Probing the Pharmacological Binding Sites of P-Glycoprotein Using

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Computational Biochemistry

Probing the Pharmacological Binding Sites of PGlycoprotein Using Umbrella Sampling Simulations Nandhitha Subramanian, Alexandra Schumann-Gillett, Alan Edward Mark, and Megan L O'Mara J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00624 • Publication Date (Web): 12 Dec 2018 Downloaded from http://pubs.acs.org on December 17, 2018

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Journal of Chemical Information and Modeling

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Probing the Pharmacological Binding Sites of P-Glycoprotein using

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Umbrella Sampling Simulations

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Nandhitha Subramanian†,¥, Alexandra Schumann-Gillett¥, Alan E. Mark†, §, Megan L. O’Mara*, †, ¥

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†School

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4072, Australia.

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¥Research

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Australia.

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§The

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Australia.

of Chemistry and Molecular Biosciences (SCMB), University of Queensland, Brisbane, QLD,

School of Chemistry (RSC), Australian National University, Canberra, ACT, 2601,

Institute for Molecular Biosciences (IMB), University of Queensland, Brisbane, QLD, 4072,

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* Corresponding author

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Email: [email protected]

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Keywords: P-glycoprotein (P-gp), Multidrug resistance (MDR), ABC transporter, potential of mean

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force (PMF), molecular dynamics, inhibitor, substrate binding, Hoechst 33342, Rhodamine 123,

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paclitaxel, tariquidar, verapamil

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ABBREVIATIONS

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P-gp, P-glycoprotein; ABC, ATP Binding Cassette; TMD, Transmembrane Domain; TM,

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Transmembrane; NBD, Nucleotide Binding Domain; MD, Molecular Dynamics; ATB, Automated

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Topology Builder; PMF, Potential of Mean Force; ICL, intracellular loops; PDB, Protein Data Bank.

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ABSTRACT

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The human multidrug transporter P-glycoprotein (P-gp) transports over 200 chemically diverse

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substrates, influencing their bioavailability and tissue distribution. Pharmacological studies have

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identified both competitive and non-competitive P-gp substrates, but neither the precise location of the

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substrate binding sites, nor the basis of competitive and non-competitive interactions has been fully

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characterized. Here, potential of mean force (PMF) calculations are used to identify the transport-

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competent minimum free energy binding locations of five compounds, Hoechst 33342, Rhodamine

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123, paclitaxel, tariquidar and verapamil to P-gp. Unrestrained molecular dynamics simulations were

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also performed to confirm the substrates were stable in the energy wells determined using the PMF

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calculations. All compounds had energy minima within the P-gp transmembrane (TM) pore. For

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Hoechst 33342 and Rhodamine 123, a second minimum outside the TM pore was also identified. Based

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on this and previous studies of nicardipine and morphine1, a general scheme that accounts for the

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observed non-competitive and competitive substrate interactions with P-gp is proposed.


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INTRODUCTION

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The multidrug efflux pump P-glycoprotein (P-gp) uses the energy derived from ATP binding and

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hydrolysis to power substrate transport out of the cell. P-gp is an integral membrane protein composed

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of two half-transporters, each containing a transmembrane domain (TMD) and a nucleotide binding

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domain (NBD). Substrate recognition and transport occurs in the TMDs, while the two NBDs dimerize

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to bind and hydrolyze ATP, powering large-scale conformational changes in the TMDs that facilitate

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transport. During substrate uptake, the TMDs are believed to adopt an inward-facing conformation

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where the TM pore is open to the cytosol. Substrate efflux is associated with the TMDs adopting an

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outward-facing conformation with the TM pore open to the extracellular side of the membrane.

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P-gp is expressed in the apical cells of barrier tissues, including the gastrointestinal tract, renal

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proximal tubules and blood-brain barrier. A key characteristic of P-gp is its broad substrate specificity.

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P-gp can efflux more than 200 chemically diverse substrate molecules, ranging from sterols, bioactive

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lipids (PAF)2 and signaling molecules, to pharmaceuticals such as opiates,3 statins,4, 5 antiepileptics,6, 7

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antiretroviral agents8 and cancer chemotherapeutics.9,

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pharmacokinetics of both therapeutic drugs and endogenous substrates, altering their absorption,

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distribution, metabolism and excretion (ADME). Furthermore, as many compounds compete for

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binding and transport by P-gp, administration of a second pharmaceutical product can have profound

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and non-additive effects on ADME. This is a particular problem in the treatment of cancers, where the

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expression of P-gp reduces the cellular accumulation of chemotherapeutics and confers multidrug

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resistance.11, 12

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Numerous biochemical and pharmacological studies have attempted to characterize the mechanism by

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which P-gp recognizes such a diverse range of substrates.13-30 However, the molecular basis of

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substrate promiscuity of P-gp remains poorly understood. Most P-gp transport substrates are primarily

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hydrophobic or amphipathic in nature, and contain one or more aromatic or cyclic groups.13, 31-34 They

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As such, it plays an important role in the


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also contain at least one positively charged group, most commonly a tertiary amine that is protonated at

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neutral pH, and thus, carry a net positive charge at physiological pH. Being predominantly

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hydrophobic, these molecules also interact with lipid bilayers. It is widely accepted that P-gp mediates

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substrate transport via a “hydrophobic vacuum cleaner” model,32 whereby substrates partition into the

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cell membrane before entering the P-gp transmembrane domain (TMD) translocation pore prior to

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efflux from the cell.35, 36

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In the case of membrane transport proteins, where substrates permeate through a protein cavity that

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spans the width of the membrane, defining a specific substrate binding site is difficult. In the case of P-

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gp, which has such broad substrate specificity, identifying a specific binding site for any individual

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transport substrate is even more challenging. Extensive mutagenesis studies have identified sets of

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residues dispersed throughout the TMDs, nucleotide binding domains (NBDs) and the connecting

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intracellular loops (ICLs) of P-gp that affect the binding and transport of various substrates.14-21, 23, 37-40

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Often, the same residues have been implicated in the binding and/or transport of non-competitive

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substrates.15, 17, 18, 37, 38 Taken together, these studies suggest that P-gp contains a large, non-specific

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binding pocket.16, 21, 41

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In contrast, competitive binding and classical Michaelis-Menten kinetic studies, suggest that P-gp

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contains multiple specific pharmacological binding sites, which interact with different classes of

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substrates in both a competitive and non-competitive manner. In Michaelis-Menten kinetics,

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competitive substrates (or inhibitors) are assumed to bind at the same binding site, while non-

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competitive substrates or inhibitors are assumed to bind to spatially distinct sites. For example,

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using fluorescence spectroscopy and kinetic studies of substrate transport, Shapiro et al.27, 28 proposed

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that Hoechst 33342, quercetrin and colchicine bind competitively to P-gp at a site referred to as the H-

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site or Hoechst site, whereas Rhodamine 123 and anthracyclines such as daunorubicin and doxorubicin

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bind competitively to a site called the R site.28 In addition, Shapiro et al.27 proposed that P-gp has a ACS Paragon Plus Environment

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regulatory site that binds both prazosin and progesterone.27 Competitive binding studies by others have led to the proposal that verapamil, vinblastine and morphine may all interact with the same site in P-gp, referred to as the vinblastine site.3,

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Based on radio-ligand and equilibrium binding studies of

vinblastine, paclitaxel, nicardipine, verapamil, Rhodamine 123, benzamidine, tariquidar, elacridar and XR9051, Martin et al.22 proposed that vinblastine binds to a site distinct from that of tariquidar, elacridar, XR9051, nicardipine, paclitaxel, Rhodamine 123 and Hoechst 33342. In the same study, Martin et al.22 also showed that Hoechst 33342, tariquidar and elacridar compete for binding to P-gp, suggesting that the three compounds could bind to the same site, namely the H-site. A detailed list describing the competitive and non-competitive interactions of a range of P-gp substrates, inhibitors and modulators is given in Table 1. In each case, these sites were defined in terms of a pharmacological response. These pharmacological sites do not necessarily correspond to distinct physical locations on the protein. In fact, Subramanian et al.1 showed that mapping the residues implicated in substrate binding and transport onto the crystal structures of mouse P-gp42, 43 does not yield clear, well-defined binding locations for any of the P-gp substrates examined. This has also been observed experimentally: Mittra et al. identified a three spatially distinct, substrate-specific “contact residues” at the lipid/protein interface that influence the transport of vinblastine, nicardipine and paclitaxel, but could not identify well-defined binding sites.44 Identifying the physical location of potential substrate binding sites in P-gp has been the goal of many computational investigations, including molecular docking, pharmacophore mapping studies and molecular dynamics (MD) simulations.45-53 A range of studies utilizing short-timescale MD simulations such as those by Klepsch et al.,48 Chufan et al.,49, 50 Jara et al.,51 Zhang et al.,52 Ma and Biggins53, 54 and Ferreira et al.45, 55, 56 have shown that P-gp substrates and inhibitors placed within the TM pore can interact with a variety of aromatic and hydrophobic residues, in effect binding weakly to multiple locations. Barreto-Ojeda et al. recently reported the spontaneous entry of lipids (POPC and POPE) into


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the TM pore via the substrate uptake portals during multiple 20 s coarse-grained simulations.57 Despite these advances, the spontaneous entry of therapeutic substrates into the TM pore has not yet been observed in non-biased simulations.1, 55, 56 In contrast, MD simulations using umbrella sampling techniques to steer the substrates through the TM pore have identified free energy minima corresponding to potential binding locations of two P-gp substrates, morphine and nicardipine.1 Other studies identified the minimum energy uptake pathway of colchicine and tariquidar from the membrane into the TM pore, but did not conclusively identify binding locations within the TM pore.56 One aspect common to the atomistic simulations reported is the intrinsic conformational flexibility of P-gp,58 which could play a key role in its ability to bind and transport such a wide range of chemically diverse substrates.

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This conformational flexibility has also been observed in atomic force microscopy

analysis, 60 and in the structural differences evident in the apo P-gp crystal structures.42, 43, 61, 62 In fact, principle component analysis of simulations initiated from three different murine P-gp crystal structures show conformational differences between replica simulations from different structures, and also between simulations initiated from the same structure, highlighting the inherent conformational flexibility of P-gp.58 In this study, a combination of MD simulations and umbrella sampling techniques have been used to determine the location of the free energy minima within the TM pore of P-gp for a set of experimentally well characterized substrates, namely Hoechst 33342,63 Rhodamine 123,64 paclitaxel; the inhibitor tariquidar; and the modulator verapamil. The molecular structures are shown in Figure 1 and their competitive and non-competitive binding interactions with P-gp are shown in Table 1. Hoechst 33342 (Figure 1A) is a bisbenzamide DNA binding fluorescent stain and Rhodamine 123 (Figure 1B) is a flurone dye. Both Hoechst 33342 and Rhodamine 123 are canonical substrates of P-gp and fluorescence spectroscopy studies on the kinetics of these two compounds suggest that they bind non-competitively to two distinct sites in P-gp.27, 28 Paclitaxel (Figure 1C), is one of the largest P-gp


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substrates (854 a.m.u.) and is a clinical anti-mitotic chemotherapeutic agent used in the treatment of ovarian, breast, lung, and pancreatic cancers.9,

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Tariquidar (Figure 1D) inhibits the basal ATPase

activity of P-gp and is one of the most potent inhibitors of P-gp in clinical trials.65 Verapamil (Figure 1E) is a P-gp substrate when administered in isolation, but acts as a P-gp modulator when coadministered with a second substrate, effectively inhibiting the transport of the second substrate.30 All five compounds are predominantly hydrophobic. Paclitaxel is uncharged at pH 7.0. The other four substrates carry a charge of +1 at neutral pH. Each substrate in this diverse set of compounds has been extensively characterized experimentally which, when paired with free energy profiles and unrestrained molecular dynamics simulations, has enabled us to tease apart the underlying biochemical particulars behind P-gp substrate recognition, binding and transport.


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Figure 1. The chemical structures of (A) Hoechst 33342, (B) Rhodamine 123, (C) paclitaxel, (D) tariquidar and (E) verapamil. For each molecule, the ionizable group and its protonation state at pH 7.0 are shown in red.

METHODOLOGY ACS Paragon Plus Environment

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To identify and compare the binding locations of Hoechst 33342, Rhodamine 123, paclitaxel, tariquidar and verapamil, the potential of mean force (PMF) for each of these compounds was calculated along an axis (Z-axis) passing through the center of the TM pore of P-gp, normal to the plane of the membrane. To verify the locations of the energy minima, we performed unrestrained molecular dynamics simulations of each compound in its minimum energy location(s) as identified in the PMF simulations. Simulation setup The starting configuration for all MD simulations performed here was based on our previously published, extensively equilibrated conformations of P-gp (Protein Data Bank (PDB) code: 3G5U, 4M1M) embedded in a 9:1 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and cholesterol bilayer.1, 58, 66 Note, although 3G5U has been superseded, we have previously demonstrated that the choice of crystal structure used to initiate the simulations does not affect the binding interactions between P-gp and its substrates significantly within the TM pore.1 The methodology employed to set up the simulations was the same as used in Subramanian et al.1 MD simulations were performed using GROMACS67 versions 3.3.3 and 5.1.4 in conjunction with the GROMOS 54A7 force field for proteins.68 The simple point charge (SPC) water model69 was used to describe the solvent water. The parameters for POPC were taken from Poger et al.70 The parameters for cholesterol were taken from O’Mara and Mark.66 All simulations were performed under periodic boundary conditions in a rectangular box. The dimensions of the box were chosen such that the minimum distance of the protein to the box wall was at least 1.0 nm. A twin-range method was used to evaluate the non-bonded interactions. Interactions within the short-range cut-off of 0.8 nm were updated every step. Interactions within the long-range cut-off of 1.4 nm were updated every 8 fs, together with the pair list. A reaction field correction was applied using a relative dielectric constant, to minimize the effect of truncating the electrostatic interactions beyond the 1.4 nm long-range cut-off.71 A dielectric constant of r = 78.5 was used, as this is the experimental relative dielectric constant for water at ~300 K. While a range of other


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values have been used with SPC water to attempt to match the reaction field to different calculated values for the dielectric constant of this water model, the properties of water have been shown to be insensitive to small changes in the relative dielectric constant beyond the 1.4 nm cut-off.72 The reaction field approach was chosen as it has been repeatedly shown that, when used in conjunction with an appropriate cutoff, the results obtained are essentially identical to those obtained using lattice sum methods such as Particle mesh Ewald (PME), including in the case of lipid bilayers,70 while being much more computationally efficient for large systems. The LINCS algorithm73 was used to constrain the lengths of the covalent bonds. The geometry of the water molecules was constrained using the SETTLE algorithm.74 In order to extend the timescale that could be simulated, explicit hydrogen atoms in the protein were replaced with dummy atoms, the positions of which were calculated at each step based on the positions of the heavy atoms to which they were attached. This eliminates high frequency degrees of freedom associated with the bond angle vibrations involving hydrogens. These degrees of freedom are largely uncoupled from the rest of the protein and their elimination allows a time step of 4 fs to be used to integrate the equations of motion without affecting thermodynamic properties of the system significantly, as discussed by Feenstra et al.75 The simulations were carried out in the NPT ensemble at T = 300 K, and P = 1 bar. The temperature and pressure were maintained close to the reference values by weakly coupling the system to an external temperature76 and pressure bath using a relaxation time constant of 0.1 ps and 0.5 ps, respectively. The pressure coupling was semi-isotropic. Data was collected for analysis every 25 ps during the PMF calculations. Images were produced using VMD 1.9.2.77 P-gp substrate and inhibitor parameters The molecular topologies for Hoechst 33342, Rhodamine 123, paclitaxel, tariquidar and verapamil were obtained using the Automated Topology Builder (ATB) and Repository version 2.0.78 The protonation state of each molecule at pH 7.0 was determined by calculating the ionization constant of


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each titratable group in the compound, using the algorithm implemented in ChemAxon 15.7.27.79, 80 At pH 7.0, Hoechst 33342, Rhodamine 123, tariquidar and verapamil were predicted to be protonated, resulting in a charge of +1 on each of these molecules. Paclitaxel was predicted to be neutral at pH 7.0. The chemical structure of each molecule is shown in Figure 1. In each case, the titratable group and the protonation state at pH 7.0 are shown in red. Parameters for all molecules at pH 7.0 were generated with the help of the ATB using the method described in Subramanian et al.1 The ATB provides parameters for small molecules (< 40 atoms) based on the GROMOS 54A7 force field.68 The parameters generated by the ATB have been demonstrated to reproduce the hydration free enthalpies for a wide range of drug-like molecules containing similar functional groups to those found in Hoechst 33342, Rhodamine 123, paclitaxel, tariquidar and verapamil with an average deviation from experiment of approximately 4 kJ/mol. These parameters have an accuracy comparable to other comparable force fields, as demonstrated in the SAMPL4 blind computational challenge.78 The parameters for Hoechst 33342, Rhodamine 123, paclitaxel, tariquidar and verapamil used in this study are available for download from the ATB (Molecule ID (MOLID): 7281, MOLID: 6182, MOLID: 7282, MOLID: 7284, and MOLID: 7283 respectively).81 Umbrella sampling simulations The PMF was calculated along the central axis of the equilibrated, membrane-embedded P-gp (PDB code: 3G5U) of Subramanian et al.1 for each compound, using umbrella sampling techniques.82 To initiate the simulations, a group of eight residues (Ala79, Ser80, Val81, Gly82, Asn83, Val84, Ser85, and Lys86) from the extracellular region of transmembrane domain 1 (TM1) was chosen as a reference group. Two sets of simulations were performed. To calculate the relative binding energy of the canonical substrates, Hoechst 33342 or Rhodamine 123, to P-gp, a single molecule of the substrate was placed in the solvent at a distance of 15.0 nm from the reference group on the intracellular side of P-gp (Z = -15.0 nm). The substrate was placed on the axis passing through the reference group and normal to


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the membrane, which corresponded to the chosen reaction coordinate (Z-axis) as shown by the horizontal line in Figure 2A. To determine the location of the free energy minimum of the other compounds within the P-gp TM pore, one molecule of either paclitaxel, verapamil or tariquidar was placed in the solvent between the intracellular extensions of the TMDs, near the intracellular lipidwater interface, at a distance of 5.5 nm from the reference group along the reaction coordinate (Z = -5.5 nm). The starting configurations for the umbrella sampling simulations for each molecule were generated as described in Subramanian et al.1 For the simulations of Hoechst 33342 and Rhodamine 123, 61 umbrella sampling windows were generated per substrate; set 0.25 nm apart so that the final window was located in the extracellular space at 15.0 nm from the reference group along the reaction coordinate. Supporting Information Table S1 shows that extended simulation times of 50 – 60 ns per window were required to obtain convergence for each compound. Convergence was also confirmed by examining whether the values of the derivatives for adjacent points showed a consistent trend. The aim of this study was to determine the location of the transmembrane binding site for each compound in Pgp. To reduce the computational cost for the simulations of paclitaxel, verapamil and tariquidar, 23 umbrella sampling windows were generated per substrate, set 0.25 nm apart so that the final window was located in the extracellular space at 5.5 nm. In each umbrella sampling window, the center of mass of the substrate was harmonically restrained using a force constant of 500 kJ/mol/nm2 in the Zdirection to allow it to sample that specific region (window) along the reaction coordinate. To reduce the effect of any directional bias induced during the generation of the reference configurations on the final PMF, the starting configuration for each of the intermediate distances was selected randomly from the two adjacent reference configurations, as described in Subramanian et al.1 The motion of the substrate was not restrained in the X-Y plane; each molecule was able to freely rotate, translate and change conformation within each umbrella sampling window. The random selection of starting positions along the reaction coordinate not only avoids directional bias but also provides an independent test of convergence. This is because using Umbrella Integration one can directly examine ACS Paragon Plus Environment

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whether the derivatives at two adjacent points converge to similar values although starting from alternative initial configurations.

Figure 2. The potential of mean force (PMF) of the P-gp substrates Hoechst 33342 (black) and Rhodamine 123 (green). (A) The structure of membrane embedded P-gp, shown in gold and silver cartoon representation. The reference group residues are shown in cyan spacefill representation. The phosphate head groups of the lipid bilayer are shown as pink sticks. The grey line represents the reaction coordinate along which the substrate was moved, which corresponds to the longitudinal axis of P-gp. (B) The PMF of Hoechst 33342 (black) and Rhodamine 123 (green) in P-gp as a function of the reaction coordinate, Z. The distance along the reaction coordinate was calculated with respect to the center of mass of the reference group. Note that the error bars are shown at 0.5 nm increments for clarity.


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For all compounds, the PMF was obtained by integrating the derivative of the free energy with respect to the distance along the reaction coordinate using the Umbrella Integration method of Kaëstner and Thiel.83,

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The advantage of Umbrella Integration over approaches such as the weighted histogram

approach (WHAM) is that there is no requirement for the individual windows to overlap. The ability to monitor convergence in this manner and to avoid the need to have overlapping distributions, as is the case in WHAM, is the reason why Umbrella Integration was used in this work. The system was simulated with the substrate located within each specific umbrella sampling window along the reaction coordinate until the derivative of the free energy had converged. The derivative of the free energy was considered to be converged if the difference between the average of the derivative calculated for a series of 1 ns sliding windows taken every 0.5 ns did not vary by more than 5 kJ/mol over a period of 5 ns (10 windows). The simulation was extended for a further 10 ns after the convergence criteria was met, for data collection. The average derivative over these final equilibrated 10 ns was used to calculate the PMF and the standard error in the derivative of the free energy. The equilibration time required for each of the five molecules to meet the convergence criteria at each umbrella sampling window is given as Supporting Information (Table S1). Note that as the PMF of paclitaxel, tariquidar and verapamil was calculated from Z = -5.5 nm, the relative free energy of these molecules along the reaction coordinate (Z-axis) is calculated relative to the value at Z = -5.5 nm. Unrestrained simulations To confirm the positions of the preferred binding locations of each substrate or inhibitor, a single molecule of each compound was placed at the location along the reaction coordinate corresponding to its specific energy minimum obtained from the PMF calculations. To ensure these simulations were independent of the PMF calculations, and that the results were independent of the choice of crystallographic model used to represent P-gp, these simulations were initiated using the membraneembedded, equilibrated 4M1M P-gp structure in a 9:1 POPC:cholesterol bilayer58 and were performed ACS Paragon Plus Environment

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in the absence of any restraints or biasing potentials. The starting location for each unrestrained simulation is given in Table 2. Each starting location represents one system that was simulated. After initial equilibration, three independent non-biased simulations of 125 ns were performed on each system. The final 100 ns of each simulation was used for analysis.


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Analysis Cluster Analysis: To determine the relative populations of the conformations of the molecules used in this work, the trajectories were clustered using the method of Daura et al.85, 86 Two conformations were considered to fall within the same cluster if the backbone RMSD between the conformations was less than 0.15 nm. Root mean squared deviation (RMSD): As a measure of the difference between configurations extracted from the trajectories or clusters, the RMSD was calculated using the method of Maiorov and Crippin87 after first performing a rotational and translational fit of each frame of the trajectory to a reference structure or domain. Protein and substrate contacts: All of the protein residues for which the centers of at least one atom lay within 0.35 nm of the center of any atom of the substrate at any time during the last 10 ns of each window simulation (for the umbrella sampling simulations) or the last 100 ns of the unrestrained simulation were considered to make a direct contact with the substrate. The fraction of time that this contact was observed (over either 10 ns or 100 ns) was calculated and presented as a percentage.

RESULTS AND DISCUSSION To identify the minimum free energy binding locations of the substrates Hoechst 33342, Rhodamine 123 and paclitaxel, the inhibitor tariquidar and the modulator verapamil, the PMF of each substrate along the reaction coordinate that passed between the NBDs and the cytosolic parts of P-gp and into the TM pore, from Z = -15.0 nm to Z = 0 nm (for Hoechst 33342 and Rhodamine 123, Figure 2A) or between the cytosolic extensions of the TMDs of P-gp and the TM pore, from Z = -5.5 nm to Z = 0 nm (for paclitaxel, tariquidar and verapamil, Figure 3A) was calculated. Note that the PMF gives the relative free energy with respect to a given reference location along a chosen reaction coordinate. The


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difference in free energy between any two states is independent of the reaction coordinate (pathway) chosen, so long as the sampling is converged. In principle, a range of alternative reaction coordinates could have been chosen, including pathways through the lipid bilayer. The aim of this study was to identify the transport-competent minimum free energy binding locations for each substrate. The difference between the free energy of the compound in bulk solution (at Z = -15.0 nm) and its minimum free energy location along the reaction coordinate provides an estimate of the free energy of binding to the protein, thus the path chosen was from the aqueous solution. The PMFs of Hoechst 33342 and Rhodamine 123 were found to have two energy wells: one transport-incompetent minimum in the cytosolic regions of P-gp and a transport-competent minimum within the TM pore, as shown in Figure 2B. The PMFs for paclitaxel, tariquidar and verapamil all contained energy wells in the TM pore.

Binding of Hoechst 33342 to P-gp The PMF of Hoechst 33342 (black line, Figure 2B) begins to decrease at Z = -12 nm as Hoechst 33342 begins to interact with the NBDs (shown at the corresponding location in Figure 2A). The minimum in the PMF of Hoechst 33342 is at -7.5 nm and corresponds to a free energy well with a minimum of -37 ± 2 kJ/mol relative to the free energy of Hoechst 33342 in bulk solution. This minimum lies at the interface between NBD1 and the cytosolic extensions of TMD1. Previous simulations examining the spontaneous binding of substrates from the aqueous solution have shown that substrates readily accumulate at regions of negative electrostatic potential on the solvent-exposed surface of P-gp, which corresponded to the NBDs and cytosolic extensions of the TMDs.1 While these results suggest energetically favorable interaction sites, this work will focus on characterising the energy minima within the TM pore that may contribute to transport-competent interaction sites.


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Hoechst 33342 encounters an energy barrier further along the reaction coordinate towards the bilayer, which has a maximum at the cytosolic lipid-water interface (Z = -4.4 nm). The difference between this maximum and the free energy minimum at -7.5 nm is 24 ± 4 kJ/mol (~12 kJ/mol lower than the free energy of Hoechst 33342 in bulk solution). The PMF of Hoechst 33342 contains another free energy well within the TM pore at a position compatible for substrate transport, at Z = -2.7 nm. This TM energy minimum has a relative free energy of -25 ± 5 kJ/mol with respect to bulk solution. In the energy well, Hoechst 33342 adopted a single elongated conformation, show in Figure S1. Hoechst 33342 formed direct contacts with 12 residues from TM helices 1, 6, 9, 10, 11 and 12 during the umbrella sampling simulations, as listed in Table S2. To confirm that the minimum in the PMF corresponded to a stable binding location, that is the PMF calculations had not only converged appropriately but that the proposed binding location was independent of the model structure used to calculate this PMF, unrestrained simulations in which Hoechst 33342 was placed in this proposed location within the 4M1M structural model were performed. Hoechst 33342 not only remained in close proximity to its position in umbrella sampling simulations, it also formed direct contacts with seven of the 12 residues identified in the PMF simulations: Leu64, Phe339, Ser340, Leu839, Met945, Ile977 and Val978. This provides an independent demonstration that the minima identified in the PMFs correspond to stable binding sites. A list of residues that contact Hoechst 33342 within the TM pore energy minimum at Z = -2.7 nm are given in Table 3. Based on FRET studies, Qu et al. proposed that the primary binding location for Hoechst 33342 (the so-called H-site) lay within the TM pore, ~1.4 nm from the intracellular lipid-water interface.25 In our simulations, the intracellular lipid-water interface is at Z = -4.5 nm. The binding location proposed here would lie ~2.3 nm from the lipid-water interface, within the combined uncertainty of the FRET studies and simulations. Of the 12 residues that form direct contacts with Hoechst 33342 in the PMF simulations in the TM energy well, Ser340 has been implicated in the


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binding of Hoechst 33342 in our unrestrained simulations and in two previous docking studies.45,

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Leu64, Phe339 and Val978 made contacts in both the PMF and unrestrained simulations, and have, along with Gly868, been implicated experimentally in the binding of verapamil, vinblastine, colchicine and Rhodamine 123.15-17, 19, 38, 39 Binding of Rhodamine 123 to P-gp The PMF of Rhodamine 123 (green line, Figure 2B) decreases much more rapidly than that of Hoechst 33342 after initial contact with P-gp. The lowest point along the reaction coordinate is located at Z = 10.0 nm, close to the cytosolic termini of the NBDs and has a relative free energy of -45 ± 3 kJ/mol relative to the bulk solvent. This energy minimum lies within a broad region below ~ -40 kJ/mol stretching from Z = -10.5 nm to approximately Z = -7.5 nm, in a location consistent with the previously identified regions of negative electrostatic potential.1 Moving beyond the NBDs along the reaction coordinate, the PMF of Rhodamine 123 increases, peaking at ~ Z = -3.5 nm, just past the intracellular entrance to the P-gp TM pore. There is a second energy well within the TM pore at Z = -2.3 nm, close to the center of the membrane. This TM energy well has a depth of -16 ± 3 kJ/mol relative to the bulk solvent and is proposed to correspond to a transport-competent interaction site. The TM free energy well for Rhodamine 123 is located approximately 0.4 nm further along the TM pore than that of Hoechst 33342. As shown in Figure S2, Rhodamine 123 bound between TM helices 1, 3, 6, 9, 11 and 12 in the PMF simulations, where it formed direct contacts with eight residues (Ser340, Leu389, Gln942, Met945, Tyr946, Ile977, Val978 and Phe979) in both the PMF and unbiased simulations. The contacts between Rhodamine 123 and P-gp in this TM energy well are summarized in Table 3 and detailed in Table S3. Of the 17 residues that interacted with Rhodamine 123 within the TM pore in the PMF simulations, Leu64, Ile336, Phe339, Tyr949, Val978 and Phe979 have been implicated biochemically in binding Rhodamine 123 at the proposed R-site.15-18, 39, 40


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Binding of paclitaxel, tariquidar and verapamil within the P-gp TM pore The majority of available mutagenesis data suggests that the P-gp residues that bind substrates and inhibitors are primarily located within the membrane-spanning region, and binding is facilitated by TM helices 4, 5, 6, 10, 11 and 12 in the TMDs.19 To identify the possible location of the minimum energy binding sites of paclitaxel, tariquidar and verapamil within the TM pore, PMFs for these compounds were calculated from the intracellular lipid-water interface to the apex of the TM pore (from Z = -5.5 nm to Z = 0 nm), as shown in Figure 3A. The PMFs of paclitaxel, tariquidar and verapamil along the reaction coordinate are shown in Figure 3B, C and D, respectively. Note, in each case the initial umbrella sampling window (Z = -5.5 nm) of each system was assigned a free energy value of 0 kJ/mol. However, as the calculations are not initiated from a comparable reference point (i.e. bulk solution), it is not possible to directly compare the relative free energy values between the compounds or to comment on their relative binding affinities. It is only possible to use these PMFs to determine the location of potential binding sites within the TM pore.


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Figure 3. The PMF for paclitaxel, tariquidar and verapamil through the transmembrane pore of P-gp. (A) The starting configuration used to initiate the PMF calculation with the substrate positioned at Z = 5.5 nm along the reaction coordinate, near the intracellular entrance to the TM pore. The PMF of (B) paclitaxel (yellow), (C) tariquidar (cyan) and (D) verapamil (red) calculated along the reaction coordinate (Z) from Z = -5.5 nm to the reference group at Z = 0 nm (cyan spacefill in panel (A)). Note that the error bars are shown at 0.5 nm increments for clarity.

The PMF of paclitaxel, shown in Figure 3B, contains two energy wells: one at the lipid-water interface (Z = -4.4 nm) with a depth of -15 ± 2 kJ/mol, and a second within the TM pore at Z = -2.7 nm with a depth of -5 ± 3 kJ/mol, close to the center of the lipid bilayer. A small energy barrier separates these wells. A snapshot of paclitaxel in each energy well is shown in Figure 4 (yellow CPK spacefill). At the deeper energy well at Z = -4.4 nm, paclitaxel interacted with residues from TM helices 4, 5, 9 and 12 in the PMF simulations, however it did not contact any of these residues in the unbiased simulations. Instead, in the unbiased simulations initiated at this energy well location (-4.4 nm), paclitaxel spontaneously moved deeper into the pore and interacted with residues at the second energy well location, Z = -2.7 nm. A snapshot of paclitaxel in this second energy well (orange CPK spacefill) from the unbiased simulations, overlaid with paclitaxel in this energy well from the PMF simulations (yellow CPK spacefill), is shown in Figure 4B. Although the energy well identified using the 3G5U structure is shallower at Z = -2.7 nm, the fact that paclitaxel spontaneously moved from the deeper energy well at Z = -4.4 nm to the shallower well in the unbiased simulations using the 4M1M structure suggests that we can predict the regions in which substrates bind. There may be uncertainty in the depths of the energy wells due to local discrepancies in structure in the TM domains and the overall plasticity of each P-gp structure,58 however the general location of the wells is supported by both the PMF and spontaneous binding data. In the PMF simulations at this second energy well, paclitaxel made ACS Paragon Plus Environment

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direct contacts with 19 residues in TM helices 1, 2, 3, 6, 11 and 12 as listed in Table 3 and Supporting Information Table S4. Paclitaxel made direct contacts with 12 of these residues in the unrestrained simulations (Leu64, Met67, Gln128, Phe190, Ile336, Phe339, Phe934, Phe938, Gln942, Val978, Phe979 and Met982). After the second energy well, the PMF rises rapidly as paclitaxel enters the constricted region of the TM pore that is closed to the extracellular environment. Experimentally, cysteine mutagenesis of residues corresponding to Phe974 and Ala307 have been shown to perturb the transport paclitaxel by P-gp.44 Both these residues lie near the extracellular protein/lipid interface, in the constricted region of the TM pore.

Figure 4. Overlay of the binding locations of paclitaxel during the unrestrained and PMF simulations, where unrestrained paclitaxel (orange spheres) moved along the reaction coordinate from the energy well located at Z = -4.4 nm (yellow spheres; (A) to Z = -2.7 nm (yellow spheres; (B)). Paclitaxel at these respective energy wells from the PMF calculations is shown in yellow CPK spacefill representation. A representative snapshot from the unrestrained simulations is shown as orange CPK spheres. The 4M1M structure of P-gp is shown, with selected helices labeled.

The PMF of the P-gp inhibitor tariquidar, shown in Figure 3C, is essentially flat from the cytosolic side of the lipid-water interface (Z = -5.5 nm) to the center of the TM pore (Z = -2.5 nm), before rising rapidly in the constricted region of the pore. At the intracellular entrance of the TM pore (Z = -3.7 nm), the PMF contains a shallow energy well that is -8 ± 5 kJ/mol deep. In this location, tariquidar adopted a


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U-shape, positioned across the entrance of the TM pore between TM helices 1, 3, 6, 9 and 12, effectively blocking the pore as shown in Supporting Information Figure S3. Here, tariquidar contacted 13 residues in the PMF simulations; four of these contacts also formed in the unrestrained simulations (His60, Gln834, Asn838 and Ser988) as listed in Table 3. In unbiased simulations, tariquidar remained in the vicinity of the energy minimum, but adopted a range of orientations that completely blocked the TM pore. These binding modes may explain the inhibition of P-gp by tariquidar. Figures showing the binding modes of tariquidar in the TM pore during the PMF and unrestrained simulations are provided as Supporting Information Figure S3. The PMF of verapamil, shown in Figure 3D, is broadly similar to that of tariquidar. The PMF of verapamil contains a shallow energy well of -5 ± 2 kJ/mol in the TM pore, close to the center of the bilayer (Z = -3.1 nm). Within the energy well, verapamil adopted an elongated conformation and bound to TM helices 1, 5, 6, 7, 8, 9 and 12 in the PMF simulations. This conformation, which is shown in pink in Figure S4, was stably maintained in the unrestrained simulations (maroon in Figure S4). Of the 20 residues that formed contacts with verapamil in the PMF simulations (Table S5), 12 of these also formed contacts in the unrestrained simulations (Leu64, Leu300, Ile302, Ile336, Phe339, Ser340, Phe724, Val978, Phe979, Gly980, Met982, Ala983) as listed in Table 3. In particular, nine of the residues forming direct contact with verapamil (Leu64, Ile302, Phe339, Phe724, Ser725, Asn838, Val978, Gly980 and Ala981) have been experimentally implicated in binding.14, 16, 17, 20, 37, 38 Non-competitive binding pairs Hoechst 33342 and Rhodamine 123 have been shown experimentally to bind non-competitively under certain assay conditions.27, 28 The widely accepted experimental model suggests that they may bind to pharmacologically distinct sites.28 Our PMFs of Hoechst 33342 and Rhodamine 123 (Figure 2B) suggest that the primary binding sites for these two substrates within the cytosolic NBD regions of P-gp are well separated (~2.5 nm apart), consistent with the substrates displaying non-competitive binding. ACS Paragon Plus Environment

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However, both have a second transport-competent binding site located within the TM pore, which the molecules must occupy to be transported. These second TM sites show some degree of overlap. For example, Ser340, Met945, Ile977 and Val978 interact with both substrates in both the unbiased and PMF simulations. Nevertheless, it is possible to map both Hoechst 33342 and Rhodamine 123 onto the structure of P-gp at the same time, based on the position of the individual substrates from unbiased simulations, as shown in Figure 5A. This suggests the TM pore may be able to accommodate both substrates simultaneously. This is supported by structural investigations, which have mapped the residues implicated in the pharmacological interactions of Hoechst 33342 and Rhodamine 123 to spatially distinct sites within P-gp. 88

Figure 5. Proposed non-competitive and competitive binding behaviors of the P-gp substrates studied here in the TM pore. (A) The non-competitive inhibitors Hoechst 33342 (gray CPK coloring, spacefill) and Rhodamine 123 (green CPK coloring, spacefill), and paclitaxel (orange CPK coloring, spacefill) and nicardipine (purple CPK coloring, spacefill), could potentially be accommodated by P-gp in the TM pore concurrently. (B) The competitive inhibitors Hoechst 33342 (gray CPK coloring, spacefill) and tariquidar (blue CPK coloring, spacefill) bind to overlapping regions of the TM pore. The 4M1M ACS Paragon Plus Environment

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structure of P-gp is shown in cartoon representation, with TM helices labeled. The substrates and inhibitors shown are from the unbiased simulations.

Based on pharmacological interactions, paclitaxel and nicardipine have also been proposed to bind noncompetitively to P-gp.22 Under a Michaelis-Menten schema for competitive and non-competitive interactions, this suggests that they may bind at physically distinct sites. The PMF of paclitaxel is shown in Figure 3B. The PMF of nicardipine calculated along the same reaction coordinate has been published previously.1 The PMF of nicardipine1 has two energy wells, the deepest of which is located at Z = -2.3 nm. In unbiased simulations, paclitaxel spontaneously moves to an energy well at Z = -2.7 nm. Again, there is some degree of overlap between the substrates, with four residues from TM6 (Phe339), and TM12 (Val978, Phe979 and Met 982) interacting with paclitaxel in both the unbiased and PMF simulations, and with nicardipine.1 Figure 5B maps the interaction location and orientation of paclitaxel and nicardipine to the 4M1M P-gp structure from the unbiased simulations, showing that they preferentially bind to spatially distinct regions in the TM pore.

Competitive binding pairs Equilibrium binding studies by Martin et al.22 suggested that that Hoechst 33342 and tariquidar bind competitively to P-gp. From Michaelis-Menten kinetics, this suggests that they may bind to the same physical region of the protein. The PMF of Hoechst 33342 contains an energy well within the TM pore at Z = -2.7 nm, close to the center of the membrane, which is proposed to correspond to a transportcompetent location. In the simulations, tariquidar adopted a U-shape and bound across the intracellular entrance to the TM pore (Z = -3.7 nm), occluding the pore. It is proposed that the physical occlusion of the TM pore by tariquidar prevents the binding of Hoechst 33342, acting as a competitive inhibitor of


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P-gp. Indeed, the binding locations of Hoechst 33342 and tariquidar during the unrestrained simulations directly overlap in the TM pore as shown in Figure 5C.

CONCLUSIONS The remarkable ability of P-gp to recognize and transport a wide variety of substrates has been the subject of intense research for over 40 years. Numerous biochemical, pharmacological and pharmacophore mapping studies examining substrate transport by P-gp have proposed a number of pharmacologically distinct substrate binding interactions. However, no experimental studies have been able to provide a detailed characterization of any physical substrate binding site. To determine the possible preferred binding locations, and to understand the interactions that govern competitive and non-competitive substrate binding to P-gp, PMFs and unrestrained simulations were performed for three P-gp substrates, one inhibitor and one modulator. The PMFs were calculated as a function of a reaction coordinate through the center of P-gp. Based on the PMFs presented in this work and the previously published PMFs for morphine and nicardipine,1 a scheme for substrate binding interactions in P-gp has been proposed for non-competitive and competitive binding pairs. Analysis of the residues that contact each substrate at their primary (lowest energy) binding location shows that in the case of non-competitive binding pairs, Hoechst 33342/ Rhodamine 123 and paclitaxel/nicardipine, the minimum free energy binding location of each substrate is distinct and spatially separated. In contrast, the competitive binding pair Hoechst 33342/tariquidar involves an overlapping set of residues in the TM pore of P-gp. This work demonstrates that P-gp contains both specific, spatially distinct binding sites and non-specific, spatially overlapping binding sites. All substrates examined in this study showed a free energy minimum within the TM pore that likely represents a transport-competent binding location. This has important consequences for our understanding of substrate binding and transport by P-gp and the physical basis of P-gp inhibition, which could be exploited to suppress multidrug efflux. ACS Paragon Plus Environment

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1 TABLES 2 3 4 Table 1. List of non-competitive and competitive P-gp substrates, inhibitors and modulators 5 6 7 Hoechst Rhodamine 8 Morphine Nicardipine Paclitaxel Colchicine Vinblastine Verapamil Tariquidar Elacridar Daunorubicin Doxorubicin 9 33342 123 10 11 12 --13 Morphine 14 15 Nicardipine NCa --16 17 Hoechst NCb NC22 --18 19 33342 No data No data NC27, 28 --20 Rhodamine 21 123 22 Paclitaxel No data NC22 NC22 No data --23 24 Colchicine NCb NCc C27, 28 NC27, 28 No data --25 26 C3 NC24 NC22 No data NC22 NCe --27 Vinblastine 28 29 Verapamil C3 NC24 NCd No data No data NCe C3 --30 31 No data NC22 C22 NCd NC22 Cc NC22 NCb --32 Tariquidar 33 No data NC22 C22 NCd NC22 Cc NC22 NCb C22 --34 Elacridar 35 36Daunorubicin No data No data NC27, 28 C27, 28 No data NC27, 28 No data No data NCc NCc --37 38 No data No data NC27, 28 C27, 28 No data NC27, 28 No data No data NCc NCc C27, 28 --39 Doxorubicin 40 41 NC – non-competitive, C – competitive; a- inferred from references3, 24; b- inferred from references3, 22; c- inferred from references22, 27, 28; d- inferred from reference22; e42 inferred from references3, 22, 27, 28. 43 44 ACS Paragon Plus Environment 45 46 29 47


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Table 2. Systems studied here, and locations of energy minima identified in the PMF simulations System

Location of the drug, Z (nm)

P-gp + HST1

z = -2.7

P-gp + HST2

z = -7.5

P-gp + ROD1

z = -2.3

P-gp + ROD2

z = -10.0

P-gp + PXL1

z = -2.7

P-gp + PXL2

z = -4.5

P-gp + TQR

z = -3.7

P-gp + VPL

z = -3.1


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Table 3. P-gp residues that made direct contacts with the substrates in the energy wells. The contacts made in the 3G5U PMF simulations are listed, and residues that contacted in both those simulations and the unrestrained 4M1M simulations are highlighted in bold. The percentage overlap between the contact residues in the PMF and unrestrained simulations is given. Substrate

Hoechst 33342

Rhodamine 123

Paclitaxel

Tariquidar

Verapamil

TM pore energy well, Z (nm)

Direct contact residues (TM helix, PMF, PMF and unrestrained)

Percentage overlap

-2.7

TM1 Leu64 TM6 Phe339, Ser340 TM9 Gly868, Asn838, Leu839 TM10 Val869, Met872 TM11 Met945 TM12 Ile977, Val978, Gly980

58 %

-2.3

TM1 Leu64, Met67, Met68, Phe71 TM3 Phe190 TM6 Ile336, Phe339, Ser340 TM9 Asn838, Leu839 TM11 Gln942, Met945, Tyr946, Tyr949 TM12 Ile977, Val978, Phe979

47%

-2.7

TM1 Leu64, Met67 TM2 Val124, Gln128, Val129, Trp132 TM3 Lys177, Val175, Phe190 TM6 Ile336, Phe339 TM11 Phe934, Phe938, Gln942 TM12 Ile977, Val978, Phe979, Gly980, Met982

63%

-3.7

TM1 His60 TM3 Phe189, Ile186, Met188 TM6 Asn347, Ala352, Asn353, Arg355 TM9 Gln834, Asn838 TM12 Ser988, Asp993, Lys996

30%

-3.1

TM1 Leu64 TM5 Leu300, Ile302 TM6 Phe332, Ile336, Phe339, Ser340 TM7 Phe724, Ser725, Phe728 TM8 Leu758 TM9 Asn838 TM12 Val978, Phe979, Gly980, Ala981, Met982, Ala983, Val984, Gln986

60%


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ASSOCIATED CONTENT Supporting Information. Detailed tables showing the equilibration times for each window (Table S1), and a detailed summary of residues that formed direct contacts with Hoechst 33342 (Table S2, Figure S1), Rhodamine 123 (Table S3, Figure S2), paclitaxel (Table S4), tariquidar (Table S5, Figure S3) and verapamil (Table S5, Figure S4) in their respective energy wells are given as Supporting Information.

AUTHOR INFORMATION Corresponding Author * Dr. Megan O’Mara * E-mail: [email protected] Present Addresses ¥Research

School of Chemistry (RSC), Australian National University, Australia.

Author Contributions Conceived and designed the experiments: MLO, AEM, NS. Performed the experiments: NS. Analyzed the data: NS. Contributed reagents/materials/analysis tools: MLO, AEM. Wrote the manuscript: NS, AS, MLO, AEM. Funding Sources MLO and AEM were supported by grants from the Australian Research Council (DP110100327) and the National Health and Medical Research Council (APP1049685). MLO was the recipient


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of an Australian Research Council Discovery Early Career Researcher Award (DE120101550). AS is a recipient of a Westpac Future Leaders Scholarship from the Westpac Bicentennial Foundation. Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government. We thank Dr Karmen Condic-Jurkic for her comments on this manuscript.


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TABLE OF CONTENTS GRAPHIC


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Figure 1. The chemical structures of (A) Hoechst 33342, (B) Rhodamine 123, (C) paclitaxel, (D) tariquidar and (E) verapamil. For each molecule, the ionizable group and its protonation state at pH 7.0 are shown in red. 179x381mm (300 x 300 DPI)


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Figure 2. The potential of mean force (PMF) of the P-gp substrates Hoechst 33342 (black) and Rhodamine 123 (green). (A) The structure of membrane embedded P-gp, shown in gold and silver cartoon representation. The reference group residues are shown in cyan spacefill representation. The phosphate head groups of the lipid bilayer are shown as pink sticks. The grey line represents the reaction coordinate along which the substrate was moved, which corresponds to the longitudinal axis of P-gp. (B) The PMF of Hoechst 33342 (black) and Rhodamine 123 (green) in P-gp as a function of the reaction coordinate, Z. The distance along the reaction coordinate was calculated with respect to the center of mass of the reference group. Note that the error bars are shown at 0.5 nm increments for clarity. 100x118mm (300 x 300 DPI)



Figure 3. The PMF for paclitaxel, tariquidar and verapamil through the transmembrane pore of P-gp. (A) The starting configuration used to initiate the PMF calculation with the substrate positioned at Z = -5.5 nm along the reaction coordinate, near the intracellular entrance to the TM pore. The PMF of (B) paclitaxel (yellow), (C) tariquidar (cyan) and (D) verapamil (red) calculated along the reaction coordinate (Z) from Z = -5.5 nm to the reference group at Z = 0 nm (cyan spacefill in panel (A)). Note that the error bars are shown at 0.5 nm increments for clarity. 221x581mm (300 x 300 DPI)


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Figure 4. Overlay of the binding locations of paclitaxel during the unrestrained and PMF simulations, where unrestrained paclitaxel (orange spheres) moved along the reaction coordinate from the energy well located at Z = -4.4 nm (yellow spheres; (A) to Z = -2.7 nm (yellow spheres; (B)). Paclitaxel at these respective energy wells from the PMF calculations is shown in yellow CPK spacefill representation. A representative snapshot from the unrestrained simulations is shown as orange CPK spheres. The 4M1M structure of P-gp is shown, with selected helices labeled. 36x16mm (300 x 300 DPI)



Figure 5. Proposed non-competitive and competitive binding behaviors of the P-gp substrates studied here in the TM pore. (A) The non-competitive inhibitors Hoechst 33342 (gray CPK coloring, spacefill) and Rhodamine 123 (green CPK coloring, spacefill), and paclitaxel (orange CPK coloring, spacefill) and nicardipine (purple CPK coloring, spacefill), could potentially be accommodated by P-gp in the TM pore concurrently. (B) The competitive inhibitors Hoechst 33342 (gray CPK coloring, spacefill) and tariquidar (blue CPK coloring, spacefill) bind to overlapping regions of the TM pore. The 4M1M structure of P-gp is shown in cartoon representation, with TM helices labeled. The substrates and inhibitors shown are from the unbiased simulations. 84x40mm (300 x 300 DPI)


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Probing the Pharmacological Binding Sites of P-Glycoprotein Using

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