’30s) which might be explained by different server loads of the web application during the times of the testing, preparation times increase proportionally to the protein size. However, compared to the other methods tested (Table 3), the CHARMM GUI membrane builder method is rather time-intensive for larger


Table 3.

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Application Benchmarks of the Membrane Accommodation of Five Membrane Proteins of Different Size and Transmembrane Topology. See Text for Details Accommodation Time

Protein, PDB ID, (# Heavy Atoms)

CHARMM-GUI Membrane Builder

InflateGRO

g_Membed

Replacement Method

Insertion Method

LpxR 3FID (2294)

3’50s (13’30s)

n.a.

3’36s

1’22s

Hia 3EMO (2618)

3’55s (7’47s)

1’50s (19’52s)

3’17s

1’14s

KcsA 1R3J (3056)

14’ (55’30s)

n.a.

3’20s

1’13s

Alpha hemolysin 7AHL (16389)

7h20’ (13h52’)

n.a.

20’32s

5’36s

AcrB 2GIF (23627)

7h25’ (cancelled after 125h)

n.a.

38’

6’35s

membrane proteins and in case of AcrB, in the membraneembedded stage 4-structure, the in vivo lipid-filled central AcrB cavity did not contain any lipids. As with any other modeling technique, the starting structures obtained should be checked carefully before putting them to practical use. 2.2.1.3. Construct Bilayer with Enlarged Inter-Lipid Spacing and Scale XY Coordinates The technique introduced by Ash and co-workers [49] is a two step process in which a bilayer is first generated whose inter-lipid spacing is typically 5 to 10 times larger than in a real membrane. The first leaflet is constructed placing copies of a single lipid on a 2D grid in the XY plane. The second leaflet is a copy of the first rotated by 180° and positioned at a Z distance approximating the natural membrane thickness. After the bilayer-aligned protein has been incorporated and lipids within a user-defined distance cut-off have been removed, the bilayer is scaled to its natural dimensions, packing the lipids around the protein. This is done in a series of alternating steps of system-wide XY coordinate scaling by a factor smaller than 1 and energy minimization and/or short MD runs to remove steric clashes and conformational distortions. Throughout this, the protein is positionrestrained to conserve its original conformation. While the incorporation of multiple proteins is supported, the MD steps required for compression and membrane equilibration limit the method to smaller peptide simulation systems or building new bilayers from scratch, as the total set up time increases substantially for larger proteins. On the other hand, varying the number of different lipids placed on the grid permits an easy way to create heterogeneous membranes. Also attention should be paid to the initial distance cutoff for lipid deletion to ensure the resulting lipid densities in both leaflets are consistent with the protein TM shape (Fig. 1a). For proteins with lipid-filled TM cavities, these lipids have to be placed manually. The program is available free of charge for academical users at: URL: http://moose.bio.ucalgary.ca/index.php?page=Programs In practice we found that for a successful installation the Python script requires additional libraries not included in the downloadable archive. Furthermore, as the script employs GroMACS tools to accomplish protein accommodation, a pre-installed GroMACS package is prerequisite. Contrary to

the membrane construction methodology described above and in the accompanying publication, the downloadable tool uses a different and un-documented strategy based on expansion and compression steps of a pre-equilibrated membrane patch similar to the InflateGRO method (see 2.2.2.5). Due to an error in the implemented routine of removing of lipids within a user-defined distance from the protein – leading to the arbitrary deletion of lipids located clearly outside the distance cut-off – we were not able to perform any benchmarks using this method. 2.2.1.4. Lipid Self-Assembly Around the Protein With the increasing computer power and progress in the development of MD algorithms, it has become possible to have lipids self-assemble around a membrane protein in appropriately long MD simulations. Beside atomistic simulation studies on the formation of micelles [94] or planar lipid bilayers around a peptide [95], this approach currently has its greatest application in coarse-grained simulations. A notable example is the freely accessible coarse grain database of inserted membrane proteins [96-98], which currently holds 382 coarse-grained models of membrane proteins embedded in DPPC bilayers. However, once again, special attention is required for proteins with lipid-filled TM cavities. For example, the central lipid-filled cavity in AcrB is empty in the database. 2.2.2. Protein Insertion Into a Pre-Equilibrated Bilayer 2.2.2.1. Delete Lipids Overlapping with the Protein The easiest way for protein insertion into a preequilibrated bilayer is through the deletion of lipids within a user specified distance range around the protein using, for example, common molecular viewers to identify overlapping lipids, or employing tools like genbox in the GROMACS package [84, 85] to solvate the protein box with copies of an appropriately sized and oriented membrane structure. However, the highly disordered lipid conformations of the bilayer will produce a rugged hole around the protein which makes longer membrane equilibration times necessary before a tight lipid packing is achieved and the equilibrium area per lipid is reached. Additionally, in proteins such as AcrB, misplaced water molecules between the protein transmembrane surface and the bilayer may lead to persistent


protein-associated water clusters that require extra simulation time to equilibrate. On the other hand, given the ongoing advances in computer power, the deletion approach might become more and more feasible in the near future, particularly in CG simulations and also in long time (microsecond) atomistic MD studies. 2.2.2.2. Use Repulsive Forces to Drive Out Lipids from the Protein Volume: Cylinder, Mdrun_Hole & GRIFFIN The approach of using repulsive forces to create a hole in the membrane which is tailored for the protein that is to be inserted dates back to 1997. After a first step of distance cut off-based lipid removal, weak repulsive forces were applied to push lipid tails out of a cylindrical volume approximating the protein shape [99]. Originally developed for helices or helix bundles, the method was inapplicable to membrane proteins of non-cylindrical TM cross sections. This limitation was overcome five years later with the introduction of mdrun_hole, where after an initial step of lipid deletion from a cylinder, the protein’s Connolly surface was used as reference boundary to drive out lipid and water molecules from the space the protein will later occupy [100]. Whereas excellent protein / lipid packing could be achieved requiring only short membrane equilibration runs, the necessity of protein-specific fine-tuning of parameters like force constants for pressure and repulsive potential, the definition of Connolly surface and the effective position of its boundary, water repulsion parameters as well as the number of initially deleted lipids can make the method challenging to use. Generated starting structures should therefore be checked carefully to ensure lipids have completely left the Connolly surface interior and no flipped lipid orientations occur before finally inserting the protein and initiating MD runs. mdrun_hole has been incorporated into the GROMACS package. URL: http://www.gromacs.org/Downloads/User_contributions/ Other_software A successor to mdrun_hole, GRIFFIN employs molecular dynamics simulations with an additional gridbased force field component to expel lipids and water from the volume that is to be occupied by the protein [101]. Now the protein Connolly surface is already used in the initial step of deleting lipids and water molecules from the protein volume in order to preserve the original lipid densities in the bilayer leaflets as much as possible. Optionally the Connolly surface can be complemented by additional geometrical objects such as spheres, rectangular cuboids and cones which can be used to mask out lipid-free TM cavities like channels or pores. During the drive-out phase an implicit 3D gridbased force field is added to the MD force field where each grid-point inside the protein volume represents a force vector directed towards the nearest point on the protein Connolly surface. Outside the protein volume, molecules experience the additional force field as electrostatic and van der Waals interactions with the virtual protein. Combinable with different MD engines, GRIFFIN was developed with particularly large proteins of irregular TM cross-sectional shapes in mind. The amount of manual intervention is reported to have been minimized while the embedding process requires 100,000 – 150,000 MD steps for c11 ring [102] (80,000 atoms) and AcrB [81] (320,000 atoms) test


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systems amounting to total preparation times of 19 to 114 hours using 2 CPU cores [101]. GRIFFIN is also applicable to curved membranes. As with all molecular modeling approaches, the resulting structures should be checked carefully before protein insertion and initiating the MD production runs. GRIFFIN is available free of charge for academic users at: URL: http://www.forrestlab.org/software_databases.html The GRIFFIN package consists of five programs, written in C, including a daemon to be installed on a high performance cluster. Once the additionally required third-party software and C libraries (IPC, boost and MPI) were installed, compilation was unproblematic. The GRIFFIN workflow is structured in four consecutive working stages of 1) structure file conversion, 2) ranking and removal of overlapping lipids plus the optional manual definition and exclusion of TM cavities, 3) calculating the force grid for lipid expulsion and 4) preparing and executing the GRIFFIN MD. Each tool requiring a large number of input parameters, including some which could have been easily automated, such as detecting the center of the bilayer or the size of the simulation box. GRIFFIN has a steep learning curve which requires a detailed training to exploit the method’s vast application potential. Although introduced as compatible with different MD engines [101], at the time of writing a GRIFFIN tutorial was only available for NAMD but not for GroMACS. Therefore GRIFFIN was not included in our benchmark applications. 2.2.2.3. CHARMM-GUI Membrane Builder The insertion method of the CHARMM-GUI membrane builder resorts to a library of 90 pre-equilibrated, 128 or 256 lipids bilayer patches, each containing a central hole of different size [82]. Radii ranging from 1 to 45 Å, holes were created using weak repulsive forces during the pre-insertion membrane equilibration and the final bilayers have a maximum size of 9x9 nm. When a protein requires a larger patch size, contains lipid-filled TM cavities, or has a noncylindrical TM cross-section, the user is automatically referred to the membrane builder’s “replacement method” (see 2.2.1.2.). The CHARMM-GUI membrane builder can be accessed at: URL: http://www.charmm-gui.org/?doc=input/membrane In practice we found that the insertion variant of the CHARMM-GUI membrane builder was only applicable to the cylindrically-shaped Hia (table 3) where processing times of 1’50s and 19’52s (see 2.2.1.2) were achieved. For the other test proteins the web server automatically switched to the replacement method due to non-cylindrical transmembrane shapes or the size of the required membrane patch exceeding 9x9 nm. 2.2.2.4. Expand and Compress the Membrane Around the Protein: InflateGRO Using a common scaling factor, the InflateGRO method [49] resorts to lateral lipid translation within the membrane plane to first expand a bilayer, then delete lipids within a distance cut-off around the protein, followed by a series of alternating steps of compression and energy minimization to bring the system back to its natural dimensions. In this


process the area per lipid serves as reference parameter to assess the required number of shrinking steps. Using only energy minimization through an external MD engine to remove steric clashes between the lipids and the protein in absence of any other solvent, the method provides a quick and easy way to embed even large membrane proteins. Depending on the shape of the protein’s transmembrane cross-section as well as scaling and cut-off parameters used, excellent lipid packing can be achieved. As long as the protein’s membrane normal is parallel to the Z-axis, the method is also applicable to curved membrane systems. InflateGRO is controlled by three main parameters: the scaling factors used for 1) bilayer expansion and 2) bilayer compression, and 3) the protein – lipid cut-off distance. The larger the expansion factor, the longer the lipid exposure to vacuum during the energy minimization steps in the compression phase. This can lead to lipids losing their liquid-phase bilayer conformations and also to an overminimization of the molecules. The expansion factor should therefore be chosen to be as small as possible to minimize this effect. Expansion factors between 2 and 3 were found to give good results. As the distance cut-off is proportional to the circularity of the hole created by lipid deletion, a cut-off should be selected that reflects the TM protein shape as accurately as possible. Proteins with irregular TM shapes require smaller cut-offs than regular ones. The compression factor controls the speed of the shrinking phase. While in most cases a scaling factor of 0.95 was found to give good results, proteins with irregular shaped TM sections such as AcrB or MexB require a greater number of compression iterations using a smaller compression factor of 0.98 or 0.99. For our benchmarks we employed an expansion factor of 2, a distance cu-off of 18 Å and a compression factor of 0.98 for all proteins. InflateGRO is available free of charge for academic users at: URL: http://moose.bio.ucalgary.ca/index.php?page=Translate_ lipdis (initial version) URL: http://www.csb.bit.uni-bonn.de/inflategro.html (most recent version) As long as Perl is installed, InflateGRO does not require any further installation apart from a shell script executing multiple iterations of the program during the compression phase. In practice all test proteins could be embedded successfully (Table 3) with processing times ranging from 3’17s (Hia) to 38’ (AcrB). However, the lipid-filled TM cavities in AcrB were not solvated automatically and had to be filled manually as described in [103]. Beyond that, InflateGRO in its current version has two major drawbacks. Firstly, due to an over-estimation of the area occupied by protein - the under-estimation of the protein area as stated in the original publication [49] is a typographical error – there are inaccuracies in the area per lipid calculation which become more pronounced with membrane proteins of tilted or asymmetric transmembrane structure and those containing large cavities like alpha hemolysin. Secondly, as InflateGRO uses a residue name criterion to identify mobile (usually lipids) and immobile components (usually protein) which are respectively affected or unaffected by lateral translation, application flexibility is limited. For example, preparing lipid mixtures is laborious and requires workarounds such as the

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renaming of lipids between compression (all lipids share the same name) and energy minimization steps (each lipid species has its own name), whereas protein insertion into multiple membranes, as is necessary for resistance nodulation division (RND) multi-drug efflux pumps [104], for example, is not possible and the simultaneous insertion of multiple proteins for peptide or protein aggregation studies is restricted to the 2009 version of the program. Addressing the aforementioned issues as well as incorporating support for coarse-grained structures in MARTINI format, we have developed an extended version of InflateGRO (Schmidt & Kandt, manuscript in preparation) that will be available shortly - along with application tutorials including the test proteins used in this review - free of charge for academical users at: URL: http://www.csb.bit.uni-bonn.de/inflategro.html 2.2.2.5. Compress and Expand a Protein in the Membrane: g_membed Whereas the previous approach changes the membrane dimensions, g_membed [105] goes the opposite way, subjecting the protein to compression and expansion steps. The starting point of this approach is a fully solvated membrane system containing the protein in an optimized orientation. Next a user-defined scaling factor is applied to compress the protein in the XY dimensions, followed by the deletion of overlapping molecules within a user-defined distance cut-off. While maximum lateral compression results in all protein atoms being translated onto a single line, larger scaling factor of 0.5 – 0.7 were reported to require shorter equilibration times. To ensure the correct lipid densities per leaflet, larger scaling factors should be used with proteins of asymmetric TM shape, or the user should provide the expected difference in the number of lipids deleted from the two leaflets, as g_membed will then continue deleting lipids until this criterion is met. To grow the protein back to its natural dimensions, a series of alternating MD – during which protein-protein interactions are excluded – and expansion steps are performed, where the latter is based on a linear interpolation between current state and the initial uncompressed protein coordinates. After 1000 steps, the insertion process is completed. Proteins with lipid-filled TM cavities require compression factors as close to 1 as possible to preserve lipids in the protein interior. However, even with a large compression factor, an abnormally low lipid density in the protein interior is unavoidable and requires manual adjustment. As long as the membrane normal of the protein is parallel to the z-axis, g_membed is also applicable to vesicles. Since version 4.5, g_membed is part of the GROMACS distribution [84, 106] available free of charge for academic users at: URL: http://www.gromacs.org/Downloads Incorporated in the GroMACS package, g_membed does not require a separate installation procedure. Using the recommended standard settings all five test proteins could be successfully embedded within 1’13s for KcsA and 6’36s for AcrB (Table 3) making g_membed the fastest of the methods tested. However as stated above and observed with other methods, lipid-filled cavities present special challenges that require manual placement. Beyond that, high lipid densities


can occur near the protein especially for proteins of irregularly shaped TM cross-sections. To avoid longer equilibration times or unwanted membrane distortions stemming from these artifacts, means of assessing the area per lipid during insertion would be desirable for future versions. On the other hand the aforementioned effect is in part due to the compression and expansion approach itself, and since the protein shape is continuously altered throughout the embedding procedure, the amount of time the lipids have to adapt to the protein surfaces is shorter than with the InflateGRO method where the protein shape remains unchanged and only the lipid positions are altered (although large compression steps here will also lead to similar effects). Beyond that g_membed supports the option of inserting a protein into multiple membranes, whereas the simultaneous embedding of multiple proteins is not possible with the current version. When preparing proteins with large water-filled cavities such as TolC or alpha hemolysin the initial water deletion and subsequent protein compression and expansion produces regions of very low water densities making a second solvation of the system necessary. 3. EXAMPLE APPLICATIONS In this section we review examples of membrane protein MD simulations published in the last few years including for each study the method used to accommodate the protein into the membrane. When no preparation method is explicitly mentioned, this information is unfortunately not provided in the respective publication. MD simulation of membrane proteins has become a quickly growing field and as a consequence a complete discussion of all recently published simulation studies lies outside the scope of this review. Instead we therefore focus on selected example studies representing three major classes of membrane proteins: G protein-coupled receptors, channels and transporters. Further reviews on membrane protein simulations can be found for example in [29, 107-113]. 3.1. G protein-Coupled Receptors Forming the largest known protein superfamily, G protein-coupled receptors (GPCRs) are of critical importance in a wide range of eukaryotic signalling processes [114]. Responding to extracellular stimuli such as proteins, peptides, small molecules, hormones, ions and light [115], GPCRs activate second messenger-based signal transduction cascades involving the c-AMP or phosphatidylinositol intracellular signal pathways [116]. Due to their role in these pathways, GPCRs are the target of at least 30% of all pharmaceuticals, but their conformational flexibility and the low affinities of agonists have so far hindered research into the structural and conformational dynamics of agonist binding [117]. At the time of writing the crystal structures of two GPCRs are known at atomic resolution: the 2 adrenergic receptor (2AR) [115, 118-120] and rhodopsin [121] the primary light receptor in the animal visual system and the first GPCR successfully crystallized. Previous biochemical studies have demonstrated that the inactive state of the hormone sensor 2AR is stabilized by a salt bridging network, or “ionic lock”, between two of the trans-membrane helices [122]. The breaking of this ionic


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lock between the helices is proposed as an important first step in the activation of GPCRs, however the presence of an ionic lock was not observed in the crystal structures of the inactive state [115, 118-120]. This has lead to speculations as to whether the crystal structures were distorted during crystallization, or whether the presence of the agonist carazolol prevented the formation of the ionic lock. In an extensive molecular dynamics study [123], Dror and coworkers inserted wild type and mutant forms of the human 2AR, in the ligand-bound and apo forms, into a POPE bilayer by deleting overlapping lipids within a 2 Å protein – lipid head groups and 1 Å protein – lipid tail groups distance. Using long timescale simulations (0.5 to 2.0 µs) on the purpose-built Anton computer system, they were able demonstrate that incorporation of the crystal structure into a hydrated phospholipid bilayer is sufficient for the formation of the ionic lock; and that this ionic lock continually dissociates and reforms on sub-microsecond timescales at unbiased conditions [123]. When the authors incorporated mutagenesis data into the simulations, they found differences in the formation rate and stability of the ionic lock, suggesting that lock breakage may be one important step in 2AR activation [123]. Another long timescale (0.5 µs) molecular dynamics study on both the 1AR and 2AR, embedded into a SOPE membrane by the same method, supported the finding that the formation of the ionic lock stabilised the conformation of the inactive state [117]. Furthermore, the authors of this study also demonstrated the presence of internal water molecules – which are missing from the crystal structure – are essential for stability of the binding pocket [117]. An understanding of the molecular details of the activation process is of key importance in structure-guided drug design in this clinically important class of receptors. In a landmark study using over 30 µs of simulation time in total on the Anton computer system, Rosenbaum and coworkers demonstrated that the agonistbound and membrane-embedded 2AR spontaneously relaxes into a rigid, inactive conformation when it is not coupled to a G protein or a stabilizing antibody [124]. For protein insertion the same method was used as in the previous two examples. These results points out the inherent difficulties in with work on trans-membrane protein systems: membrane proteins are often part of a complex, multi-faceted cellular pathway that cannot be considered in isolation. 3.2. Channels Protein-assisted membrane transport occurs via two types of integral membrane proteins: transporters (see 3.3) and channels which facilitate passive transport by diffusion along a concentration gradient. Whereas some channels like the cell toxin alpha-hemolysin are unspecific and constantly open, others like ion channels employ (I) gating mechanisms that permit switching between an open and closed channel form as well as (II) selectivity filters to facilitate substratespecific diffusion control. Channel selectivity is controlled by the size, chemical composition and shape of the pore through which solutes diffuse, whereas channel gating can be regulated by voltage, stretch or pressure changes across the membrane, by light, or by the binding of an extracellular ligand, an internal second messenger, a nucleotide or another protein to the channel. The composition of the membrane


itself has marked effects on protein function, modulating the gating and conductance characteristics of many channels and transporters. For example, the concentration of sphingolipids and cholesterol in the membrane have been widely demonstrated to regulate the gating properties and activation of several ligand gated ion channels and serotonin receptors [125]. The presence of cholesterol in the membrane is also essential for functional activity of some transporters, such as P-glycoprotein, the primary multidrug efflux transporter in humans [126]. As the study of membrane channels through MD simulations is a wide and vivid field of research, we will subdivide this section into ion channels and other channels. 3.2.1. Ion Channels Voltage gated potassium (Kv) channels are widely expressed in excitable cells and play an essential role in membrane hyperpolarization during the propagation of an action potential across the plasma membrane [127]. Kv channels contain four voltage-sensor paddles connected to the tetrameric ion-conducting channel [128]. One key helix in the voltage-sensor paddle, the S4 helix, contains repeats of charged arginine residues that are exposed to the lipid bilayer. A continual problem in membrane protein crystallization is the need to solubilize the protein in detergents in order to promote crystallization. This means that the orientation of the protein in the membrane must be determined indirectly as described in section 2.1. In the case of the voltage gated ion channels, the relative orientation and range of motion of the voltage-sensor domains in relation to the channel pore has been a topic of considerable debate. One recent study that used unbiased MD simulations to investigate the role of the voltage sensor in channel gating [129] showed the S4 helix spontaneously adopting a 3-10 helical conformation in closed state, when embedded in a DPPC membrane using the Woolf & Roux membrane builder. This change in conformation allows a network of salt bridges to form throughout the domain and exposes all arginine repeats to a water-filled crevice in the center of the domain. Free energy calculations demonstrated that the irregular shape and dielectric inhomogeneity of the voltage sensor domain modulate the membrane potential, focusing the electric field around the S4 helix [129]. This effect means relatively small motions of the voltage sensor in the bilayer are sufficient to communicate the change in membrane potential and induce pore opening [129]. Since the crystallization of KcsA in 1998 [130], potassium channels have been studied extensively with molecular dynamics simulation techniques. In the last few years, advances in computational power have dramatically extended the timescales achievable with MD, allowing events such as ion permeation and hydrophobic gating to be analyzed in atomic detail. In one recent study, microsecond timescale all-atom MD simulations were used to examine single ion permeation through the Kv1.2 pore embedded in a POPE membrane in the absence of the voltage sensors [131]. To replicate the membrane potential, an electric field was applied across the membrane. In the hyperpolarized openstate channel conformation, the authors observed the transition to a closed, non-conducting state brought about by a decrease in the number of water molecules in the pore and simultaneous hydrophobic collapse of the pore [131]. This

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phenomenon of hydrophobic gating has also been observed in earlier simulations of the octane slab-emdedded MscL channel [132] and the truncated TM section of the acetylcholine receptor (NAchR)studied in a membranemimicking methane slab [133] and may have biological implications for the mechanism of fast gating phenomena observed in many ion channels. 3.2.2. Other Channels The primary biological role of aquaporins (AQPs) is to control the diffusion of water across the membrane. The wealth of crystal structures available and the rapid diffusion times for water have made them ideal candidates for molecular dynamics simulations. Two AQP0-controlled factors influencing the transparency of the lens in the eye are the hydration of the lens cells, and the maintenance of tight junctions between lens cells. AQP0 is found in both an octameric form at cell junctions, and a tetrameric form in other parts of the membrane [134]. In contrast to previous predictions based on static crystal structures, molecular dynamics simulations on both the octameric and tetrameric forms showed no significant differences in water permeability in the junctional or non-junctional form [134]. To build the system analogous to a junction between cell membranes, a tetramer of AQPO channels were inserted into a pre-equilibrated POPE membrane, then the system was duplicated and rotated by 180°, then positioned so that the AQPO tetramers formed tight protein-protein contacts between the two bilayers. The resulting simulations indicate that the low permeability of AQP0 to water may be an important factor in maintaining the mechanical stability of the junction, which in turn plays a major role in maintaining the transparency of the lens [134]. Other studies have suggested that aquaporins may also play a role in O2 and CO2 diffusion [135]. One recent molecular dynamics study investigates the permeability of AQP4 to NO and O2 [136]. To set up a simulation for explicit gas diffusion, the authors first embedded the AQO tetramer in a POPE membrane by deleting overlapping lipids within a 0.8 Å distance cut-off of the protein and solvated the system. 150 of the bulk water molecules were then replaced with NO. Umbrella sampling was used to determine the potential of mean force of both NO and O2 was 3 kcal mol1 along the central pore of AQPO. This small barrier was attributed to the orientation of charged residues near the pore entrance. While this study demonstrates that gas permeation is energetically possible, it has not yet been established whether this phenomenon occurs physiologically [136]. The Outer membrane porin (Omp) proteins regulate the diffusion of small molecules across the outer membrane of Gram negative bacteria [137]. Of particular interest is their role in small molecule or antibiotic uptake through the bacterial outer membrane [137]. While simulations of membrane proteins are generally focused on the behavior of the protein in a physiological environment, in some cases, the experimental results may be better characterized by incorporating the protein into a detergent or micelle environment instead of a phospholipid bilayer. In many cases, this technique of approximating a membrane environment significantly reduces the size of the system to be simulated, decreasing the computational time per nano-


second and thus allowing longer timescales to be simulated. However, it must be noted that the composition of the membrane often has an effect on protein function, dynamics and stability. In one recent investigation, Danelon and coworkers have embedded OmpF into micelles of the crystallization detergent lauryl dimethyl amino oxide (LDAO) [137]. To examine antibiotic binding, they placed different antibiotics in the pore of OmpF. The authors also measured the experiential diffusion rate of OmpF reconstituted into bilayers. These molecular dynamics studies show that the zwitterionic -lactams ampicillin and amoxicillin have a high affinity for OmpF, while the monoanionic carbenicillin, azlocillin, and piperacillin interact weakly or not at all with OmpF [137]. These simulations have determined that OmpF has a binding site for ampicillin and amoxicillin near the center of the pore, at the narrowest point in OmpF [137]. In another study utilizing a combined experimental and molecular dynamics approach, the binding interactions between the central constriction of the OmpF pore and one of two penicillins, either ampicillin or benzylpenicillin, were characterized for OmpF in LDAO micelles [138]. The simulations used both equilibrium molecular dynamics and the metadynamics (also known as local elevation) simulation technique, which adds a memorydependent potential energy biasing term to prevent the simulation from revisiting conformations that have already been sampled. This technique allows for a more extensive sampling of the potential energy of the antibiotic and OmpF. The results demonstrate differences in the interaction “hotspots” for each antibiotic that may be exploited for rational drug design [138]. Playing a central role in a numerous biological processes, the mechanosensitive channels facilitate a sudden, nonselective release of solutes and water to balance the osmotic conditions on both sides of the membrane as response to an increasing tension in the membrane [139]. One of the best characterized members of this protein family is the mechanosensitive channel of large conductance MscL [140] which has been recently subjected to a 40.5 s coarsed-grain simulation study embedded in a 16 nm diameter liposome [141] to study membrane tension-regulated channel gating. Using the liposome formed in a preceding simulation of spontaneous lipid self assembly, insertion of MscL in its closed conformation was carried out by deleting overlapping lipids. After 0.5 s of simulations in which the membrane tension was steadily increased by transferring water into the liposome interior, the authors observed a spontaneous activation of MscL at the limit of membrane elasticity. Opening in an asymmetric iris-like manner, the single channel was sufficient to relax liposomal stress induced by a [142] bar pressure difference during the following 40 s of simulation. The findings also suggest a computational means of rational drug design using MscL-containing liposomes as delivery vehicles. Furthermore, the iris-like channel opening observed in this study exemplifies the large potential of coarse-grained MD to predict protein conformations in experimentally unsolved states even though as in this case the MARTINI force field is restricted to conformational changes that preclude any changes on a secondary structure level.


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3.3. Transporters Transporters resort to an energy source like light, proton motive force or ATP hydrolysis to actively transport substrates against a concentration or electrochemical gradient. Generally substrate-specific transporters follow an alternate access mechanism, ensuring the protein is never open in both directions of transport at the same time. Depending on whether a single species of molecule is transported, or whether several molecules are simultaneously transported in the same or opposite directions, transporters can be classified into uniporters, symporters or antiporters. One of the simplest transporters crystallized to date is the leucine transporter (LeuT), which utilizes a chloride dependent Na+ coupling mechanism to power the transport of neutral amino acids across the synaptic cleft [142]. LeuT was crystallized in the presence of its leucine ligand and three different agonists, at an intermediate point in the transport cycle [143], leaving the precise details of the transport mechanism unknown. One study implementing 100 ns MD simulations has examined the process of substrate release from the LeuT binding pocket, to provide a reverse chronological view of the events involved in substrate association, permeation into and binding to LeuT binding site [144]. One of the underlying questions in amino acid transport is what determines high affinity substrate specificity in the binding of neutral amino acids? To address this question, free energy calculations have been performed on LeuT dimers embedded in a pre-equilibrated POPE membrane patch, complexed with the neutral and zwitterionic form of its ligands, Leu, Gly and Ala. This study showed that, in contrast to the working hypothesis [144] leucine must be in its zwitterionic form to enable highaffinity binding to LeuT and lead to the identification of key amino acids that determine selectivity of leucine over alanine and glycine via a best fit mechanism [144]. It is important to note that for a study such as this, which is examining the permeation of zwitterionic verus neutralized amino acids, the interpretation of all results are critically dependent on the pKa, and thus the protonation state, assigned to each ionizable residues in the protein. This is also the case for simulations investigating the related ammonia/ammonium transporter, AmtB. The structure of AmtB was crystallized with its substrate bound to it [145]. In contrast to the case for neutral amino acids, NH4+ is the predominant physiological form, but it is transported through AmtB in the neutral NH3 form [145]. Molecular dynamics simulations investigating the transport of ammonium versus ammonia in an AmtB monomer embedded in a DMPC bilayer using InflateGRO revealed that a key aspartate in the entry vestibule to the AmtB pore is the proton acceptor and gating residue, allowing the deprotonation of NH4+ and subsequent transport of NH3 across the membrane [146]. This result highlights the importance of dynamics in the interpretation of structural information and give a plausible explanation for the conservation of this key aspartate residue across all Amtrelated proteins [146]. Simulations on one member of the major facilitator family of transporters, GlpT (glycolipid transfer protein), have highlighted the molecular basis of transport, showing


that during the transport cycle, the substrate glycerol-3phosphate is coordinated by charged residues [147]. During transport, the protein moved around the substrate in a rockerlike alternating access motion, changing the electrostatic potential of the binding site [147]. GlpT insertion was carried out deleting all overlapping lipids. ABC (ATP binding cassette) transporters use the energy released from the ATP hydrolysis in their nucleotide binding domains to power transport of substrate across the membrane [14]. In the ABC import proteins, a separate substrate binding protein associates with the membrane transporter [14], delivering the substrate. Addressing the question of substrate sensitivity, the E. coli vitamin B12 importer BtuCD and its substrate binding protein ButF were recently investigated in a combined protein-protein docking and simulation approach [149]. Using InflateGRO for protein insertion, two BtuCD-F docking complexes with opposite BtuF orientations were simulated in a POPE / water environment to investigate their dynamics and BtuCD’s conformational response to the presence and absence of BtuF, vitamin B12 and Mg-ATP in a series of 28 independent MD simulations [149]. Although simulation time was restricted to 20 – 30 ns the authors observed conformational trends throughout the multi copy runs, suggesting that B12-loaded BtuF stabilizes the open conformation of BtuCD, whereas the transporter begins to close when BtuF or vitamin B12 was removed. If the findings are correct, the study provided evidence that BtuCD, like other ABC transporters is capable of substrate sensitivity although a previous reconstitution study [150] which has suggested otherwise. A pair of pore-lining, nonTM helices was identified as hotspots of conformational change and proposed that they act as possible substrate sensors. Molecular dynamics studies on the related ABC exporter SAv1866 which employed InflateGRO for membrane insertion, showed the choice of lipid environment modulated the conformational changes in the transmembrane domains [151]. Similarly, simulations of homology models of the ABC transporters P-glycoprotein [152] and TAP-12 [153] demonstrated that the quality of the homology model could be improved by taking into account the proteinmembrane environment. InflateGRO was used for both these studies. 4. CONCLUDING REMARKS In this paper we have reviewed currently available techniques to build starting structures for molecular dynamics simulations of membrane proteins and performed application benchmarks using five membrane proteins of different size and trans-membrane structure. Although the preparation techniques presented employ different methodologies and ideas, one should keep in mind that in the end it does not matter which preparation technique is used to build a starting structure as long as the known experimental data are reproduced sufficiently well. However, in practice we find the currently available techniques differing in a number of aspects including usability, length of training period required before a tool’s potential can be fully exploited, the overall size of the simulation system, the membrane protein topology as well as the MD package and force field to be used. Although restricted to a handful of

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examples, our set of five test proteins might provide a useful guide line in assisting in the choice of protein accommodation method when setting up molecular dynamics simulations of membrane proteins. Regardless which method is eventually employed, it is always useful to never blindly trust any of the presented methods and techniques and always check and validate the obtained results before proceeding in the simulation setup. As evident from the example application studies presented, even though molecular dynamics simulations of (membrane) proteins is still a young field, the advances made over the last years have been impressive; permitting us now to address biological questions by computer simulation on a time and complexity scale that was considered impossible only a few years ago. CONFLICT OF INTEREST The author(s) confirm that this article content has no conflicts of interest. ACKNOWLEDGEMENTS We thank Nadine Fischer for proof reading and helpful comments. This work was financially supported by the Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen. ChK is a junior research group leader funded by the NRW Rückkehrerprogramm. MLO is supported by a University of Queensland Postdoctoral Fellowship. REFERENCES [1] [2]

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Received: April 01, 2011

Revised: September 01, 2011

Accepted: December 12, 2011

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