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Surfactin at the Water/Air Interface and in Solution Javier Iglesias-Fernández, Leonardo Darré, Axel Kohlmeyer, Robert K. Thomas, Hsin-Hui Shen, and Carmen Domene Langmuir, Just Accepted Manuscript • Publication Date (Web): 22 Sep 2015 Downloaded from http://pubs.acs.org on September 26, 2015

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Surfactin at the Water/Air Interface and in Solution Javier Iglesias-Fernández,a Leonardo Darré,a Axel Kohlmeyer,b Robert K. Thomas,c and Hsin-Hui Shen,d Carmen Domenea,e,1 a

Department of Chemistry, King’s College London, Britannia House, 7 Trinity Street, London SE1 1DB, UK, bInstitute for

Computational Molecular Science (035-07), College of Science and Technology, Temple University, 1901 N. 13th Street, Philadelphia, PA 19122, USA, cPhysical & Theoretical Chemistry Laboratory, South Parks Road, University of Oxford, Oxford OX1 3QZ, UK, dDepartment of Microbiology, Faculty of Medicine, Nursing & Health Sciences, Clayton, Monash University, Melbourne, Victoria 3800, Australia, eChemistry Research Laboratory, Mansfield Road, University of Oxford, Oxford OX1 3TA, UK

1

Corresponding author: [email protected]

Tel: +44 - (0) 207848-7541

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Abstract The lipopeptide surfactin produced by certain strains of Bacillus Subtillis is a potent bio-surfactant with high amphiphilicity and a strong tendency for self-aggregation. Surfactin possesses a number of valuable biological properties such as antiviral, antibacterial, antifungal and haemolytic activities. Owing to these properties, in addition to the general advantages of bio-surfactants over synthetic surfactants, surfactin has potential biotechnological and biomedical applications. Here, the aggregation properties of surfactin in solution, together with its behaviour at the water/air interface were studied using classical molecular dynamics simulations (MD) at three different pH values. Validation of the MD structural data was performed by comparing neutron reflectivity and volume fraction profiles computed from the simulations with their experimental counterparts. Analysis of the MD trajectories supported conclusions about the distribution, conformations and interactions of surfactin in solution and at the waterair interface. Considering altogether, the work presented provides atomistic models for the rationalization of some of the structural and dynamic characteristics, as well as the modes of action of surfactin at different pH values.

Keywords: Molecular dynamics simulations; neutron reflectivity; antimicrobial peptides; biosurfactant


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Introduction Lipopeptides are molecules produced by a variety of fungi and bacteria composed of a lipid tail attached to a peptide.1 Lipopetides can potentially be involved in many biotechnological and biomedical applications.2 Thus, the study of their behaviour in solution and at interfaces has become a major area of research. In particular, the lipopeptide surfactin and its isoforms, which are produced by certain strains of Bacillus Subtillis, exhibit antiviral,3 antibacterial3-5 and antitumor6 activities, as well as haemolytic properties.7 For instance, surfactin is able to form cationic channels in the presence of planar lipid bilayers facilitating the transport of cations across membranes with potential applications in drug delivery.8 Due to its high amphiphilicity and strong tendency for self-aggregation, surfactin shows remarkable surface-, interface- and membrane-active properties.9 Surfactin is composed by a cyclic heptapeptide structure interlinked with a hydroxy fatty acid of chain lengths between 12 and 17 carbon atoms, to form a cyclic lactone ring structure.10, 11 The pattern of amino acids and hydroxy fatty acids in the molecule depends on the bacterial strain that produces surfactin, and the type of culture conditions.12 1H-NMR experiments and molecular modelling have indicated the presence of a horse-saddle conformation for the peptide ring component,13 whereas the hydrophobic tail is able to project away from the ring or fold back to it. Additionally, the presence of two acidic residues in the heptapeptide, Asp and Glu, makes this system strongly responsive to pH changes in the range of 4 to 9. The aggregation properties of surfactin in bulk solution have been previously investigated by several groups. Ishigami et al,14, 15 reported the presence of rod-like micelles with an aggregation number of approximately 170 at pH 8.7, employing alkaline titration techniques. This contrasts with results obtained by small-angle neutron scattering (SANS) experiments16 which suggested a smaller aggregation number (20±4) at pH 7.4, and an overall micelle diameter of 50±5 Å, with a hydrophobic core formed by surfactin tails and leucine residues.16 The formation of larger aggregates as pH decreases was also reported.17 Specifically, neutralization of acidic residues in the heptapeptide ring changed the aggregation pattern from spherical micellar at pH 9.5 to rod-like at neutral values of pH, and to micellar lamellar at pH values of 5.5.17 In contrast, Knoblich et al18 reported a distribution of spherical to ellipsoidal micelle shapes using electron cryo-microscopy at pH values of 7, 9.5, and 12.


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The amphipathic character of surfactin, which explains its aggregation properties in solution, also confers surfactin the ability to affect the surface tension of liquids. In fact, surfactin is able to reduce the water surface tension by ~2.5 times even at low concentrations (i.e. 20µM), increasing the ability of water to ‘wet’.19 The influence of several factors such as pH, temperature and electrolytes on the surface properties of surfactin was studied by Maget-Dana and Ptak,20 who suggested the possibility of several orientations of the molecule at the interface. Ishigami et al14, 15 attributed the excellent surface activity of surfactin to the formation of β-sheet secondary structures at the interface. By means of molecular modelling, and by fitting some experimental data, Gallet et al21 proposed a conformation in which the peptide ring stays parallel to the hydrophobic/hydrophilic interface, with the two acidic residues extending to the aqueous solution, and the β-hydroxy fatty acid folded back permitting the interaction with a leucine side chain. This conformation was also reported by means of neutron reflectometry techniques,16, 17 where the formation of ball-like structures at the interface was also proposed. In a molecular dynamics (MD) based study at the oil/water interface, Nicolas et al22 demonstrated that surfactin molecules show a high structural variability, capable of adopting different conformations depending on the interfacial concentration. Neutral surfactin molecules at the air/water interface have been studied by MD simulations using eight-nanosecond trajectories,23 highlighting the structural variations of the peptide ring and the hydrophobic tail as a function of the interfacial concentration. The present study aims to clarify several aspects of the behaviour of surfactin in solution and at the water/air interface, at different values of pH. MD simulations were used to gain insights into the structural characteristics, aggregation patterns and interaction networks of surfactin in these two environments. In addition, validation against experimental neutron reflectometry measurements is performed to confirm the reliability of the molecular models employed, and to allow an atomic characterization of the structures experimentally observed. Methodology The surfactin model used in the present study corresponds to the C15 isoform. Its chemical structure is displayed in Figure 1a-b. It is composed of a heptapeptide ring with the amino acid sequence LGlu-LLeu-DLeu-LAla-LAsp-DLeu-LLeu, and closed by a β-hydroxy fatty acid of 15-carbon atom length. A series of MD simulations were 4 ACS Paragon Plus Environment

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performed at three different values of pH where surfactin is: i) neutral, with both acidic residues Glu and Asp protonated, ii) mono-ionized, with Glu protonated and Asp deprotonated, or iii) di-ionized, with both acidic residues negatively charged. Similar set-up protocols were employed for all the systems considered. For the study of surfactin aggregation in solution, three systems corresponding to each protonation state were set up with 20 surfactin molecules randomly placed in a water box of dimensions 92 x 90 x 84 Å3 built with the Solvate plug-in of VMD.24 For the water/air interface simulations, three systems corresponding to the three protonation states were set up employing 72 surfactin molecules randomly placed at the surface of a water box of dimensions 75x75x35 Å3. Ions were added to each system to achieve neutralization. The resulting systems comprised ~65,000 and ~45,000 atoms, respectively. The CHARMM 27 force field25 was used to describe the protein. The TIP3P model26 was employed for water, and the standard CHARMM parameters were used for ions.27 MD simulations of surfactin aggregation in solution were performed in the NpT ensemble, whereas calculations at the water/air interface consist of 10 ns in the NpT ensemble followed by constant volume simulations (NvT ensemble). All calculations were done using version 2.9 of NAMD.28 Following 10,000 steps of energy minimization, each system was equilibrated and production runs of variable length were produced. Pressure was kept at 1 atm by the Nosé-Hoover Langevin piston,29-31 with a damping time constant of 50 ps and a period of 200 ps. The temperature was maintained at 300 K by coupling the system to a Langevin thermostat, with a damping coefficient of 1 ps-1. The particle mesh Ewald (PME) algorithm was used for evaluation of electrostatic interactions beyond 12 Å, with a PME grid spacing of 1Å, and NAMD defaults for spline and κ values.32 A cut-off at 12 Å was applied to non-bonded forces, and both electrostatics and van der Waals forces were smoothly switched off between the switching distance of 10 Å and the cut-off distance of 12 Å, using the default switching function in NAMD. A Verlet neighbour list with pair-list distance of 13.5 Å was used to evaluate non-bonded neighbouring forces within the pair-list distance only.33 The lengths of covalent bonds involving hydrogen atoms were constrained by the SETTLE algorithm34, 35 in order to be able to use a 2-fs time-step. The multi time step algorithm Verlet-I/r-RESPA33, 35 was used to integrate the equations of motion. Non-bonded shortrange forces were computed for each time step, while long-range electrostatic forces were updated every 2 time steps. Simulations of surfactin in solution were run for 175 ns for the neutral system, and 250 ns for the mono- and di-ionized states, respectively. 5 ACS Paragon Plus Environment

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Radial distribution functions between a selected surfactin molecule and the remaining surfactin molecules were used to check the evolution of the aggregates. Simulations of surfactin at the air-water interface were run for 100 ns each. Volume (V), surface area (SA), and effective radius were calculated using the Voss Volume Voxelator web server (http://3vee.molmovdb.org).36 Radial distribution functions were computed with a plugin implemented in VMD.37 The total simulation time including both systems, in solution and at the water/air interface considering the three pH conditions amounts to ~1.2 µs.

Figure 1. (a) Chemical composition of surfactin. (b) 3-dimensional structure of C15-surfactin. C atoms are depicted in green, N atoms in blue and O atoms in red. Hydrogen atoms are not displayed for clarity. (c) Representative snapshot of the di-ionized surfactin system at the water/air interface, obtained from the MD simulation (Left side). Acidic and non-polar residues are coloured in red and cyan respectively. Water is shown in a continuous transparent representation. Only half of the simulation box (one interface) is used for the calculation of the reflectivity and volume profiles. A scheme of the layers representation used for the calculation of the reflectivity profiles is also shown. Layers 0, 1 and N+1 are used to complement the MD data in order to mimic the experimental set-up (useful in the case of supporting surface experiments). In the present case, layers 0/1 and layer N+1, are used respectively to extend the water and air regions of the MD data to consider their larger sizes in the experimental set-up. Layers 2 to N correspond the MD simulation. (d) Snapshots of the molecular aggregates of the


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three systems studied taken at the end of the simulation. Acidic residues are displayed in red, and non-polar in cyan.

Calculation of the reflectivity and volume fraction profiles was achieved using NeutronRefTools, a VMD plug-in developed in-house.38 In brief, to compute the reflectivity profile from the simulated trajectories, the system is discretized in layers in the direction orthogonal to the interface. Subsequently, for each snapshot, the neutron scattering length density (NSLD) in each layer is calculated using Equation 1: NSLD ( ) = ∑

∙ !

Equation 1

where nk is the number of atoms of type k in a given layer i, bk the coherent scattering length for such atom type, and Vlayer,i the volume of the layer defined by the x and y dimensions of the box, and the bin size. The NSLD in layer i can be also decomposed in the contributions of each molecular specie j in such layer "#$% ( ) = ∑* )+ "#$% ( ) (&'() ).

Equation 2

From the NSLD values contributed by each layer, the NSLD profile along the direction orthogonal to the interface is obtained. Averaging such NSLD profile over the trajectory snapshots gives the NSLDMD. Once the latter is obtained, different methods can be applied to obtain the reflectivity, i.e. the Abeles matrix formalism39 as is the case in NeutronRefTools. However, in order to obtain a reflectivity profile comparable with the experimental measurement, some additional considerations might be needed to match the MD and the experimental setups. In particular, when supporting materials are used in the experiments but are not explicitly included in the simulation, additional layers of defined NSLD and thickness can be added to the NSLDMD profile, generating a modified profile that combines the MD data with layers mimicking the supporting material. For example, when comparing against experimental data obtained using Si/SiO2 as supporting material, layer 0 should be assigned to Si, and layer 1 to the SiO2 surface. In addition, to account for the roughness/diffuseness of the interface between each layer, a Gaussian correction to the Fresnel reflection coefficient,40 called roughness, needs to be considered. Using the modified NSLD, the thickness of every layer and the roughness between each pair of layers, the Abeles matrix formalist is used to obtain a reflectivity profile based on the MD information but within the framework of the reference experimental set-up.


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To filter out false positive results, multiple contrasts are employed and compared with the experimental data. It is worth noting that the deuteration approach available in NeutronRefTools implies substituting hydrogen atoms in specific moieties of the substrate or randomly substituting water molecules by deuterated water molecules up to a given percentage. Seven contrasts were generated from the MD simulations and fitted to the corresponding experimental data. Four of such contrasts correspond to different deuteration levels of surfactin in D2O: i) SHH-D2O, fully protonated surfactin; ii) SDDD2O, fully deuterated surfactin except the exchangeable protons; iii) SHH-dLEU-D2O: fully protonated surfactin except four perdeuterated leucines ; iv) SDD-hLEU-D2O: fully deuterated surfactin except four leucines. The remaining three contrasts correspond to the same deuteration levels of surfactin except for the fully protonated but in null reflecting water (NRW) instead of D2O: v) SDD-NRW; vi) SDD-hLEU-NRW; vii) SHH-dLEU-NRW. A momentum transfer range of 0.05 to 0.4 Å (in accordance to the experimental data), a roughness of 0.2 Å for the layers defined from the MD simulation, and an incoherent background noise of 2.5x10-6 (D2O contrasts) and 6.0x106

(NRW contrasts) were used for the calculation of the reflectivity profiles. One half of

each NSLDMD profile was omitted to avoid duplication of the surfactin water/air interface (see Figure 1c). Layers 0 and 1, correspond to the bulk solvent in the absence of supporting surface, with SLD values of 6.35x10-6Å-2 and 0.0 x10-6 Å-2 for D2O and NRW, respectively. Layer1was 1000 Å thick, and the roughness between layers 0 and 1 was 4.0 Å. Layer N+1 corresponds to vacuum (SLD = 0.0 x10-6 Å-2). SLD values from layer 2 to layer N were computed from the MD trajectories. Volume fraction profiles are useful quantities for the analysis of the distribution of components in a given direction of the system, i.e. orthogonal to an interface. Volume calculations were performed with NeutronRefTools. The program executes the radical Voronoi tessellation implementation of Voro++41 to compute the 3D Voronoi cell of every particle in the system and its corresponding volume. The volume of a given molecular component is then calculated adding the contribution of each constituent particle. However, in the case of the water/air interface system, the large vacuum domain results in an arbitrary overestimation of the volumes of the particles at the interface. Therefore, generation of the 3D Voronoi cells is done for each layer in which the system is partitioned instead of for the entire simulation box. This alternative 8 ACS Paragon Plus Environment

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approach compensates for artefacts in the estimation of Voronoi-based volumes at vacuum/solvent interfaces. This process is then repeated for all the trajectory snapshots and the resulting values are averaged, giving the MD based volume fraction profile. Results and Discussion Aggregates in solution Snapshots at the end of the simulation of the systems studied in solution for the three different protonation states considered are shown in Figure 1d. It can be directly observed that in the three cases surfactin forms aggregates of different association degree. For example, at high values of pH, where both acidic residues are deprotonated (di-ionized surfactin), the molecules organize themselves in two aggregates. In contrast, at lower pH values, a unique structure is formed. These results are expected considering that previous experimental studies have reported dependency of the degree of aggregation of surfactin with pH values.17 During the simulations, the lipid chains of surfactin adopt different conformations, from fully extended to folded, where the hydrophobic tail interacts with the side chains of leucine and valine residues. This behaviour is displayed in all three protonation states. Therefore, the orientation of the lipid chain with respect to the cyclic polypeptide is pH independent. Several conformations of a neutral surfactin molecule are depicted in Figure 2a. The normalized probability distribution displayed in Figure 2 corresponds to the angle α between two vectors connecting the centre of mass of the valine backbone and the centre of mass of the C1, C2 and O atoms in the lactone group, and connecting the centre of mass of the latter lactone atoms and the centre of mass of the C13, C14 and C15 atoms in the lipid tail. Analysis of the normalized probability provides information about the orientation of the lipid tail with respect to the peptide ring (toward the ring: α90). All three protonation states mostly populate values of α higher than 90 degrees indicating a lipid tail oriented away from the peptide ring, although conformations with the tail pointing toward the ring are also sampled albeit less frequently. It is found that the neutral and di-ionized systems are relatively similar with a peak at ~125 degrees, although the neutral system seems lightly shifted to higher values. In contrast, the mono-ionized system has a tendency for higher angles, with a peak centred at 150 degrees. In addition, the polypeptide ring of surfactin adopts a horse-saddle conformation with small deviations from an average value independently of the protonation state the molecule (Figure 2a). The RMSD of the Cα atoms from an 9 ACS Paragon Plus Environment

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average structure for the individual surfactin molecules is found to be 1.0 ± 0.3 Å, 0.9 ± 0.3 Å, and 1.2 ± 0.5 Å for the neutral, mono-ionized and di-ionized protonation states, respectively. These conformations are in agreement with the ones previously reported by modelling and H-NMR experiments.13

Figure 2. a) Snapshots of four different conformations of neutral surfactin aligned using the heptapeptide ring backbone. Carbon, oxygen and nitrogen atoms are presented in green, red and blue respectively. For clarity, hydrogen atoms are not displayed. b) Definition of the angles considered in this study. c) Normalized probability distribution of the α angle for the three protonation states, i) neutral, ii) mono-ionized and iii) di-ionized, obtained from the last 25 ns of each simulation.

For each system, the surfactin radial distribution function measured from a surfactin molecule located in the centre of the aggregate (largest aggregate in the di-ionized system) was evaluated to track the evolution of the aggregates during the simulation (Supplementary Material S1 and S2). The values of the radial distribution functions vary with the pH. However, the overall position of the maximum remains almost identical in the three cases. For the neutral surfactin, a higher peak indicates the presence of a more compact aggregate than in the other two cases where the presence of negatively charged residues has a destabilizing effect. For the mono-ionized case, a less pronounced peak with a slightly broader distribution is observed and the function decays to zero at longer distances in agreement with the idea of a less compact 10 ACS Paragon Plus Environment

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aggregate. The aggregate in the neutral system is formed at short simulation times (~50 ns) while in the mono-ionized system longer simulation times are required (~200 ns). In contrast to the neutral and mono-ionized systems, di-ionized surfactin forms a different class of aggregate which is also formed faster. The reduced extension of the blue traces in Figure S1a would be a consequence of the size, and the lower peak reflects the fact that the di-ionized aggregate is less dense than the neutral and mono-ionized ones. The small aggregation number can be inferred from data in Figure S1b, where the integral of the surfactin radial distribution function, g(R), is presented. For neutral and monoionized systems, the integral converges to the same value, indicating the presence of the same number of particles in the aggregate. Figure 3 compares the ratio between the surface area (SA) and the volume (V) as a function of volume for the three studied protonation states of surfactin to that of an ideal sphere. Overestimated values (red points in Figure 3) with respect to the sphere behaviour indicate higher values of the surface area for a given volume. This effect is attributed to the irregularities present on the surface of the aggregates that result in an increase of the surface area. If this was the case, had the aggregates presented a spherelike shape, then subtracting a systematic area assigned to surface irregularities from the total area should provide a smoother surface and a good fit to the sphere profile. Indeed, when 20 % of the total surface is assigned to irregularities, an improved fit is observed (blue points in Figure 3). Consequently, surfactin molecules in solution form sphericallike shape aggregates for all three protonation states considered. This observation contrasts with SANS results17 that suggested a correlation of the shape of the aggregate with the pH value. Specifically, low pH results in lamellar structures, which evolve to rod-like micellar and spherical micellar structures as the pH increases.17 In addition, a small aggregation number (20±4) and a diameter of 50±5 Å were observed for the micellar structures at pH 7.4.16 For both neutral and mono-ionized systems, complete aggregation is observed during the simulation, up to the maximum number of surfactin molecules in the system (20 molecules). According to SANS experiments, such aggregation level corresponds to spherical micelles, which is indeed what is observed in the simulations. Consequently, the number of surfactin molecules used in the simulations might be limiting the observation of non-spherical aggregates like those described experimentally. Although simulations with higher number of surfactin molecules would be desirable to account for this scenario, the time-scale (microseconds)


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required to study self-assembly of larger structures such as rod-like micelles, is beyond the limit of atomistic MD simulations in explicit solvent. In fact, the use of simplified models (coarse-grained and/or implicit solvation) would be essential as illustrated in Wang et al.42 However, such approaches strongly impoverish the detailed description offered by atomistic simulations like the ones presented in this study.

Figure 3. SA/V ratio vs V for an ideal sphere (black line) and the three studied protonation states of surfactin (red squares). A correction to the SA considering surface irregularities (~20% of total surface) is also shown (blue squares). Distances between the centres of mass of specific residues of surfactin molecules and the centre of mass of the aggregate were analysed, along the MD trajectories, to shed light into the organization of residues inside each aggregate. Figure 4 shows normalized histogram representations of the distance between the centre of mass of the aggregate to the centres of mass of i) surfactin molecules, ii) hydrophobic tails, iii) leucine residues and iv) glutamic and aspartic residues. A clear pattern was found in the charged systems, where residues from the hydrophobic tail are located on the centre of the aggregate, followed by leucine residues and, finally, glutamic and aspartic residues located on the surface of the aggregate. These results corroborate previous observations that place leucine residues on the hydrophobic region, whereas charged residues are on the surface of the aggregate. However, the present data provides additional insight; the hydrophobic nucleus of the aggregate can be stratified in the core formed by the lipid tails and the leucine residues surrounding the tails. In contrast, the neutral system shows a homogeneous distribution of all residues in the aggregate, in agreement with a less hydrophilic character of surfactin at low pH values and, therefore, less separation between the hydrophobic and hydrophilic parts of the molecule.17, 20


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Figure 4. Normalized probability distribution of distances between the centre of mass of the aggregate and the centre of mass of i) surfactin molecules, ii) hydrophobic chain residues (tails), iii) leucine residues and iv) glutamic and aspartic residues (Glu-Asp), for the three cases considered in this study: neutral, mono- and di-ionized surfactin. Finally, to analyse the distribution of the different components of surfactin in the aggregates, volume profiles were calculated over the last 25 ns of each simulation (Figure S3). It was found that tails are located mainly at the centre of the aggregate, as expected due to their hydrophobic character. In agreement with the distribution of the centre of mass of the different components in each aggregate, at neutral pH values, glutamic and aspartic residues are located in the inner regions of the aggregate, whereas at higher pH values, negatively charged Glu and Asp residues are located at the surface. Surfactin at the water/air interface Neutron reflection data previously published16, 17 was used as a reference to compare the outputs from the MD simulations. Neutron reflectivity profiles are shown in Figure 5 and in Figures S2 and S3 in the supplementary material for each of the pH values. Parameters used for the fitting of the MD-based reflectivity profiles to the experimental data can be found in the Methods section. The chi-square value was used to compare the MD-based and experimental reflectivity profiles as a guiding metric for tuning the fitting rather than as a final measurement of the quality of the fit. The contrasts studied in D2O, which are primarily sensitive to the extent of surfactin immersion in water, indicate that the simulations properly reproduce this property, as exemplified by the 13 ACS Paragon Plus Environment

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good fit obtained for the fully protonated contrast (SHH_D2O). However, contrasts studied in fully deuterated surfactin (S-D-D) in NRW, which is sensitive to surfactin coverage at the water/air interface, show that the amount of surfactin at the interface is independent of the pH studied. Some disagreement between simulated and experimental data is observed at pH 6.5 (SDD_NRW contrast in Figure S4). This could be ascribed to the degree of water penetration into the surfactin assemblies that might not entirely be captured in the simulations when surfactin is in the neutral protonation state. Consideration of a higher percentage of water in the surfactin layer simulated leads to an improvement of the fitting (Figure S6). Fits of the partially deuterated surfactin (SDDhLEU_D2O and SHHdLEU_D2O) depend on the coverage, immersion and orientation of surfactin fragments with respect to other fragments and water molecules. These cases are more severe tests for the simulations because capturing the organization of residues at the atomistic level is essential to obtain reasonable fittings. However, SDDhLEU_D2O displays a reasonable fit, in contrast to SHHdLEU_D2O that shows some level of disagreement. This suggests that the orientation of Leu residues (or the exact average position of the Leu residues relative to the rest of the surfactin molecules or waters) might not be captured in the simulations. At this level of isotopic discrimination, capillary wave roughness may also affect the structural correlation normal to the surface in such a way that cannot be dealt with by simple averaging. Another plausible explanation to justify the differences observed could be ascribed to the resolution of the neutron reflectivity technique at the water-air interface (typically dQ/Q ≈ 8%) which is not comparable to the resolution obtained in MD simulations, in particular when subtle differences are crucial like those involved in the study of the location of two amino acids. Overall, good correlation between experimental and simulations results is obtained, validating the models employed in the molecular dynamics simulations. A representative snapshot of the di-ionized system at the air/water interface after convergence was achieved is shown in Figure 1c. Surfactin molecules tend to aggregate at the interface with the polar residues oriented toward the water phase, whereas hydrophobic chains cluster together to avoid water molecules.


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Figure 5. Experimental (black points) and computational (red lines) neutron reflectivity profiles in null reflective water at pH =7.5. This pH value corresponds to the monoionized system. Distributions of absorbed surfactin fragments and water along the direction normal to the air/water interface were reported by Shen et al17 at different values of pH. Although it is well known that changes in the hydrophilicity-hydrophobicity character of surfactin occur in parallel with changes in the pH, experimental measurements indicate that 15 ACS Paragon Plus Environment

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surfactin moves as a whole with the only variation being its relative position with respect to the aqueous sub-phase.17 In the simulated trajectories (Figure S7), hydrophobic surfactin tails get stacked together avoiding the water phase, with a slight amount of water protruding into the interior. Leu residues are also mainly localized in the hydrophobic region like it was observed for the aggregation of surfactin in solution. In contrast, Glu and Asp residues are mainly oriented toward the water phase in line with their hydrophilic character.

Figure 6. Normalized probability distributions of the angles defined in Figure 2b from the last 25 ns of each simulation at the three protonation states considered: neutral, mono- and di-ionized. Top, middle and bottom panels correspond to α, αH and αT angles respectively. The molecular shape and orientation of surfactin at the air/water interface was also analyzed. In order to characterize the orientation of the surfactin molecules, and understand if the surfactin ring stays parallel to the interface or not, and the relative orientation of the hydrophobic tails, the αH and αT angles were computed. These are ´ and the S01 ´ vectors with the z-axis, defined as the angles between the S,-. ´ is defined as respectively. A schematic representation is displayed in Figure 2b. S,-.

the vector formed between the centre of mass of the valine residue and the centre of ´ is defined as the vector mass of C2, C3 and O atoms of the hydrophobic chain. S01


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formed between the centre of mass of C2, C3, O and the centre of mass of C13, C14 and C15 atoms in the hydrophobic chain. Subsequently, αH is the angle defined between the plane of the surfactin polypeptide ring and the z-axis and αT is the angle formed between the hydrophobic tail and the z-axis. Fluctuations of the three angle distributions considered are displayed in Figure 6. Surfactin molecules adopt mainly conformations where the ring stays parallel to the interface (αH ~90 degrees) as previously proposed by Gallet et al21 although some others are also allowed (Figure 6, middle panel). The orientation of the hydrophobic tail was measured using the αT angle (Figure 6, bottom panel). All systems display a dual distribution that at neutral pH is less pronounced, reflecting a lesser hydrophilic character of the heptapeptide ring. In contrast, at nonneutral conditions, two maxima at ~35 and 130 degrees are displayed. An example of a conformation corresponding to each maximum is shown in Figure 7. Values of the αT angle ~35 and 130 degrees correspond to conformations of surfactin in which the lipid tail is folded back to the heptapeptide ring (Figure 7a) or when the hydrophobic chains interact with Leu and Val residues of adjacent molecules (Figure 7b).

Figure 7. Snapshots of a surfactin molecule displaying a conformation in which the lipid tail a) is folded back to the heptapeptide ring (αT=~35 degrees), or b) it interacts with Leu and Val residues from adjacent molecules (αT=~130 degrees). Surfactin molecules are shown in green and cyan.

Conclusions The growing resistance of bacteria against conventional antibiotics has led to an intense search for alternatives such as surfactin. The unique feature of surfactin is its cyclic peptide head group, which is not completely hydrophilic and confers a certain degree of amphiphilic character. A variety of remarkable applications and physiological activities


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have been proposed for surfactin and therefore it has been the focus of increasing experimental and computational efforts. In the present work, computer simulations have been used to characterize the dependency of surfactin aggregation on pH, both in aqueous solution and at the water/air interface. In the modelling of the neutron experimental reflectivity profiles, three different deuterated surfactin models were employed: one perdeuterated, one with the four Leu residues perdeuterated, and one with everything except the four Leu perdeuterated. The neutron reflectivity profiles of these three models in null reflecting water and in D2O with a seventh profile of the protonated surfactin in D2O were calculated at three different pH values, and compared to experimental data already published.16, 17In solution, surfactin forms aggregates of spherical-like shape in all ionization states studied. The size of the aggregates and the fatty acyl chain tail and amino acid distributions in the aggregate respond to pH variability. The charged side chains protrude into the water while the apolar residues reach toward the hydrophobic core. The component distribution within the aggregates is either i) a fatty acyl chain core with a shell of hydrophobic amino acids surrounded by an second external shell composed of polar amino acids (ionized surfactin) or ii) inhomogeneous mixture of fatty acyl chain tails and amino acids (neutral surfactin). At the water/air interface, surfactin molecules form films independently of the environmental pH, in agreement with experimental reports. Comparison between simulations and neutron reflectometry data render similarities in terms of water immersion and coverage, although some discrepancies are found relative to the orientation of Leu residues. Conformational analysis confirms that the surfactin peptidic domain tends to be found parallel to the water/air interface, and fatty acyl chain tails show high flexibility. In summary, the data presented here contributes to gain fundamental atomistic insight into surfactin aggregation and its behaviour at interfaces. ACKNOWLEDGMENTS. We would like to acknowledge the use of computational resources from the EPSRC UK National Service for Computational Chemistry Software (NSCCS), the Hartree Center, and Temple University via a National Science Foundation major research instrumentation grant number CNS-09-58854. This work was supported by the Biotechnology and Biological Sciences Research Council. References 1. Raaijmakers, J. M.; de Bruijn, I.; Nybroe, O.; Ongena, M., Natural functions of lipopeptides from Bacillus and Pseudomonas: more than surfactants and antibiotics. Fems Microbiol Rev 2010, 34, 1037-1062.


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