Membrane proteins and drug discovery: what is the link

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Drug Discovery Today: Informatics (Computational Drug Discovery)

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Computational Analysis of Membrane Proteins: the Largest Type of Drug Targets

Yalini Arinaminpathy*

,1

, Donald M. Engelman

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, Mark B. Gerstein

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Address:

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Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney

10 Avenue, New Haven, CT 06520-8114, USA

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12 * Corre

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ponding Author:

13 Dr Yalini Arinaminpathy; E-mail: yalini.pathy@yale.edu

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25 – 30 Word Teaser

: The sheer pharmacological importance of membrane proteins

17 contrasts starkly with their limited number of known structures. Computational tools are

18 valuable in bridging the gap between their structure, function and mechanism.

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Keywords:

Membrane Proteins, Molecular Dynamics, KcsA, GPCR, ion channels.

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Abstract

21 The biological importance of integral membrane proteins, from the level of the

22 cell to entire organism is highlighted by the profound effects of various genetic

23 deficiencies in transporters and channels. Given their key roles and significance, it is

24 therefore necessary to understand their structures and thence mechanisms and regulation,

25 at the molecular level. Membrane proteins represent approximately 30% of currently

26 sequenced genomes. Paradoxically, however, at present, > 45,000 crystal structures are

27 deposited in the protein data bank (PDB), of which only 2% are of membrane proteins.

28 Interestingly, 971 protein folds are currently known, of which only 5% (49 folds) are of

29 membrane proteins. The great disparity between our understanding of soluble proteins

30 and membrane proteins has occurred largely due to the many practical problems of

31 working with membrane proteins, specifically difficulties in expression, purification and

32 crystallization. Thus, technological developments have been increasingly utilized in order

33 to make crucial advances in understanding membrane protein structure and function.

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1.1 Introduction

36 Integral membrane proteins play essential roles in numerous physiological

37 functions, such as molecular recognition, energy transduction and ion regulation. Despite

38 the experimental challenges of studying these proteins, they are critical to understand

39 since they represent more than 60% of drug targets [1], [2]. For example, G-protein

40 coupled receptors (GPCRs), a class of membrane proteins, are intensively studied using

41 computational resources since the malfunction of these receptors results in serious

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43 disorders such as hypertension, congestive heart failure, stroke and cancer. On a similar scale, genetic disorders of ion channels result in ‘channelopathies’ such as cystic fibrosis,

44 Bartter syndrome and paralysis. Therefore, ongoing technological advances are exploited

45 to study membrane proteins, in order to improve or develop novel pharmacological drug

46 targets.

47 The availability of complete or partial genome sequences for a number of

48 organisms from a number of domains including the eubacterial, archaean and eukaryotic

49 domains now makes possible much more detailed studies of membrane protein topology.

50 Compounded by their genomic abundance, the use of computational tools in this field is

51 essential and timely. In combination with the advancement of simulation techniques, the

52 advent of structural genomics has spurred the membrane protein field to consider high-

53 throughput methods, which can help redress the disparity between soluble proteins and

54 membrane proteins. Indeed, numerous bioinformatics and proteomic analyses (e.g.[3],

55 [4], [5], [6], [7]) have been carried out to examine membrane protein architecture and

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Drug Discovery Today: Informatics (Computational Drug Discovery)

56 even to closely analyze detailed stabilizing and mediating interactions between

57 transmembrane helices in membrane proteins.

58 Membrane proteins are, in many respects, easier to investigate computationally

59 than experimentally, due to the uniformity of their structure and interactions [8], [9]. The

60 high propensity to form secondary structures reduces the number of degrees of freedom,

61 which determine the protein’s fold, and hence lowers the complexity of predicting the

62 structures of these proteins. Computational techniques represent key methods for relating

63 the few static experimental membrane protein structures to dynamic biological systems,

64 thereby yielding maximum benefit from the limited structural and mechanistic

65 information available. The computational techniques employed in the biological arena to

66 explore membrane proteins are dizzyingly vast, thus in this review, we will focus on one

67 intensively utilized technique: MD simulations.

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1.2 Membrane Protein Structure

Membrane proteins are essentially divided into two main classes: some contain a

71 significant portion of their mass within the interior of the membrane (intrinsic or integral

72 membrane protein (IMP)) while other proteins are only associated to the membrane

73 surface (extrinsic or peripheral proteins). For IMPs, two common structural motifs have

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75 been observed for the transmembrane (TM) domains of membrane proteins: (1) α-helical or (2) a β-sheet topology [10]. These two folds [Figure 1] are the simplest solutions to

76 satisfying the hydrogen bonding potential of the polypeptide backbone amide groups

77 within the lipid bilayer. The majority of IMPs display α-helical transmembrane segments

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78 and can be further divided into two types: bitopic (those which traverse the lipid bilayer

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80 with a single α-helix) and polytopic (an α-helical bundle).

Membrane proteins that are α-helical typically form well-packed bundles as, for example,

81 found in bacteriorhodopsin, photosynthetic reaction centers and cytochrome C oxidase.

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Formation of β-sheets is seen in bacterial outer membrane proteins (e.g. OmpA [11] and

83 FecA [12]), which span the membrane as β-barrels.

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1.3 Experimental structure determination

97 The disparity between our knowledge of soluble proteins and membrane proteins

98 is largely due to the practical difficulties involved in expressing and crystallizing the

99 latter [13]. Their inherent membrane-bound nature makes structure determination a

100 particular challenge, and thus requires special treatment. This is particularly true for α-

101 helical membrane proteins as they tend to be hydrophobic and are therefore difficult to

102 unfold and refold in vitro. β-barrel proteins, on the other hand, are more hydrophilic and

103 amenable to the traditional methods of denaturation and refolding into detergents, or

104 directly into lipids [14], [15].

105 Three main bottlenecks exist with obtaining structural information of membrane

106 proteins. Firstly, it is difficult to obtain the protein of interest since membrane proteins

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107 are usually only present in the cell at low concentrations. Overexpression is therefore a

108 necessity for the majority of membrane proteins that cannot be readily obtained in

109 sufficient amounts from their native environments [16]. Many different expression

110 systems are used though each have their drawbacks, including low yield (often due to

111 toxicity), heterogeneous post-translational modification, low stability and partial

112 proteolysis [13]. The majority of membrane protein crystal structures result from proteins

113 that naturally occur at high concentrations or have been overexpressed in a homologous

114 system. Secondly, membrane proteins are naturally embedded in a heterogeneous

115 dynamic environment of the mosaic lipid bilayer [Figure 2] and it is impossible to use

116 high-resolution experimental techniques in their native environment.

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126 The proteins therefore need to be extracted from the native membrane and studied in

127 detergent or lipid environment in vitro, which leads to difficulties in sample preparation

128 for biophysical methods, such as X-ray crystallography and NMR. However, cryo-

129 electron microscopic analysis differs from these techniques in that it can be used to study

130 membrane proteins in a crystalline or non-crystalline state at high resolution [17]. This

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131 has enabled, for example, the structure of bacteriorhodopsin to be analyzed to a resolution

132 of 2.8Å [18], [19]. Thirdly, membrane proteins are generally insoluble in aqueous

133 solution hence detergents are required in concentrations above the critical micellar

134 concentration (CMC). Too much detergent can denature the protein or impede

135 crystallization by phase separation, whilst too little and the protein may become

136 insoluble. The production of three-dimensional (3D) or two-dimensional (2D) crystals

137 remains one of the major challenges in obtaining structural information.

138 Despite the inherent difficulties in studying the structure of membrane proteins,

139 they remain a crucial area of study due to their essential role in the control of important

140 biochemical processes. A number of experimental methods exist and are continually

141 being developed with the aid of technological tools to extract structural information on

142 membrane proteins. Spectroscopic methods [20], [21] such as vibrational spectroscopy,

143 Raman, FTIR and circular dichroism (CD) have been utilized to determine their

144 secondary structure and to help distinguish between competing models of structure or

145 function. Bacteriorhodopsin [22], the acetylcholine receptor [23], lactose permease [24]

146 and the outer membrane proteins of E. coli [25] are examples of membrane proteins

147 whose secondary structure content have been determined with such techniques.

148 Alternative methods, namely crystallographic techniques, have since been used to

149 determine high-resolution structures. Three methods are generally employed: electron

150 microscopy, NMR and X-ray crystallography. Despite the difficulties involved in

151 generating large and sufficiently well-ordered 3D crystals, X-ray crystallography is still

152 the most successful and least difficult technique for obtaining high-resolution structures.

153 Electron crystallography [26] and atomic force microscopy (AFM) [27], [28] are also

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154 used as methods for membrane proteins whose natural propensity is to form 2D arrays

155 [29].

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156 Each method has its own advantages and hence the structural data obtained are

157 complementary. All methods are generally used in parallel in an attempt to achieve the

158 best structural description of a membrane protein. In addition, methods are continually

159 being developed, with the use of computational resources, leading to an increasing rate of

160 membrane protein structure determination. From the plot [Figure 3], the growth of known

161 structures is exponential with a growth rate of ~1.3-fold per year, but lags behind the rate

162 for soluble proteins during the equivalent time period. Assuming a continuous

163 exponential rate of growth in the number of structures determined, we would expect close

164 to 300 structures to be known by the end of 2008. Despite the increasing rate of structure

165 determination, improved structure prediction methods combined with computational tools

166 are important in studying membrane proteins.

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1.4 Computational Structure Determination

179 Computer simulation methods have provided key insights into the general nature

180 of protein motion and aspects of motion linked to the function of proteins in their native

181 state. They are rapidly becoming a standard tool to study the structure and dynamics of

182 membrane proteins. With the increasing number of high-resolution structures of

183 membrane proteins, a wide range of membrane proteins can now be simulated over time

184 spans that capture essential biological processes. Whilst X-ray structures of membrane

185 proteins provide static, spatially and temporally averaged snapshots of the proteins in

186 specific crystal environments, simulations enable us to explore the structural dynamics of

187 the proteins in an attempt to bridge the gap between structure and function of proteins. By

188 employing computational methods one can combine both experimental and theoretical

189 data of ion channels in order to describe their physiological properties in terms of

190 underlying physical processes.

191 One of the main challenges is to relate molecular structures to the physiological

192 properties of the protein. For ion channels this has been addressed by describing the

193 structure of the channel at varying levels of detail and accuracy, where the key regions

194 can be broken down into the transbilayer pore, the selectivity filter and the gate. A wide

195 variety of computational approaches such as molecular dynamics (MD) simulations (e.g.

196 [30], [31], [32]), continuum electrostatic Poisson-Boltzmann (PB) theory [33], [34],

197 Brownian dynamics (BD) [35], [36], electrodiffusion theory [37] have helped to refine

198 our understanding of the molecular determinants of channel function. MD arguably

199 provides the most detailed information in the theoretical studies of membrane proteins.

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200 In an MD simulation, all atoms in the system (including ions and water

201 molecules) are represented explicitly. In the classical fine-grained approach, simulations

202 are typically carried out using empirically determined pairwise interaction potentials

203 between the atoms. Another MD approach, known as

ab initio

MD, uses interactions

204 between atoms determined from first principles electronic structure calculations. Since

205 there are no free parameters with this approach, it could potentially be the ideal approach

206 to modeling ion channels. However, due to the extremely demanding nature of the

207 computations, its applications are currently limited to very small systems.

208 With the atomistic approach of classical MD simulations, rapid advances in

209 simulation methodologies and computational power has led to accessible timescales of up

210 to 0.1

 s. Amongst all structurally known membrane proteins, the ion channels (KvAP,

211 KcsA), transporters (AQP, GlpF, ABC transporter), and outer membrane proteins

212 (OmpA) have been examined via simulation in particular detail. The methods described

213 will be briefly compared using the bacterial K

+

channel, KcsA, as a case study. This is the

214 first biological ion channel whose tertiary structure was elucidated [38] and has been

215 studied extensively in terms of ion selectivity, permeation and gating.

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1.5 Comparisons between MD techniques

218 MD simulations on KcsA have been employed to examine channel selectivity, ion

219 permeation and ion transport energetics in potassium channels, with the main focus being

220 on the selectivity filter and understanding the permeation properties of K

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ions in the

221 filter and cavity region. Many of the results from MD simulations based on realistic all-

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222 atom models have been consistent with the information obtained from high-resolution

223 structural data [39].

224 The scope of MD simulations can be extended by using commonly utilized

225 algorithms. For example, three methods employed are (i) umbrella sampling [40]; (ii) the

226 application of external forces to the system, such as using an expanding sphere inside the

227 pore at the gate region of the KcsA channel to induce gating [41], and with the

228 application of steered-MD; (iii) alchemical free energy perturbation (FEP) [42][43].

229 Although results from these techniques increase our confidence in MD, we cannot build a

230 complete picture of ion permeation if ion fluxes cannot be simulated or if channel

231 conductance cannot be calculated. Single-channel measurements reveal the net

232 translocation of one ion in KcsA to be in 10-20 ns [44] which is the order of timescales

233 accessible by MD simulations [42][45]).

234 Despite the significant increase in computational power and molecular dynamics

235 methods, the direct simulation of ionic fluxes across channels and large conformational

236 changes using an atomistic description remains computationally challenging at present.

237 This is due to time scales of 10 – 100 ns being much shorter than the typical timescale for

238 allosteric effects which is usually in the order of microseconds to milliseconds. Thus,

239 biased MD methods, such as steered MD (e.g. [46]) and targeted MD (e.g. [47]) have

240 been developed to circumvent these time scale limitations. However, the above described

241 weakness of MD is the strength of Brownian Dynamics (BD). The drawback of BD is,

242 however, the comparatively poor description or parameterization of the biological system

243 simulated. Thus, diffusion coefficients and free energies for example, which can be

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244 determined with MD, cannot be calculated with BD. Hence, both BD and MD are

245 complementary.

246 In BD, ion permeation can be simulated for sufficiently long to measure channel

247 conductance without having to treat a system in all atomic details explicitly. BD

248 simulations (for example on KcsA [48] [49]) treat protein atoms forming the channel as

249 rigid, and the water implicitly as a dielectric continuum performing Brownian motion.

250 Despite these severe limitations of the continuum electrostatic approximation and the

251 assumption of a rigid channel structure, BD simulations [50] confirmed the multi-ion

252 mechanism to be in agreement with the ion flux determined experimentally [51]. The

253 ability to compute current flow across ion channels confers a distinct advantage to BD

254 simulations over other techniques with the applications of BD to calculating current and

255 voltage conductance in ion channels. The assumptions in BD of treating the water-protein

256 interface as a rigid boundary and the treatment of water in a narrow pore as a continuum

257 are simplifications since proteins (and lipid bilayers) are in fact dynamical, undergoing

258 fluctuations on a picosecond timescale, which is much more rapid that the timescale for

259 ion permeation.

260 The treatment of the water-protein boundary has been modified in some studies in

261 an attempt to reduce its simplified stochastic nature. For example, an elaborate treatment

262 of boundaries was proposed [52][53] using a grand canonical Monte Carlo (GCMC)

263 method. However, comparison of BD using a simple stochastic boundary and the GCMC

264 boundary [54] revealed no significant differences with the results obtained when the

265 boundaries were at a reasonable distance from the channel. MD is also the preferred

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266 technique for size-dependent selectivity among ions with the same valence, since such

267 ions cannot be distinguished in BD. Whilst microscopic quantities can be deduced from

268 BD, the increased high-level detail adopted by MD and Monte Carlo (MC) algorithms

269 enables the analysis of large-scale conformational changes. For example, one study [55]

270 explored the conformational changes between the open (KcsA crystal structure [38] and

271 closed forms (model generated from MthK [56]) of KcsA. The simulation of the large-

272 scale conformational transition was run by imposing lateral forces to the C-termini of the

273 inner helices and minimizing the energy at each step. As a result of the applied forces the

274 inner helices converged to form a tightly packed structure, with a change in backbone

275 geometry in the central region.

276 Whilst MD provides the most detailed information about the dynamics of ion

277 channels, currently accessible simulation times are its greatest limitation. Hence it is

278 unclear at present if the results obtained from MD simulations reflect reality or are an

279 artifact of the method. However, this problem seems sure to be surmounted in the future

280 with the doubling of computer speeds over the years. In the meantime faster, more

281 coarse-grained methods are being employed [57][58] to calculate the conductance of ion

282 channels. In addition, free energy methods such as replica exchange and ensemble

283 dynamics [59][60] are methods that are becoming increasingly viable with increasing

284 computational power. Despite such hurdles, MD simulations in combination with other

285 computational tools such as homology modelling, and experimental studies such as

286 mutagenesis analysis, have proved to be essential in the study of membrane protein

287 structure and function, which in turn enables the development of novel pharmacological

288 drug targets.

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1.6 Conclusions and Future Outlooks

290 Integral membrane proteins (IMPs) perform key functions in regulating the

291 physiological state of the cell. This is especially true for receptors and ion channels that

292 control, for example, the transmembrane (TM) potential. The scarcity of IMP structures is

293 due to the fact that the route from membrane protein sequences to atomic-resolution

294 structures is not as straightforward as for their soluble counterparts. This is, in turn,

295 primarily due to the substantial difficulties with overexpression and crystallization of

296 IMPs. Thus, the use of computational tools such as protein simulation methods, in

297 combination with experimental and structural genomic studies is becoming increasingly

298 valuable in studying the structure and function of membrane proteins.

299 The explosion of genomic data in combination with huge advances in

300 computational resources and experimental techniques is leading to a greater

301 understanding of biological structure, function and mechanisms. Considering the

302 dramatic advancements in molecular dynamics simulation methodologies in recent years,

303 it is likely that current drawbacks will be overcome considerably in the near future.

304 Reassuringly, over the last few years there has been a dramatic increase in the number of

305 membrane protein crystal structures obtained, with 30 structures being solved in 2006

306 alone.

307 Unsurprisingly, given the immense computing power and wealth of genomic and

308 structural data, recent years have seen a rise in structural genomics initiatives primarily

309 focusing on membrane proteins in an attempt to harness the synergy between the growing

310 data and technology available. Examples of such initiatives include the (1) Swiss

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311 National Center of Competence in Research (NCCR; www.structralbiology.ethz.ch

), (2)

312 Membrane Protein Network (MePNet; www.mepnet.org

), (3) European Membrane

313 Proteins (E-MeP; www.e-mep.org

), (4) Protein Wide Analysis of Membrane Proteins

314 (ProAMP; www.pst-ag.com

), (5) Biological Information Research Center, Japan (BRIC;

315 unit.aist.go.jp/birc) and the (6) Membrane Protein Structure Initiative (MPSI;

316 www.mpsi.ac.uk

). At present, the large majority of crystallized membrane proteins are

317 bacterial proteins, thus there is an urgent need to obtain structures of eukaryotic

318 membrane proteins as these could be potential drug targets. In this respect, structural

319 genomics initiatives are essential for rapidly increasing the structure determination

320 throughput of eukaryotic membrane proteins. Interestingly, this situation is analogous to

321 that of soluble proteins; slow structure determination in the 1970s was followed by an

322 exponential increase of structures generated due to improved experimental protocols.

323 The paradox posed by the sheer number of potential helical membrane proteins

324 and the lack of high-resolution structural and thermodynamic information for them

325 emphasizes the extensive work that remains to be done in the field of membrane proteins.

326 The potential payoff may be great as this class of proteins has historically contained

327 excellent targets for therapeutics. Advances in our ability to understand and manipulate

328 membrane proteins may lead to the discovery or design or pharmaceutical agents that can

329 modulate their functions.

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520

Figure Legends

Figure 1: Two examples of membrane proteins. KcsA (left) (PDB: 1K4C) is a voltage-

521 gated K+ selective α-helical protein. OmpA (right) (PDB: 1QJP) is an example of a β-

522 barrel membrane protein. The dashed lines indicate the position of the bilayer.

523

524

525 Figure 2: Illustration of a membrane protein (KcsA, shown in purple) embedded in a lipid

526 bilayer. For clarity, the water molecules on either side of the lipid bilayer have not been

527

528 included. The hydrocarbon core of a membrane is typically ~25-30Å wide with the headgroups spanning ~10Å. The polar head groups of the lipids face the aqueous

529 environment on both sides of the membrane, whereas their hydrophobic chains form the

530 insulating interior of the bilayer. Owing to the ester carbonyls and water associated to the

531 lipid headgroups, lipid molecules possess electrical dipoles which result in a considerable

532 electrical potential (positive inside the bilayer). (Figure generated using KcsA crystal

533 structure, PDB: 1K4C).

534

535

536 Figure 3: Rate at which new structures (α-helical and β-barrel) have been determined

537 since 1991. The count of membrane protein structures includes the same protein from

538 different organisms. The data suggests that there will be ~300 structures at the end of

539 2008. Data extracted from http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html

540

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