Functionality Mapping on Internal Surfaces of Multidrug Transporter

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Functionality Mapping on Internal Surfaces of Multidrug Transporter AcrB Based on Molecular Theory of Solvation: Implications for Drug Efflux Pathway Takashi Imai,*,†,‡ Naoyuki Miyashita,†,‡ Yuji Sugita,†,§ Andriy Kovalenko,|| Fumio Hirata,^ and Akinori Kidera‡,# †

Quantitative Biology Center, RIKEN, Kobe, Hyogo 650-0047, Japan Computational Science Research Program and §Advanced Science Institute, RIKEN, Wako, Saitama 351-0112, Japan National Institute for Nanotechnology, National Research Council of Canada, and Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta T6G 2M9, Canada ^ Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, Okazaki, Aichi 444-8585, Japan # International Graduate School of Arts and Sciences, Yokohama City University, Tsurumi, Yokohama 230-0045, Japan

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‡

bS Supporting Information ABSTRACT: AcrB is a membrane protein and a multidrug efflux transporter. Although the recently solved X-ray crystal structures of AcrB provide a rough sketch of how drugs efflux, the pathway and mechanism have not been completely elucidated. In this study, a ligand-mapping method based on the 3DRISM molecular theory of solvation, which we recently developed, is applied to AcrB in order to identify the drug efflux pathway. We use a fragment-based approach as a strategy to map chemical functionality on the internal surfaces. A few “multifunctional” ligand-binding sites, which recognize various types of functional groups, are detected inside the porter domain. Spatial links between the multifunctional sites indicate a probable multidrug efflux pathway. The frustrated environment of the protein cavity constructed of weak interactions between ligand and protein may be a mechanism for allowing smooth transportation through the protein. Guided diffusion appears to be the main mechanism for efflux.

’ INTRODUCTION Multidrug transporters are proteins associated with drug resistance of bacteria,1 a cause of serious problems in medical practice. The transporters exist in the cell membrane of bacteria to expel extraneous drug molecules from the membrane to the outside of the cell. AcrB is one of the principal multidrug exporters expressed almost constitutively in Escherichia coli to confer intrinsic drug tolerance.2,3 AcrB takes up and exports a wide variety of toxic compounds, including different classes of antibiotics, dyes, detergents, bile salts, and even small organic molecules such as hexane.4,5 It cooperates with an outer membrane channel, TolC, which aids passage through the periplasm, and a membrane fusion protein, AcrA, which assists in linking AcrB and TolC,4 9 as illustrated in Figure 1. Recently, Murakami et al.10 succeeded in the determination of a crystal structure of trimeric AcrB under lower crystallographic symmetry than previously.11 In the new crystal structure, the three AcrB protomers have different local conformations, although the global 3-fold symmetry is maintained. Similar asymmetric AcrB structures in different space groups have also been solved by Pos’ group12 and Gr€utter’s group.13 r 2011 American Chemical Society

On the basis of the three distinct structures, a three-step “functionally rotating” mechanism has been proposed for the transport of drugs by AcrB.5,8 10,12 In this mechanism, each protomer undergoes a sequential structural change among access (A), binding (B), and extrusion (E) states along the drug transport cycle, i.e., ABE f BEA f EAB f ABE. In the access state, there is a tunnel connecting the exterior to an internal cavity in the porter domain. In the binding state, the internal cavity expands enough to accommodate drugs. Indeed, some substrates were detected in the cavity.10 In the extrusion state, the substrate binding pocket shrinks and opens toward an upper central pore leading to the outer membrane channel. Although these structures provide a rough sketch for the drug efflux mechanism based on a geometric arrangement of the internal cavities, the pathway has not been completely elucidated in terms of physicochemical interactions. Computational prediction of ligand binding sites, or so-called ligand mapping, is potentially an effective approach to identify Received: February 17, 2011 Revised: April 8, 2011 Published: April 28, 2011 8288

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Figure 1. Structure of AcrB embedded in a POPC membrane used in the calculations. The side view (upper) and top view (lower) are shown. The main chains of AcrB are illustrated by a cartoon representation: green, access; blue, binding; red, extrusion. The POPC atoms except hydrogen are represented by the translucent stick model in gray color. The van der Waals spheres show the modeled structure for the missing residues in the original PDB structure (2DHH). A schematic illustration of hypothetical drug efflux in AcrAB-TolC system is also provided.

the drug efflux pathway on the basis of ligand-protein interactions. There are, however, two practical difficulties in this approach for multidrug transporters. The first problem is how effective and appropriate is it to probe internal surfaces rather than the outside of the protein. Molecular mechanics and dynamics

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approaches are certainly useful for ligand mapping on the surface of small proteins,14,15 but such methods may be less effective for internal surfaces of large proteins because of sampling problems. Internal cavities generally have more complex shapes and occasionally are almost isolated from other cavities or the exterior, which means that the results can depend highly on initial conditions, especially the initial coordinates of ligand and water molecules. Ligand mapping using empirical score functions including an implicit solvent effect is another method that has often been adopted, as this has a low computational cost.16,17 However, such empirical approaches may be inappropriate for internal surfaces where each of the solvent water molecules can play a distinct role at the atomic level particularly in a confined space. In order to overcome these problems, we have employed a ligand mapping approach based on the three-dimensional reference interaction site model (3D-RISM-LM) that we developed recently.18 The 3D-RISM-LM method yields the most probable binding modes (MPBMs) of target ligands (drugs, small molecules, water all denoted as solvent) around a protein (solute) with a given structure, thus explicitly taking into account the effect of solvent water without any “simulation” or “sampling” but only by solving statistical mechanical equations with some reasonable approximations. This is a great advantage over molecular simulation methods as it is intrinsically free from solvent sampling problems. Our method using an explicit water model has a further distinct advantage over empirical approaches in that it deals with the effect of water at the atomic level in a reasonable physicochemical manner. Another practical problem concerns the diversity of potential ligands. As mentioned above, AcrB recognizes a wide variety of ligand molecules which may differ not only in physical properties (size and shape) but also in chemical properties or functionalities. While it is impractical to bring all of the various ligands into our calculation, if one selects only a limited number, the results not only lack generality but also ignore the multidrug efflux ability of AcrB. As an alternative strategy, we have adopted a fragment-based approach, which uses a series of drug fragments made up of different functional groups, instead of drug molecules themselves. In the general fragment-based approach, a few selected fragments with high binding affinity are linked together, merged into a common structure, or further elaborated with additional components, to construct drug compounds with higher affinity.19 However, in the present study, we utilize fragment MPBMs simply for mapping various kinds of chemical functionalities on the internal surfaces of AcrB. We then integrate them to find the properties allowing the multidrug transport in the cavities. In this paper, we describe fragment-based functionality mapping on the internal surfaces of AcrB using the 3D-RISM-LM method. We then discuss the pathway and molecular mechanism of multidrug efflux based on our functionality mapping.

’ COMPUTATIONAL METHODS Modeling of AcrB Embedded in a Membrane. AcrB embedded in a 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) membrane is treated as a “solute” in the present system. The three-dimensional (3D) atomic coordinates of AcrB were taken from the Protein Data Bank (PDB; code 2DHH).10 Missing residues in the intracellular loop region (residues 499 512 for each protomer, indicated by van der Waals spheres in Figure 1) were modeled using MODELLER.20 Protonation states of the 8289

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Figure 2. Organic-solvent molecules used as drug-fragment ligands in the calculations. They are categorized into four chemical functionality groups: aromatic, aliphatic (hydrophobic), hydrogen-bond donor, and hydrogen-bond acceptor. Note that all molecules in the hydrogen-bond donor group also have a hydrogen-bond acceptor site.

titratable residues were determined with an empirical method using PROPKA.21 Missing hydrogen atoms were added at the proper positions using the MMTSB toolset.22 The trimeric AcrB was embedded in a POPC bilayer membrane with a lateral size of 148.77 Å  148.77 Å according to the spatial arrangement predicted in the Orientations of Proteins in Membranes (OPM) database.23 The structure of POPC was taken from the lipid bilayer library in CHARMM-GUI.24 All crystallographic water molecules were removed. Energy minimization was performed in vacuum to eliminate unphysical overlaps and distortions in local structure, in which the modeled residues and the added hydrogen atoms were constrained at predicted positions, using the CHARMM 27 force field with j, ψ cross-term map (CMAP) correction.25,26 The same force-field parameters were employed for AcrB and POPC in the 3D-RISM calculations. Modeling of Drug Fragments. Sixteen organic-solvent molecules were prepared as drug-fragment ligands: benzene, pyridine, cyclohexane, piperidine, and tetrahydropyran, which represent six-membered ring frameworks frequently found in drug molecules,27 and isobutene, isopropanol, 2-methoxypropane, acetone, isopropylamine, trimethylamine, dimethyl sulfoxide (DMSO), acetic acid, methyl acetate, acetamide, and nitromethane, which correspond to typical side-chain moieties of drug molecules,28 attached to a few carbon atoms for termination. They are categorized, in Figure 2, into four chemical functionality groups: aromatic, aliphatic (hydrophobic), hydrogen-bond donor, and hydrogen-bond acceptor. The united-atom optimized potentials for liquid simulation (OPLS-UA) force field was employed to characterize the ligands.29 38 (Note that polar and aromatic hydrogens are represented explicitly, while nonpolar aliphatic hydrogens are embedded in the connecting carbon in the latest OPLS-UA model.) 3D-RISM-LM Calculation. In the 3D-RISM-LM procedure, the 3D-spatial distribution functions (DFs) of ligand atomic sites are first calculated for each ligand using 3D-RISM theory.39 41 The MPBMs of the ligand are then obtained from the 3D-DFs with a grid search algorithm.18 A general description of 3D-RISM theory has been reported previously,39 41 and is also provided in the Supporting

Information. A brief outline of the calculations is presented here. First, the solvent site site correlation functions in 1 mM aqueous solution of each ligand are calculated using the RISM theory coupled with the Kovalenko Hirata (KH) closure approximation,42 starting with the site site intermolecular potentials, the solvent densities, and the temperature (T = 300 K). The transferable intermolecular potential 3P (TIP3P) model43 is employed for water, using a correction with respect to the Lennard-Jones parameters (σ = 0.4 Å and ε = 0.05 kcal/mol) of the hydrogen sites.44 The solvent site DFs are then obtained on a 3D grid around the “solute” (AcrB embedded in POPC membrane, as described above) by solving the 3D-RISM/KH equations, using the solvent site site correlation functions and the potential functions between solute and solvents. The 3D-RISM/ KH equations are solved on a 3D uniform rectangular grid of 300  300  300 points in a cubic supercell of 148.77  148.77  148.77 Å3 under the periodic boundary condition. The longrange electrostatic asymptotes are separated and treated analytically, including special corrections for 3D supercell finiteness.45 The theoretical background of our ligand mapping method is described in the Supporting Information as well as in our previous paper.18 The mapping procedure in the present study is presented below. First, for each ligand, the binding modes whose site-integrated potentials of mean force (SI-PMFs) are lower than a threshold value, which are referred to as highly probable binding modes (HPBMs), are derived using the grid search algorithm. (This calculation is similar to conventional ligand docking if a “docking score” is used instead of SI-PMF.) The SI-PMF is calculated from the 3D-DFs of the ligand sites and so corresponds approximately to the binding free energy. (Note that aromatic hydrogens are not included in the calculation of SI-PMF.) The threshold is determined with respect to each ligand, so that 30 MPBMs were finally obtained for each ligand in the internal cavity of the porter domain (see below). Second, the MPBM is extracted from each cluster composed of highly overlapping HPBMs as follows. The HPBMs are ranked according to their SI-PMF values. Binding modes are then picked sequentially and subjected to the following selection: a binding mode that “overlaps” with a higher ranked mode is eliminated 8290

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from the rank list. An “overlap” occurs when the root-meansquare deviation is less than 2 Å. The binding modes that finally remain in the list are identified as MPBMs. The MPBMs not located in the interior of the porter domain were removed for clear representation. In the final result, we have the top 30 MPBMs for each ligand on the internal surfaces of the porter domain of AcrB. In the grid calculation, the same translational grid interval is used as in the 3D-RISM calculation. The rotational grid is discretized with an interval of 15 for Euler angles j and θ; ψ is simply scaled by sin θ (i.e., Δψ = 15/sin θ) in order to avoid oversampling along the z-axis.

’ RESULTS AND DISCUSSION Functionality Mapping. The top 30 MPBMs for each fragment on the internal surfaces of the AcrB porter domain in the access, binding, and extrusion forms are shown in Figure 3, where they are color-coded according to chemical functionality: green, aromatic; yellow, aliphatic (hydrophobic); blue, hydrogen-bond donor; red, hydrogen-bond acceptor. An overall feature is that the MPBMs are rather widely distributed in the internal cavities, and MPBMs with different functionalities are generally located at different binding sites, as can be seen from the mosaic color distribution of MPBMs in Figure 3. This binding characteristic is apparently different from that obtained for “ordinary” substrate receptor systems (i.e., receptors that recognize a specific class of substrates or drugs), in which only a limited number of MPBMs are detected in restricted regions corresponding to the specific substrate-binding sites.18,46 Different distributions of fragment MPBMs are found in the different structural states of the protomer. The differences are primarily caused by structural modifications of the internal cavities, which have already been discussed in detail in previous papers.5,8 10,12,13 However, fragment MPBMs not only detect the structural changes of the cavities but, at the same time, sensitively probe changes in chemical properties on the internal surfaces. One of the most significant findings in our functionality mapping is that there is no stable binding site that is stable in all three structural states. This means that a drug avoids being trapped in one location through site specific interactions. Such a characteristic of a channel is probably essential for the passage of the drug through the protein and its efflux. It is not feasible to describe all the structural and chemical properties around all the fragment MPBMs. We rather focus on a limited number of what we consider the “most significant” binding sites. Thus, there are some clusters consisting of MPBMs with different functionalities. Among such clusters, those that include all four kinds of functionalities may be particularly significant based on the following reasoning. In general, a wide variety of drug compounds are likely to be transported through multiple efflux pathways, each specific to a particular class of drugs. The pathways could form a network inside the transporter and there may be hubs where several pathways cross. The network hubs should have a multifunctional nature in order to act as an intersection of pathways for different functionalities and should be the most significant sites of the efflux pathway network. On the basis of this idea, we focus on MPBM clusters with all four kinds of functionalities (i.e., four different colors in Figure 3), which are hereafter referred to as multifunctional sites (MFS). Multifunctional Sites Detected. An MFS is found in the lower part of the porter domain in the access protomer, and is labeled MFS-1 (Figure 3A). As shown in Figure 4A, there is an

Figure 3. Top 30 MPBMs for each fragment ligand on the internal surfaces of the AcrB porter domain. The MPBMs are represented by van der Waals spheres: green, aromatic ligand; yellow, aliphatic; blue, hydrogen-bond donor; red, hydrogen-bond acceptor. The AcrB molecule is illustrated by the surface or transparent stick representation; green, access; blue, binding; red, extrusion. (A) The MPBMs in the access protomer. (B) MPBMs in the binding protomer. (C) MPBMs in the extrusion protomer.

access route from the outside of the protein to MFS-1, implying that drugs can readily penetrate down to MFS-1 during the access state. Two MFSs are found in the lower and middle parts of the binding-form porter domain, and are labeled MFS-2 and MFS-3, respectively (Figure 3B). MFS-2 and MFS-3 are not sterically 8291

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Figure 4. Another view of the MPBMs for positional identification of the MFSs. The MPBMs at MFSs are colored in red and the others in yellow. The AcrB molecule is illustrated by the surface or transparent stick representation: green, access; blue, binding; red, extrusion. (A) Access protomer viewed from the rear to show the access route to MFS-1. (B) Binding protomer viewed from the rear. A near-side part is clipped to expose the largest internal cavity indicated by the white dashed line. (C) Extrusion protomer viewed from the inside. Only the MPBMs at the position corresponding to MFS-3 in the binding state (ex-MFS-3) are displayed.

separated from each other because the sites are located within a single large cavity, as illustrated in Figure 4B. MFS-2 is rather close to MFS-1, while MFS-3 is located in the vicinity of the

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upper central pore. No MFS is detected in the extrusion state (Figure 3C). The binding site corresponding to MFS-3 in the binding state (ex-MFS-3) is no longer multifunctional in the extrusion state, but opens toward the upper central pore (Figure 4C). Spatial connections between the MFSs provide a probable multidrug efflux pathway as follows. First, a drug passes through the tunnel indicated in Figure 4A to reach MFS-1 during the access state. Then, the structural transition from access to binding forces the drug to MFS-2, which is followed by the transfer to MFS-3. Finally, the drug is released from MFS-3 toward the upper channel when the binding-to-extrusion structural transition occurs. Chemical and Steric Environments around the MFSs. In order to clarify the mechanism of drug efflux in more detail, we investigated the chemical and steric environments around the MFSs and the changes associated with the structural transitions between the access, binding, and extrusion states, which are shown in Figure 5. In the access state, the MPBMs at MFS-1 have no specific interactions with surrounding protein residues, except for a hydrogen bond between the OH group of isopropanol and the side-chain NH group of Q34 (Figure 5A). The other potential hydrogen-bond donors, namely, the side-chain OH groups of S113 and S115 and the side-chain NH group of Q569, are not directly involved in the stabilization of the fragment binding modes. On the other hand, this pocket does not have a strongly hydrophobic character: no aromatic side-chain but some nonpolar moieties constitute the cavity wall. All four kinds of fragments are stabilized in the weakly polar and weakly hydrophobic environment. The binding pocket shrinks in both binding and extrusion states to such a size that it cannot accommodate even small fragments (Figure 5B,C). There are also no specific interactions between the MPBMs at MFS-2 and the surrounding protein residues in the binding state (Figure 5E). Neither a hydrogen-bond donor nor an acceptor is found in the pocket, but some aromatic side-chains (F178, Y327, F610, and F628) and a few nonpolar moieties surround it. Although this binding pocket has a hydrophobic character, it accommodates polar fragments as well as aromatic and hydrophobic ones. The MPBMs disappear in both access and extrusion states again due to steric hindrance (Figure 5D,F). MPBMs for all 16 varieties of fragments are detected in MFS-3 in the binding state (Figure 5H). Some of the MPBMs of the hydrogen-bond acceptor fragments make hydrogen bonds with the main-chain NH groups of Y275, Q218 (of the access protomer), and T222 (of access), the side-chain NH2 group of R185, and the side-chain OH group of Y772, whereas the other MPBMs of the hydrogen-bond acceptor and donor fragments have no specific interactions with protein residues. The MPBMs of aromatic and aliphatic fragments also do not have specific interactions, except for some pyridine MPBMs, which are stabilized by hydrogen bonds. In the corresponding part of the access protomer (Figure 5D), the pocket is slightly smaller and isolated from other cavities. Most of the MPBMs found in the confined space are ring fragments, but any aromatic fragment is not detected. In the extrusion state (Figure 5I), the pocket is much smaller and opens toward the upper central pore (the posterior; see also Figure 4C). MPBMs for only a few varieties of polar fragments, namely, piperidine, methyl acetate, and acetamide, are found here. Two of the acetamide MPBMs have hydrogen bonds with the main-chain NH groups of Y275 and Q218 (in the binding protomer), respectively. 8292

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Figure 5. Local structural changes around the MFSs associated with the structural transition among the access, binding, and extrusion states. The MPBMs of ligands and the proximal residues of the protein are depicted by the stick representation: green (for ligand) or gray (for protein), carbon; red, oxygen; blue, nitrogen; white, hydrogen. Yellow dashed lines indicate a hydrogen bond between ligand and protein atoms. (A) The MPBMs at MFS-1 and the residues within 5 Å of them in the access protomer. (B) The same residues as in panel A in the binding protomer. (C) The same residues as in panel A in the extrusion protomer. (D) The same residues as in panel E in the access protomer. (E) The MPBMs at MFS-2 and the residues within 5 Å of them in the binding protomer. (F) The same residues as in panel E in the extrusion protomer. (G) The same residues as in panel H in the access protomer, and the MPBMs surrounded by them. (H) The MPBMs at MFS-3 and the residues within 5 Å of them in the binding protomer. (I) The same residues as in panel H in the extrusion protomer, and the MPBMs surrounded by them. The viewpoints in the first column (access) are the same as in Figure 3A; the second (binding) are as those in Figure 3B; the third (extrusion) are as those in Figure 3C.

Hence, fragment MPBMs in the MFSs are not generally stabilized by specific interactions such as hydrogen bonds, but are maintained there by a complicated free-energy balance originating in the weakly polar and weakly hydrophobic surroundings. A MFS in one structural state is destroyed in another state simply by steric hindrance rather than by a possible rearrangement of chemical interactions. This implies that drugs are incorporated into the protein and transported merely by diffusion rather than through particular attractive interactions. Diffusion is regulated by spatial changes to the internal cavities, which are coupled to the conformational state transitions. This picture is consistent with coarse-grained molecular simulations reported recently.47 The spatial arrangement of functional sites that provides the frustrated environment of the protein cavity is a

mechanism for realizing smooth transportation without strong retention at particular positions. Comparison with Experimentally-Postulated Modes. Geometrical analyses of X-ray crystal structures have suggested that there are three possible routes for drugs to enter the internal binding pocket.5,8 10,12,13 One is the access tunnel found in the access protomer; the other two are in the binding protomer. The first one corresponds with the access route in the initial step of our model described above (i.e., the tunnel indicated in Figure 4A). One of the tunnels in the binding protomer is at a position similar to the access route in the access protomer, linking the outside periplasmic region to the largest internal cavity shown in Figure 4B. The other tunnel in the binding protomer extends down to the membrane region, linking it to the 8293

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Figure 6. Comparison between the experimentally detected ligandbinding sites (side-chain atoms in green) and the theoretically predicted MPBMs of fragments (the MFSs in red and the others in yellow) within the binding pocket of the binding protomer. The main chains of the binding protomer are illustrated in a blue transparent cartoon representation.

internal cavity. Our results, however, show that both of these routes do not directly connect to MFS-2, but to another site, the left lower MPBM cluster in Figure 4B. The latter location is not an MFS, but is nearly multifunctional (for example, would be through the introduction of an aromatic functionality). Hence we suggest that the tunnel in the access protomer is the primary access route, and those in the binding protomer may be secondary ones possibly used for more qualified classes of drugs. The 3D structures of two drug molecules accommodated by AcrB have been modeled from the rough electronic density maps obtained in the internal cavity of the binding protomer.10 The models indicate that one substrate interacts with residues F178, N274, and F615, and the other interacts with Q176, F615, and F617. Figure 6 illustrates a comparison of the experimentally detected binding sites with the theoretical MPBMs within the cavity (in similar view to Figure 4B). The experimental binding sites are located between the two theoretical MFSs. There is no MPBM interacting with the experimentally detected residues. (Note that “no MPBM” never means zero probability of ligand binding, but indicates binding affinity less than the criterion level.) At this stage, the apparent discrepancy cannot be entirely resolved, but another computational study recently done by Takatsuka et al.48 provides a hint of an explanation. In their study, they used a conventional docking program to determine the top five binding modes of several drugs and fragment compounds in the internal cavity of the binding protomer. Many of them were predicted to bind to a narrow groove in the upper part of the cavity, whereas some others preferred to bind to a wide cave in the lower part of the cavity. In fact, many of the

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binding modes of groove-binders overlap with the experimentally postulated binding modes, and some others stick out toward MFS-3. On the other hand, some cave-binders are found in the vicinity of MFS-2. The comparison implies that a wide stretch of cavity surface, from MFS-2 to MFS-3, can provide at least transient binding sites during the transport of drugs. It should, however, be noted that of the three approaches, only our treatment includes entropic effects of the solvents (ligand and water) at room temperature, since the crystallographic analysis was performed at 100 K and the docking was based on an “energy calculation.” Our result, therefore, suggests that the binding sites in between MFS-2 and MFS-3 detected by the other methods may be energetically favorable under certain conditions but unstable under more physiological conditions once entropy is taken into consideration. Our model permits drugs to be transported through the protein without being trapped in the region from MFS-2 to MFS-3. Model Dependence. In this study, we used the available X-ray crystal structure basically without further refinement. In general, even a local orientational change of a side chain can significantly affect the stability of ligand binding. However, the fragmentbased approach adopted in this study minimizes such configurational effects. As a simple example, consider a ligand molecule with two interaction domains of phenyl and carbonyl groups stabilized by aromatic and hydroxyl groups of the protein, respectively. When the protein hydroxyl slightly changes orientation, the ligand-binding affinity is largely reduced because of inconsistency between the binding modes of the two domains where the whole molecule is used as a probe. On the other hand, in the fragment-based approach, each of two fragments (for example, a combination of benzene and acetone) can be independently reorientated to optimize each binding mode. Because of such versatility, further improvement in local conformation modeling would not significantly affect the present results. In future work, we will consider the possibility of larger structural fluctuations than local side chain rearrangements by using molecular dynamics simulation to refine the functionality mapping. More extensive structural fluctuations may occur even in each conformational state. One involving a change in cavity size could have a substantial effect on fragment mapping.

’ CONCLUSIONS In this study, we have performed functionality mapping on the internal surfaces of AcrB in an application of the fragment-based approach using 3D-RISM-LM, our recently proposed ligandmapping method. The functionality mapping based on physicochemical interactions suggests a more particular molecular mechanism of multidrug transport in AcrB than that derived from geometrical cavity analysis. The mapping revealed MFSs inside the porter domain, and their location in association with a particular protomer conformation indicates a probable multidrug efflux pathway: (1) a drug would approach MFS-1 through a tunnel during the access state, (2) then be squeezed to MFS-2 and transferred to MFS-3 during the binding state, and (3) be released from MFS-3 toward the upper channel during the extrusion state. The primary mechanism of drug efflux appears to be diffusion through frustrated environment constructed of weak interactions rather than a sequence of well-designed stronger attractive interactions. The direction of diffusion is regulated by spatial changes in the internal cavities accompanying conformational state transitions. 8294

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The Journal of Physical Chemistry B In addition to the contribution to understanding of the molecular mechanism of multidrug transport in AcrB, the present study provides a good example of the practical use of 3DRISM-LM in a fragment-based approach. Similar approaches can be applied to many other significant targets including substratebinding pocket search (especially for undefined binding sites) and fragment-based lead discovery.

’ ASSOCIATED CONTENT

bS

Supporting Information. Detailed descriptions of the 3D-RISM theory and 3D-RISM-LM method, and a schematic illustration of the mapping procedure (Figure S1). This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected].

’ ACKNOWLEDGMENT We thank Prof. Satoshi Murakami for unpublished information and valuable comments. We are also grateful to Dr. Tsutomu Yamane, Dr. Xin-Qiu Yao, Prof. Mitsunori Ikeguchi, and Prof. Shoji Takada for fruitful discussions. This work was supported by Research and Development of the Next-Generation Integrated Simulation of Living Matter, a part of the Development and Use of the Next-Generation Supercomputer Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. ’ REFERENCES (1) Li, X.-Z.; Nikaido, H. Drugs 2009, 69, 1555. (2) Ma, D.; Cook, D. N.; Hearst, J. E.; Nikaido, H. Trends Microbiol. 1994, 2, 489. (3) Nishino, K.; Yamaguchi, A. J. Bacteriol. 2001, 183, 5803. (4) Nikaido, H. J. Bacteriol. 1996, 178, 5853. (5) Seeger, M. A.; Diederichs, K.; Eicher, T.; Brandst€atter, L.; Schiefner, A.; Verrey, F.; Pos, K. M. Curr. Drug Targets 2008, 9, 729. (6) Tikhonova, E. B.; Zgurskaya, H. I. J. Biol. Chem. 2004, 279, 32116. (7) Eswaran, J.; Koronakis, E.; Higgins, M. K.; Hughes, C.; Koronakis, V. Curr. Opin. Struct. Biol. 2004, 14, 741. (8) Murakami, S. Curr. Opin. Struct. Biol. 2008, 18, 459. (9) Pos, K. M. Biochim. Biophys. Acta 2009, 1794, 782. (10) Murakami, S.; Nakashima, R.; Yamashita, E.; Matsumoto, T.; Yamaguchi, A. Nature 2006, 443, 173. (11) Murakami, S.; Nakashima, R.; Yamashita, E.; Yamaguchi, A. Nature 2002, 419, 587. (12) Seeger, M. A.; Schiefner, A.; Eicher, T.; Verrey, F.; Diederichs, K.; Pos, K. M. Science 2006, 313, 1295. (13) Sennhauser, G.; Amstutz, P.; Briand, C.; Storchenegger, O.; Gr€utter, M. G. PLoS Biol. 2007, 5, e7. (14) Miranker, A.; Karplus, M. Proteins: Struct., Funct., Genet. 1991, 11, 29. (15) Seco, J.; Luque, F.; Barril, X. J. Med. Chem. 2009, 52, 2363. (16) Goodford, P. J. J. Med. Chem. 1984, 28, 849. (17) Brenke, R.; Kozakov, D.; Chuang, G.-Y.; Beglov, D.; Hall, D.; Landon, M. R.; Mattos, C.; Vajda, S. Bioinformatics 2009, 25, 621. (18) Imai, T.; Oda, K.; Kovalenko, A.; Hirata, F.; Kidera, A. J. Am. Chem. Soc. 2009, 131, 12430. (19) Rees, D. C.; Congreve, M.; Murray, C., W.; Carr, R. Nat. Rev. Drug Discovery 2004, 3, 660. (20) Sali, A.; Blundell, T. L. J. Mol. Biol. 1993, 234, 779.

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