Interdiscip Sci Comput Life Sci (2014) 6: 1–12 DOI: 10.1007/s12539-014-0191-3

Efflux Pump-Mediated Antibiotics Resistance: Insights from Computational Structural Biology Nadine FISCHER† , Martin RAUNEST,

Thomas H. SCHMIDT,

Dennis C. KOCH, Christian KANDT∗

(Computational Structural Biology, Department of Life Science Informatics B-IT, Life & Medical Sciences Institute, University of Bonn, 53113 Bonn, Germany)

Received 20 January 2013 / Revised 3 September 2013 / Accepted 18 November 2013

Abstract: The continuous rise of bacterial resistance against formerly effective pharmaceuticals is a major challenge for biomedical research. Since the first computational studies published seven years ago the simulation-based investigation of antibiotics resistance mediated by multidrug efflux pumps of the resistance nodulation division (RND) protein super family has grown into a vivid field of research. Here we review the employment of molecular dynamics computer simulations to investigate RND efflux pumps focusing on our group’s recent contributions to this field studying questions of energy conversion and substrate transport in the inner membrane antiporter AcrB in Escherichia coli as well as access regulation and gating mechanism in the outer membrane efflux ducts TolC and OprM in E. coli and Pseudomonas aeruginosa. Key words: molecular dynamics simulation, AcrB, TolC, OprM, proton conduction, multidrug transport, access regulation, membrane protein.

1 Introduction 1.1

Efflux pump-mediated antibiotics resistance

The continuous increase of microbial resistance to antibiotics constitutes a serious health problem making a detailed understanding of the underlying molecular mechanisms and a paramount challenge for biomedical research (McDevitt and Rosenberg, 2001; Dougherty et al., 2002; Cohen, 2006). Since the initial successes in treating bacterial infections with antibiotics in the 1940s, bacterial strains have emerged displaying resistances against practically all of the commonly available agents (Nikaido, 2009). Moreover it is particularly alarming that according to the World Health Organization (WHO) (2012) today even in developed countries bacterial infections count again among the top five causes of death while at the same time the approval rates of new antibiotics have been declining continuously since the 1980s (Bandow and Metzler-Nolte, 2009; Wenzel et al., 2012). Every bacteria population already contains a usually small number of individual cells resistant to antibiotics. Constant application of such agents then wipes out the †

Current address: German Research School for Simulation Sciences GmbH, 52425 J¨ ulich, Germany. ∗ Corresponding author. E-mail: [email protected]

major part of the population that is not resistant to antibiotics, thus creating ideal growing and proliferation conditions for those cells not affected by the pharmaceuticals given. These bacteria will then form the basis of the new and now antibiotic-resistant strain (Fig. 1). Since old antibiotics lose their efficiency faster than new ones can be developed (Wax et al., 2008), a detailed understanding of the molecular basis of bacterial multidrug resistance is crucial.

Fig. 1

In a bacteria population the continuous application of antibiotics eventually creates ideal survival conditions for the initially small fraction of cells resistant to the drug given.

One way of action by which bacteria achieve antibiotics resistance is by preventing drug access to its target molecule (Nikaido, 2009), for example through an overproduction of multi-drug efflux pumps of the resistance nodulation division (RND) protein super family (Saier and Paulsen, 2001). Prominent examples of such RND transporters include AcrAB-TolC in E. coli (Ma et al., 1993; Zgurskaya and Nikaido, 1999a and 1999b) and

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Interdiscip Sci Comput Life Sci (2014) 6: 1–12

MexAB-OprM in P. aeruginosa (Akama et al., 2004; Sennhauser et al., 2009; Symmons et al., 2009; Xu et al., 2012), both exploiting proton motive force over the inner membrane to power the expulsion of a broad range of compounds from the periplasmic space out of the cell (Fig. 2). Drugs

ED

et al., 2005) efflux duct of the VceAB-C pump in Vibrio cholera, the structure of the assembled complex is unknown. However, structural models of the functional complex have been proposed differing essentially in the stochiometry and relative location of the adaptor protein and the amount of un-mediated antiporter – efflux duct interaction (Symmons et al., 2009; Tikhonova et al., 2011; Xu et al., 2011; Xu et al., 2012). 1.2

AP IMA DD PD

Drugs H+

H+

drugs

TMD

Fig. 2

In Gram-negative bacteria a major cause of antibiotics resistance is an overproduction of RND efflux pumps, membrane protein complexes comprising three different components (left): (1) an inner membrane (IM) proton drug antiporter (IMA) subdivided into a transmembrane (TMD) porter (PD) and docking domain (DD); (2) an outer membrane efflux duct (ED) through which substrates are transported across the peptidoglycan (PG) and the outer cell membrane (OM) containing lipopolysaccharides (LS) in the outer leaflet; and (3) an inner membrane-anchored adaptor protein (AP) coupling IMA and ED as well as enhancing pump activity. The right panel shows a structural model of an assembled IMA-ED-AP complex (Symmons et al., 2009) embedded in a double phospholipid membrane / water environment.

RND transporters comprise three different protein components transiently assembling into a functional complex where an inner membrane proton / drug antiporter (IMA) acts as the pump’s engine and active transport unit expelling substrates through an outer membrane efflux duct (ED) directly into the extracellular medium. Beyond stabilizing antiporter and efflux duct interaction, inner membrane-anchored adaptor proteins (AP) have been shown to have pump activity-enhancing properties (Zgurskaya and Nikaido, 1999b). Whereas at the time of writing crystal structures have been solved for the individual components of three different but structurally homologous RND efflux pumps, namely AcrAB-TolC (Koronakis et al., 2000; Murakami et al., 2002; Mikolosko et al., 2006; Seeger et al., 2006; Bavro et al., 2008; Symmons et al., 2009; Pei et al., 2010; Nakashima et al., 2011; Eicher et al., 2012) and CusBA-C (Su et al., 2009; Kulathila et al., 2011; Su et al., 2011) in Escherichia coli and MexAB-OprM (Akama et al., 2004; Sennhauser et al. 2009; Symmons et al., 2009; Phan et al., 2010) from Pseudomonas aeruginosa plus the X-ray structure of VceC (Federici

Computational structural biology

The scientific use of computational tools to gain biological understanding currently encompasses a large palette of applications ranging from supportive techniques in 3D structure determination (Larijani et al., 2006; Lottspeich and Engels, 2006; Rhodes, 2006) to entirely computational methods such as sequence and structure analysis, drug discovery, docking, protein structure prediction, homology modeling or biomolecular simulations (Leach, 2001; Frenkel and Smit, 2002; Schlick, 2002; Bourne and Weissig, 2003; Baxevanis and Ouellette, 2005; Taylor and Asz´ odi, 2005; Kukol, 2008; Nussinov and Schreiber, 2009; Voth, 2009; Bajorath, 2010). Most of the purely computational approaches share a common workflow: starting from experimentally determined data – sequence or 3D structure – a computational investigation is performed culminating in specific predictions that reiterate back into future experiments to increase the understanding of the, for example, protein in question (Fig. 3).

Experiment

Understanding Predictions

Fig. 3

Starting from experimental data, computational analysis and simulation can lead to an increased understanding of a protein in question resulting in specific predictions that can be checked in future experiments.

The field of biomolecular simulations encompasses a variety of techniques that can be classified in the two major branches of quantum mechanical (QM) and molecular mechanical (MM) simulations, differing essentially in the level of accuracy and approximation in describing molecular behavior. Whereas QM treats electrons explicitly focusing on the dynamics of the chemical bond, MM considers electrons only indirectly and the main focus is on the dynamics of entire atoms and molecules (Kandt and Monticelli, 2010). One of the most wide-spread MM methods in biomolecular simulations is molecular dynamics (MD) simulation. Us-

Interdiscip Sci Comput Life Sci (2014) 6: 1–12

ing Newton’s equations of motion MD simulation predicts the motions of a system of particles under the influence of internal (atomic interactions) and external forces (temperature, pressure and optional biasing forces in steered MD). Computing the forces acting on each particle in a system, e.g. atoms or larger entities in coarse grained approaches (Voth, 2009), MD can currently predict the dynamics of 105 -106 atoms biomolecular systems on a time scale of micro- to milliseconds (Sherwood et al., 2008; Marrink et al., 2009; Kandt and Monticelli, 2010; Shaw et al., 2010; Schmidt et al., 2012). In that way MD complements and extends the available structure determination methods by bringing back for a limited time the biologically fundamental element of motion that is usually lost during the structure determination process. Permitting to cast a glimpse on the dynamic behavior of e.g. a protein and its immediate micro-environment, MD simulations offer insights into molecular behavior at a level of detail not accessible by any experimental technique today. On the other hand, users should be aware that a full sampling of conformational space is currently only possible for small individual molecules whereas for large molecules like proteins only a partial sampling of conformational space near the starting structure can be achieved (Grossfield and Zuckerman, 2009). Like all computational biology approaches MD simulations ideally culminate in specific predictions such as mutagenesis candidates or distance changes directly testable by future wet lab experiments to further our understanding of the biomolecule investigated (Fig. 3). 1.3

Computer simulations of RND efflux transporters

With the first studies published seven years ago (Lu et al., 2006; Vaccaro et al., 2006) the computational investigation of RND efflux pump components is still a young field of research that is just beginning to gain momentum and will likely develop into a vivid area of investigation in the years to come. To date computational investigations have been reported for the AcrAB-TolC and MexAB-OprM efflux transporters, using a methodological spectrum including elastic network normal mode analyses (Lu et al., 2006; Phan et al., 2010), multiple basin simulations (Yao et al., 2010), data-driven docking (Lobedanz et al., 2007; Symmons et al., 2009), steered MD (Schulz and Kleinekath¨ ofer, 2009; Schulz et al., 2010; Schulz et al., 2011; Vargiu et al., 2011) as well as unbiased equilibrium MD simulations (Vaccaro et al., 2006; Vaccaro et al., 2008; Schulz and Kleinekath¨ ofer, 2009; Fischer and Kandt, 2011 and 2013; Collu et al., 2012; Feng et al., 2012; Raunest and Kandt, 2012; Wang et al., 2012; Koch et al., 2013). In this review we focus on our group’s contributions to the computational investigation of RND transporters using unbiased, atomistic MD simulations to study proton

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and substrate transport in AcrB as well as access regulation and gating mechanism in TolC and OprM. A more complete review covering all recent MD investigations of RND efflux pump components can be found in (Ruggerone et al., 2013).

2 The inner membrane proton / drug antiporter AcrB Discovered twenty years ago acriflavine resistance protein B (AcrB) is the proton / drug antiporter in the AcrAB-TolC RND efflux pump in E. coli, transporting a broad range of chemically diverse substrates (Nikaido, 1996 and 1998; Piddock, 2006). X-ray crystallography revealed that AcrB was a homotrimer where each monomer is trapped in a different conformation representing different reaction cycle intermediates termed “L” or “access” (monomer A), “T” or “binding” (monomer B) and “O” or “extrusion” (monomer C) (Murakami et al., 2006; Seeger et al., 2006; Sennhauser et al., 2007). Each monomer exhibits characteristic protonation states in the H+ -conducting transmembrane domain (TMD) and displays conformations resulting in different accessibilities of the substrate transportation pathways in the periplasmic porter domain (PD) from which substrates reach the outer membrane efflux duct TolC via the AcrB docking domain (DD) which extends furthest into periplasmic space. The atomistic MD simulations we performed on wild type AcrB in an explicit phospholipid / 150 mM NaCl environment aimed at contributing answers to two questions: how are protons transported through the TMD and how is substrate transported through the PD. 2.1

Proton conduction

In RND efflux pumps proton conduction through the TMD is the energy source powering the conformational changes necessary for drug expulsion. Whereas for other proton-conducting systems such as bacteriorhodopsin a highly detailed level of comprehension has been reached (Lanyi, 2004; Garczarek and Gerwert, 2006), the current level of our understanding of AcrB proton transport is still at the beginning: with Asp407, Asp408, Lys940, Thr978 and Arg971 only five TMD key residues have been identified impacting AcrB function by an activity loss of at least 90% when mutated to alanine (Guan and Nakae, 2001; Murakami et al., 2002; Murakami and Yamaguchi, 2003; Takatsuka and Nikaido, 2006; Seeger et al., 2009). Furthermore except for the positions of some water molecules reported last year (Eicher et al., 2012) the distribution of TMD-internal water is unknown, based on which direct conclusions could be drawn to possible proton conduction pathways (Abresch et al., 1998; Luecke, 2000; Svensson-Ek et al., 2002; Kandt et al., 2004; Grudinin et al., 2005; Kandt et al., 2005; Arkin et al., 2007; Pis-

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liakov et al., 2012). To predict the TMD-internal water distribution, gain new insights into AcrB proton conduction and identify new possible key residue candidates we sampled the dynamics of protein and internal water molecules on a 50 ns time scale in six independent and unbiased equilibrium MD simulations of full-length wild type, asymmetric AcrB embedded in a phospholipid membrane (Fischer and Kandt, 2011). Although the explicit simulation of proton transfer requires at least partially a quantum mechanical treatment (Aqvist and Warshel, 1993; Tuckerman et al., 1997; Schmitt and Voth, 1998; Lill and Helms, 2001; Warshel, 2003; Marx, 2006; Kamerlin and Warshel, 2010), classical MD simulation can nevertheless produce valuable insights into proton conduction, providing a quantitative description of hydration patterns and water dynamics inside the moving protein under physiologically relevant conditions (Kandt et al., 2004; Kandt et al., 2005; Arkin et al., 2007; Pisliakov et al., 2012). To predict TMD hydration we obtained dynamics samples of protein-internal water molecules (Fig. 4(a)) which we quantified as voxel-based residence probabilities (Fig. 4(b)). Analyzing the resulting mean water densities, we found that TMD hydration was monomerspecific and dynamic (Fig. 4(c)). Whereas periplasmic and cytoplasmic bulk water is transiently connected via a continuously hydrated and known key residueencompassing core region in monomer A and throughout the entire simulation time in monomer B, there is no bulk water access in monomer C. Further, with the exception of monomer C, TMD hydration is also asymmetric within each monomer with up to three water channels leading to the core region from periplasmic side but only a single water channels connecting to cytoplasmic bulk water. Correlating well with known point mutations and their impact on AcrB activity (Guan and Nakae, 2001; Takatsuka and Nikaido, 2006; Seeger et al., 2009), the simulation-derived water distributions suggest three alternative routes of proton transfer (Fig. 4(d)). Combining the information of average water distribution and residue water hydrogen bond interaction with conformational changes between the monomers, we identified three groups of new key residue candidates: a) framework residues lining the mean water densities and providing the scaffold for TMD water organization, b) four groups of gating residues regulating TMD bulk water access in a combination of side chain re-orientations and shifts of trans-membrane helices (Fig. 4(e)) and c) three negatively charged TMD surface residues which might act like proton antennas attracting water molecules to the mouths of two periplasmic water channels, located at or below the level of lipid head groups. With bulk water access present in the A and B states but not in C, our data suggest that proton uptake occurs either in a

Interdiscip Sci Comput Life Sci (2014) 6: 1–12

so far unknown intermediate between B and C or during the A or B state with the third excess proton transiently stored in a protonated water cluster until the C intermediate is reached. With direct bulk water contact in C, our findings support Arg971-H as likely proton release group candidate, deprotonating either in the C intermediate or during the transition to A. Providing hypotheses directly testable mutagenesis, activity assays or spectroscopy experiments, our simulations were the first MD study investigating AcrB proton transfer, using a methodological approach easily transferable to other RND efflux transporters or proton pumping proteins. 2.2

PD ground state dynamics and implications for substrate transport

Whereas energy conversion in AcrB takes place in the TMD, the PD is the main part of the protein where substrate recruitment and transport occur. The main drug transport pathway includes three major way points (Fig. 5(a)): (1) a PD entrance region (PDe) also known as “access” or “proximal binding pocket” found open in monomers A and B but closed in C in the crystal structures; (2) the “deep”, “distal” or “hydrophobic binding pocket” (HBP) located further inward the PD displaying an open conformation only in monomer B; (3) a PD exit region (PDx), found open only in C, from which substrates enter the DD-formed funnel before finally reaching the ED TolC (Nakashima et al., 2011; Eicher et al., 2012). Remarkably all 34 available AcrB X-ray structures solved the PD in nearly identical conformations within the same reaction cycle intermediate, displaying C RMSDs of less than 1 ˚ A when fitted to the 2GIF X-ray structure (Murakami et al., 2002; Murakami et al., 2006; Seeger et al., 2006; Sennhauser et al., 2007; Veesler et al., 2008; Nakashima et al., 2011; Eicher et al., 2012; Fischer and Kandt, 2013). To gain insights into the transport mechanism and obtain evidence whether the high level of PD conformational homogeneity represents an intrinsic feature of AcrB or stems from the crystallization conditions used, we computed 100 ns dynamics samples of the full-length wild type and membrane-embedded protein in a series of six independent and unbiased MD simulations (Fischer and Kandt, 2013). Using distance and radius of gyration analyses to monitor the PDe, PDx and HBP opening state in each monomer, we found that the PD is more flexible than previously assumed, displaying clear opening and closing motions of the PDe in the A and B state (Fig. 5(b)) as well as the PDx in the C intermediate (Fig. 5(d)) supporting the hypothesis that Gln125 and Tyr758 act as gating residues (Sennhauser et al., 2007). Concurrently in all simulations the HBP collapsed in monomer B resulting in predominantly closed HBP conformations in all three protomers (Fig. 5(c)). Comparing

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Water density (H2O/Å3) 0.03

(a)

0.56

(b)

(c)

(d) open

17-34 ns

34-50 ns

Monomer A

0-17 ns

closed E1

Monomer B

E2

E3

Monomer C

X

(e)

Fig. 4

(f)

Using the MD-derived dynamics of the full-length AcrB trimer and TMD-internal water molecules (a) we computed voxel-based spatial residence probabilities (b) to predict TMD hydration. The obtained water distributions indicated that TMD hydration is monomer-specific and dynamic (c) suggesting three alternative routes for proton conduction (d), connecting the known key residue-encompassing core region (CR) to bulk water through three periplasmic (E1-3) and one cytoplasmic water channel (X). In the simulations each water channel is regulated by four groups of gating residues (e). Adapted from (Fischer and Kandt, 2011), modified.

our protein conformations to AcrB X-ray structures, our findings suggested that the conformational homogeneity seen in the crystal structures is likely not an intrinsic feature of the protein but an artifact caused by bound but structurally unresolved buffer or detergent molecules implying that the actual structure of

substrate-free AcrB has not been solved yet. With the PDe and PDx adopting two distinctive conformational states within the same intermediate, the simulations further implied that each of the currently known three reaction cycle intermediates can occur in at least two variants. Moreover, the observation that

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Interdiscip Sci Comput Life Sci (2014) 6: 1–12

monomer A

monomer B DD

DD CFu

monomer A

X-ray

PC2

MD

PC1 PDe

PDx

PC2

PDe

PC2

PC1 PDe

TMD

monomer A

monomer C DD

DD CFu

monomer C

TMD

monomer B

PC1 HBP

PDe

PC1

PC2 PDe

PDx monomer A

(b) PC1

HBP

PC2 PDe

DD

PN2

PN1*

DD

X-ray

I277

I277

F615 V6

F178

F617

PN1

PC1 TMD

MD

F615

F136

F628 Y327

monomer B (c)

Fig. 5

PN2

I626 F610

F617

F628

Y327

DD

PN1*

F178

I626

V139

F136

DD

V612

F610

V139

monomer B

(a)

monomer C

TMD

TMD

PN1*

PN2 (d)

In AcrB substrate recruitment and transport takes place in the porter domain and includes three main way points: (1) the proximal binding pocket or porter domain entrance (PDe) found open in monomers A and B but closed in C in the AcrB X-ray structures; (2) the hydrophobic binding pocket (HBP) found open only in B; and (3) the porter domain exit (PDx) through which substrates reach the central funnel (CFu) formed by the AcrB docking domain (DD). Using unbiased MD 100 ns simulations of the full-length AcrB trimer we found the PDe opening and closing in A and B (b), the HBP adopting closed conformations in all monomers (c) and the PDx undergoing opening and closing motions in monomer C (d). Panel (c) adapted from (Fischer and Kandt, 2013), modified.

the monomer-specific orientation of the Thr676 loop connecting the PC1 and PC2 subdomains was decoupled from the relative subdomain orientations independently regulating PD access can be seen as piece of evidence suggesting that the Thr676 loop is likely playing a key role in substrate transport pushing compounds

towards the HBP. On a 100 ns time scale we observed no conformational trends supporting the hypothesis of a conformationally homogeneous AcrB resting state in the absence of substrate (Su et al., 2006) as the relative conformational distance between monomers increased in all simulations. If the observed PDe dynamics have

Interdiscip Sci Comput Life Sci (2014) 6: 1–12

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an inhibiting effect on AcrB pump activity by lowering the life time of substrate-accessible conformations, the in silico PDe opening and closing motions of the isolated protein could provide a structural explanation for the AcrB-enhancing effect of the adaptor protein AcrA (Zgurskaya and Nikaido, 1999b) through stabilization of substrate-accessible PD conformations. Providing predictions directly testable by activity measurements, cross-linking, mutagenesis or spectroscopy experiments, our MD simulations provided the most extensive sampling of AcrB ground state dynamics available so far, using a methodological approach easily transferable to other RND efflux transporters.

3 The outer membrane efflux ducts TolC and OprM Once recruited and translocated by the IMA, substrates leave the cell via the ED (Fig. 2). TolC and OprM are channel proteins in the outer membrane of E. coli and P. aeruginosa acting as efflux ducts in the AcrAB-TolC and MexAB-OprM RND efflux pumps. Resembling the shape of homotrimeric hollow cylinders, TolC and OprM occur in at least two conformational states: one permitting and one blocking the passage of substrates, depending on whether the ED is complexed with its corresponding inner membrane transporter partners (Gotoh et al., 1998; Zhao et al., 1998; Saier and Paulsen, 2001; Nikaido, 2011).

3.1

Gating and access regulation

The available TolC and OprM crystal structures (Koronakis et al., 2000; Akama et al., 2004; Higgins et al., 2004; Bavro et al., 2008; Pei et al., 2010; Phan et al., 2010) suggested access regulation on both ends of the channel with three inwardly oriented extracellular loops restricting channel access via the membrane-spanning ß-barrel. On periplasmic side the channel diameter is constricted by the dense packing of the tip regions of 12-helices where for TolC an inner (BNI) and outer periplasmic bottleneck (BNII) has been identified in this region (Fig. 6(a)). Displaying an overall similar architecture, OprM however lacks TolC’s double aspartate ring constituting BNI (Andersen et al., 2002a; Andersen et al., 2002b; Higgins et al., 2004; Bavro et al., 2008; Pei et al., 2010; Phan et al., 2010). While both OprM and TolC have to undergo opening and closing motions to fulfil the biological function, the details of the underlying gating mechanism are unknown. To gain insight into TolC and OprM access regulation we carried out series of independent, unbiased atomistic MD simulations, sampling membrane-embedded wild-type TolC and OprM dynamics on a 150-300 ns time scale in a 150 mM NaCl solution (Raunest and Kandt, 2012; Koch et al., 2013). Monitoring its extracellular loop conformations in a series of nine MD runs (Raunest and Kandt, 2012) we found TolC opening and closing freely on extracellular

ToIC-OprM

X-ray

MD

X-ray

MD

(b)

(d) ToIC Na+ Asp230 Asp171

BN I BN II (a)

Fig. 6

Asp374 Asp371 Thr368 Thr366

Na+ (c)

BNI BNII

Asp416 Val408 (e)

Na+

OprM Na+

(f)

Using unbiased 150-300 ns MD simulations of wild type TolC and OprM (a) (Raunest and Kandt, 2012; Koch et al., 2013) we found TolC opening and closing freely on extracellular side (b) while on periplasmic side the channel opened in the region of the outer periplasmic bottleneck BNII until the binding of sodium ions in the bottleneck area induced closure (c). Displaying a similar behavior, OprM opens and closes freely on extracellular side (d) whereas towards the periplasm the channel opens in the region of the outer periplasmic bottleneck BNII but remains in crystal structure-like closed conformations in the area of the inner periplasmic bottleneck BNI (e). For OprM too the simulations indicated a new sodium binding site whose location appears to be correlated with the distribution of closely neighbored pairs of negatively charged residues (highlighted) in the OprM and TolC crystal structures (f). Panels (a) and (f) adapted from (Koch et al., 2013), modified.

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side (Fig. 6(b)) suggesting the absence of a gating mechanism in the isolated protein. Moreover TolC’s general accessibility from the extracellular medium might indicate a new way of designing TolC-directed drugs, specifically targeting the channel interior. On periplasmic side we observed in all simulations TolC an opening of the BNII region until in one run the successive binding of two sodium ions preferably interacting with Asp371, Thr366, Thr368 and Asp153 induced closure (Fig. 6(c)). The resulting BNII conformation is more closed than any of the available crystal structures. Concurrent with a third site of heightened sodium residence probability at Asp374, TolC remains closed in the BNI region unless the removal of all NaCl from the system induces an opening response of BNI followed by a re-opening of BNII. Displaying a so far unreported high degree of conformational dynamics in the channel mouth regions, our findings suggested that TolC was locked only on periplasmic side in a sodiumdependent manner. Extending the sampling time of previous TolC simulations by a factor of 7.5-10 (Vaccaro et al., 2008; Schulz and Kleinekath¨ ofer, 2009), our TolC study yielded predictions directly testable by mutagenesis, fluorescence and electron spin resonance spectroscopy experiments. Using the same methodological and analytical approach of multi-copy MD simulations combined with dihedral angle, distance, ion Z-traces, 1D and 3D density analyses, we carried out an analogous study on OprM, exploring protein dynamics in five independent, unbiased 200 ns MD simulations (Koch et al., 2013). Similar to TolC we found OprM opening and closing freely on extracellular side (Fig. 6(d)), suggesting here too both the absence of a gating mechanism on this side in the isolated protein and the same potential of OprM-specific drug development. Assuming a similar BNI / BNII architecture as in its E. coli homologue TolC, we monitored OprM’s periplasmic opening state using Asp416 and Val408 which we selected based on their proximity to their TolC counterparts after superimposing OprM and TolC X-ray structures (Fig. 6(a)). Unlike TolC we observed in all simulations an opening of both bottlenecks (Fig. 6(e)). However, the effect is stronger pronounced in the outer bottleneck region reaching end conformations up to 3 times more open than the starting crystal structure. At the same time the inner bottleneck displayed postsimulation conformations only 1.1 to 1.4 times more open than in the starting structure. Assuming our simulations were correct our findings imply that similar to TolC OprM periplasmic gating occurs only in the inner bottleneck region at Asp416 and that in vivo additional components, absent in our simulation, might be

Interdiscip Sci Comput Life Sci (2014) 6: 1–12

required for periplasmic gating if the observed opening trend at Asp416 is not negligible. In addition to that we identified in each monomer a previously unreported sodium binding site in the channel interior coordinated by Asp171 and Asp230 (Fig. 6(e)). Apparently uninvolved in gating or structure stabilization the functional role of the Na site remains to be investigated. However inspecting the distribution of negatively charged residues in the ED interior suggested a pattern, as both the TolC and OprM Na binding occurred at pairs of closely neighbored aspartates (Fig. 6(f)). The first simulation study of OprM, our investigation provided new predictions directly testable via mutagenesis, fluorescence and electron spin resonance spectroscopy experiments.

4 Conclusions In this article we reviewed the application of molecular dynamics computer simulations to study RND efflux pump-mediated antibiotics resistance. Concentrating on our group’s recent contribution to this field we discussed simulations studies of three different RND transporter components focusing on proton conduction and porter domain dynamics in the inner membrane drug / proton antiporter as well ground state dynamics of the outer membrane efflux ducts TolC and OprM suggesting unilateral access regulation.

Acknowledgements This work was financially supported by the Ministerium f¨ ur Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen, Germany. ChK is a junior research group leader funded by the NRW R¨ uckkehrerprogramm.

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