the effect of structural parameters and positive charge distance on the interaction free energy of...

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The effect of structural parameters and positivecharge distance on the interaction free energy ofantimicrobial peptides with membrane surfaceMohammad Mehdi Ghahremanpoura & Soroush Sardariaa Drug Design and Bioinformatics Unit, Department of Medical Biotechnology, BiotechnologyResearch Center, Pasteur Institute, No. 69, Pasteur Ave., Tehran, 13164, IranPublished online: 12 Mar 2014.

To cite this article: Mohammad Mehdi Ghahremanpour & Soroush Sardari (2014): The effect of structural parametersand positive charge distance on the interaction free energy of antimicrobial peptides with membrane surface, Journal ofBiomolecular Structure and Dynamics, DOI: 10.1080/07391102.2014.893204

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The effect of structural parameters and positive charge distance on the interaction free energyof antimicrobial peptides with membrane surface

Mohammad Mehdi Ghahremanpour and Soroush Sardari*

Drug Design and Bioinformatics Unit, Department of Medical Biotechnology, Biotechnology Research Center, Pasteur Institute, No.69, Pasteur Ave., Tehran, 13164, Iran

Communicated by Ramaswamy H. Sarma

(Received 3 July 2013; accepted 7 February 2014)

Many attempts have been made to find hints explaining the relationship between physicochemical and structural proper-ties of antimicrobial peptides (AMPs) which are relevant to their antimicrobial activities. We here found that there is adifference in the percentages of hydrophobic, hydrophilic, and charged residues between AMPs killing both bacteria andfungi (Group A) and AMPs that only kill bacteria (Group B). The percentage of charged residues in Group A AMPs ishighly elevated, while in Group B the percentage of hydrophobic residues is increased. This result suggests a sequence-based mechanism of selectivity for AMPs. Moreover, we examined how the distance between basic residues affects theinteraction free energy of AMPs with the membrane surface, since most of the known AMPs act by membrane perturba-tion. We measured the average distance between basic residues throughout the 3D structure of AMPs by defining Dprparameter and calculated the interaction free energy for 10 AMPs that interacted with the DPPC membrane using molec-ular dynamics simulation. We found that the changes of the interaction free energy correlates with the change of Dpr bya linear regression coefficient of r2 = .47 and a cubic regression coefficient of r2 = .70.

Keywords: antimicrobial peptides; charge distribution; free energy; molecular dynamics simulation; drug design

Introduction

Antimicrobial peptides (AMPs) play an important role inthe innate immunity (Agerberth et al., 1995; Ganz &Lehrer, 1995; Simmaco, Mignogna, & Barra, 1998). Asthey exhibit not only a rapid kill profile but also a broadspectrum of action against various organisms rangingfrom bacteria and fungi to the enveloped viruses(Hancock, 1997; Hancock & Lehrer, 1998; Hancock &Scott, 2000). Any safe therapeutic agent should be selec-tive toward microbial targets rather than those of the hostcells (Yeaman & Yount, 2003). In the case of AMPs, thecell membrane of the target organisms could be amongsuch targets as AMPs generally induce various mem-brane defects such as pore formation, membrane thin-ning, and promotion of nonlamellar lipid structures(Lohner & Prenner, 1999).

AMPs are enriched in positively charged residueswhich are evolutionary conserved (Agerberth et al., 1995),implying that the peptide charge might be relevant to theAMPs activity (Soltani, Keymanesh, & Sardari, 2007).

Many efforts have been carried out to clarify the roleof peptide charge in modulating the antimicrobial activityof AMPs. For instance, Dathe and his colleagues haveshown that increasing the charge of magainin II analogsfrom +3 to +5 increases their antibacterial activity, while

a net charge increase from +3 to +6 or +7 leads to a lossof antibacterial activity under conditions whereother structural features such as hydrophobicity andhelicity were kept constant (Dathe, Nikolenko, Meyer,Beyermann, & Bienert, 2001). NMR spectroscopy andmolecular dynamics simulation of the peptide analogs ofdefensin have shown that the charge density along thepeptide three-dimensional (3D) structure directly corre-lates with the antimicrobial activity (Bai et al., 2009).Another experiment has revealed that the ratio betweenhydrophobicity and the net positive charge is directlyproportional to the antimicrobial activity (Rosenfeld,Lev, & Shai, 2010). Furthermore, Jiang and coworkershave exhibited that the addition of one positive-chargedresidue on the polar face of antimicrobial peptide L-V13K would result in a 32-fold increase in its membrane dis-ruption activity (Jiang et al., 2008). These debatableresults do not make it clear whether the peptide totalcharge is an increasing or a decreasing factor for antimi-crobial activity of AMPs.

In this study, our main aim was to find the relation-ship between membrane interaction free energy of AMPsand the average distance between the positively chargedresidues along their 3D structure. We also carried outsystematic statistical analyses to answer the following

*Corresponding author. Email: [email protected]

2014 Taylor & Francis

Journal of Biomolecular Structure and Dynamics, 2014http://dx.doi.org/10.1080/07391102.2014.893204

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mailto:[email protected]://dx.doi.org/10.1080/07391102.2014.893204

two questions: (i) what are the differences in amino acidcomposition between the AMPs that kill both bacteriaand fungi and those that kill only bacteria? (ii) is thereany correlation between the physical properties of aminoacids and their occupancies in AMPs amino acidsequence? This work lays a foundation for predicting themembrane interaction free energy of de novo designedAMPs, which in turn aids in selection of the bestdesigned peptide for chemical synthesis and for furtherin vitro and in vivo tests.

Materials and methods

Bioinformatics

We used 92 AMPskilling or inhibiting both bacteria andfungi (Group A), 105 AMPs only killing bacteria(Group B), and 40 transmembrane (TM) helices from 13integral membrane proteins (Group C) to compare theiramino acid compositions. The sequences of AMPs andTM helices were obtained from Antimicrobial PeptideDatabase (Wang, Li, & Wang, 2009) and Protein DataBank, respectively. The complete list of AMPs analyzedin this section is shown in Tables S1 and S2 and themembers of integral membrane proteins are presented inTable S3. Transmembrane regions of each membraneprotein were determined by using TMDET server(Tusnady, Dosztanyi, & Simon, 2005). We also selected16 peptides from group A and 17 peptides from group Bwhose 3D structures were available to find the averagedistance between positively charged residues (Dpr) inAMPs based on their energy-minimized 3D structures.To do this, we built a distance matrix for each peptide.Each element of the equation reference goes here matrixrepresents the distance between two positive residues iand j. This matrix would have a size of N N where Nis the number of positive residues. The distance betweenresidue C atoms is calculated for each pairwise.

Distance Matrix

Ri Rj Rk RnRi 0 dij dik dinRj 0 djk djnRk 0 dknRn 0

where dij is the distance between the C atoms of basicresidues Ri and Rj. Dpr is the average value of the dis-tance matrix. In the next step, 10 peptides (five fromgroup A and five from group B) were randomly selectedto be used in molecular dynamics simulation. Isoelectricpoint and the net charge of the peptides were calculatedby H++ server (Gordon et al., 2005). Hydrophobicity(H), hydrophobic moment (H), and wheel diagramswere also obtained by using HeliQuest webserver(Gautier, Douguet, Antonny, & Drin, 2008).

Molecular dynamics

Simulations of peptidemembrane interaction were per-formed using GROMACS 4.5.3 package program (Pronket al., 2013; Van Der Spoel et al., 2005). Gromos96force field was used to model peptide and lipidmolecules in combination with simple point charge watermodel (Berendsen, Grigera, & Straatsma, 1987; vanGunsteren et al., 1996). As the starting structure ofmembrane bilayer, we used a Dipalmitoylphosphatidyl-choline (DPPC) membrane model consisting of 128 lipidmolecules obtained from Tieleman laboratory (Anzo, deVries, Hltje, Tieleman, & Marrink, 2003). Some mole-cules of water were randomly replaced by ions for neu-tralization and each system also contains 150 mM NaCl.All systems were simulated under NPT ensembles withinperiodic boundary conditions. Peptide, lipids, and solventwere coupled separately to a heat bath at 323 K withtime constant T = .1 ps applying NoseHooverthermostat (Hoover, 1985; Nos & Klein, 1983) Atmo-spheric pressure of 1 bar was maintained under semi-iso-tropic condition using ParrinelloRahman algorithm withtime constant P = 2 ps (Parrinello & Rahman, 1981).Electrostatic interactions were computed by PME andbonds involving hydrogen atoms were restrained usingLINCS algorithm (Darden, York, & Pedersen, 1993;Essmann et al., 1995; Hess, Bekker, Berendsen, &Fraaije, 1997). All systems were energy-minimized usingsteepest descent algorithm and the convergence criterionfor the energy gradient was 1000 kJ mol1 nm1. Equilib-rium MD simulations were performed for 1.5 ns using apositional restraint on peptide heavy atoms with a springconstant of 1000 kJ mol1 nm2 to allow equilibration oflipids, solvent, and ions while keeping the conformationof peptide unchanged. We prepared separated systemsfor 10 individual cationic AMPs. Two independent MDsimulations were performed for each system to provideergodicity. As it can be seen from simulation systemdetails shown in Table 1, a total of 2890 ns of simulationtime are produced for all the peptides.

Free energy

The calculation of the interaction free energy of AMPswith biomembranes can become significantly long byusing methods like umbrella sampling and adaptivebiasing force (Hnin & Chipot, 2004; Ulmschneider,Andersson, & Ulmschneider, 2011). Therefore, thesemethods cannot be used to screen a large database of denovo designed AMPs based on their interaction freeenergy with the target membranes, even though they cal-culate free energy very accurately. Consequently, we hereapplied a simple approach introduced by Ulmschneideret al. (2011) to calculate the interaction free energy ofthe AMPs with the DPPC membrane surface. According

2 M.M. Ghahremanpour and S. Sardari

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to a two-state partition scheme, the pathway taken byAMPs to pass through the membrane surface is con-trolled by the equilibrium free energy between the inter-facial loose interaction (LI) and the tight interaction (TI)states (Figure 1). Therefore, the peptide interaction freeenergy was calculated using vant Hoff relationship:

DGLI!TI RT ln qTIqLI

Here, T is the temperature of the system, R is the gasconstant, qTI and qLI refer to the population of the TI

and LI states, respectively. The free energy was calcu-lated as a function of the peptide position relative to themembrane polar surface along the membrane normal axis(ZCM). To distinguish the LI state from the TI state, thecriterion of ZCM < 0 was used;

Peptide State TI ; ZCM\0LI ; ZCM 0

To increase the statistical precision of the calculation,two independent simulations were run for each system tocollect a large conformational ensemble representing thepeptidemembrane interactions at different states (TI andLI). The ensemble of system conformation was alsoextracted using different time intervals in order to con-sider all possible interactions between the peptide andthe lipid bilayer.

Results

Connecting biological and physical principles

We compared amino acid composition of AMPs with theTM helices of integral membrane proteins. The resultsshow a significant difference in the percentage of hydro-phobic, hydrophilic, and charged residues betweenAMPs and the TM helices of membrane proteins(Table 2). Charged residues are predominant in AMPs,while they are less common in the membrane integrating

Table 1. Simulated systems.

Simulation Time 1 (ns) Time 2 (ns) Number of lipids/waters Ionic strength (mM) Box dimensions (nm)

1MLT 100 200 126 DPPC + 7331W 150 6.44 6.44 102G9P 100 185 126 DPPC + 7316W 150 6.44 6.44 101ZRV 100 185 126 DPPC + 7285W 150 6.44 6.44 101KET 100 195 126 DPPC + 7290W 150 6.44 6.44 101MAG 100 185 127 DPPC + 7349W 150 6.44 6.44 101KXM 100 195 126 DPPC + 7318W 150 6.44 6.44 101YTR 100 185 127 DPPC + 7266W 150 6.44 6.44 101Z64 100 190 126 DPPC + 7356W 150 6.44 6.44 102JPY 100 185 126 DPPC + 7472W 150 6.44 6.44 102RLG 100 185 127 DPPC + 7396W 150 6.44 6.44 10

Note: This table shows details of each simulated system.

Figure 1. Schematic interaction pathway for AMPs that areable to interact autonomously with the membrane surface.Note: The pathway taken by AMPs to interact with themembrane surface at equilibrium consists of two popu-lated states including the loose interaction (LI) and thetight interaction (TI) states. DGz shows the barrier for apeptide interacted with the membrane surface to movefrom the LI state to the TI state and DGLI!TI representsthe free energy difference between these two states. Thepresented snapshot is obtained from the MD-trajectory ofmelittin interacted with the DPPC membrane surface.

Table 2. Amino acid composition.

Occurrence %Group A Group B Group C

Hydrophobic Residues 35.17 38.25 61.34Hydrophilic Residues 20.00 28.86 31.10Charged Residues 44.83 32.89 7.560

Note: Comparison between amino acid compositions obtained inpeptides that kill fungi and bacteria (Group A), peptides that only killbacteria (Group B), and TM helices of integral membrane proteins(Group C).

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proteins (Table 2). Our results also exhibit that basicamino acids have more presence than acidic ones inAMPs (Table 3). This is in agreement with the previousinterpretation that basic residues such as Lys and Arg arecapable to make electrostatic bonds to the phosphategroup of lipid head moieties as well as hydrogen bondto the carbonyl group located deeper in the bilayer, whileacidic residues are only able to interact with cholinehead groups (Johansson & Lindahl, 2008, 2009). Ourresults also show that AMPs killing both fungi and bac-teria (group A) have more positive residues compared tothose that only target bacteria (Table 3). It may be askedwhether amino acids of AMPs were selected based ontheir stability as a result of biophysical properties or theywere only selected to enable AMPs to play their biologi-cal function. The results presented in Tables 4 and 5demonstrate that amino acid composition of TM helices

of membrane integral proteins is approximatelycorrelated with amino acid physical properties. Amongthese properties, membrane-buried parameter andhydropathy show a better correlation by a coefficients ofr2= .530 and r2= .490, respectively. However, the corre-lation coefficient of tendency of amino acids to be foundin the middle part of a transmembrane helix and thetransfer free energy of each residue from cyclohexane towater phase were slightly smaller (r2= .462 and r2

= .409, respectively). Interestingly, we could not find anycorrelation between amino acid composition of AMPsand the mentioned biophysical properties (see Table 5).This observation indicates that amino acid of AMPs

Table 3. Occurrence of basic and acidic amino acids inAMPs.

Occurrence %Group A Group B Group C

Basic Residues 82.43 79.77 81.00Acidic Residues 17.57 20.23 19.00

Note: Proportion of acidic and basic amino acids is also comparedbetween all three peptide groups (A, B, and C).

Table 4. R square.

Occurrence (%) Physical propertiesA B C HMI TATH MV MBP TFE H

I 7.04 6.88 7.14 .31 .60 166.7 1.67 4.92 4.50V 7.31 7.24 8.53 .07 .31 140.4 1.14 4.04 4.20L 11.22 8.51 21.42 .56 .55 166.7 2.93 4.92 3.80F 5.74 5.07 7.73 1.13 .32 189.9 2.03 2.98 2.80C 2.08 1.63 .79 .24 .13 108.5 1.23 1.28 2.50M 1.04 .54 5.36 .23 .10 162.9 2.96 2.35 1.90A 6.52 11.77 11.50 .17 .13 88.6 1.56 1.81 1.80W 1.04 2.53 3.00 .85 .30 227.8 1.08 2.33 .90G 9.92 9.05 7.50 .01 .74 60.1 .62 .94 .40P 2.61 6.94 2.30 .45 2.10 112.7 .76 1.60T 4.17 2.71 6.94 .14 .52 116.1 .91 2.57 .70S 4.69 7.24 5.55 .13 .84 89.0 .81 3.40 .80Y 1.30 2.17 4.76 .94 .68 193.6 .68 .14 1.30Q 1.56 3.44 .70 .58 2.36 143.8 .51 5.54 3.50N 1.04 3.80 1.98 .42 2.05 114.1 .27 6.64 3.50E 1.56 1.99 .20 2.02 2.68 138.4 .23 6.81 3.50D 2.34 1.26 .80 1.23 3.49 111.1 .14 8.72 3.50H 3.65 2.17 .70 .96 2.06 153.2 .29 4.66 3.20K 15.92 12.31 1.20 .99 2.71 168.6 .15 5.55 3.90R 8.87 7.06 1.59 .81 2.58 173.4 .45 14.92 4.50

Note: The comparison between the percentage of each amino acid found in all the peptide groups shown by A, B, and C and physicochemical proper-ties of amino acids. HMI: Hydrophobicity at Membrane Interface (Wimley and White, 1996), TATH: Tendency of Amino acids to be found in the mid-dle of a Transmembrane Helix (Hessa et al., 2005), MV: Molecular Volume (Zamyatnin, 1972), MBP: Membrane Buried Parameter (Woolley et al.,2003), TFE: Transfer Free Energy From cyclohexane to water (Radzicka & Wolfenden, 1988), H: Hydropathy (Kyte & Doolittle, 1982).

Table 5. Physical properties of aminoacids and theiroccurrence in AMPs and transmembrane helices of integralmembrane proteins.

r2

A B C

HMI .000 .000 .260TATH .006 .011 .462MV .000 .051 .014MBP .007 .003 .530TFE .000 .023 .409H .022 .029 .490

Note: This table demonstrates r squares obtained from regression analy-sis between amino acid composition of each peptide group and physicalproperties of amino acids.


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might be selected just to relay their biological function.AMPs spontaneously insert into membranes by a directtransition from water to the lipid bilayer (Song et al.,1996). This may be driven by electrostatic attractionbetween electronegative surface of the membrane(Dempsey, 1990) and net positively charged AMPs. Asmentioned above, the net electropositivity of AMPs orig-inates from a higher occupancy of basic residues thanacidic residues in AMPs. In addition, the charged resi-dues are also advantageous for establishing polarinteraction (e.g. hydrogen bonds) with the membranesurface.

Simulation stability

The time-evolution of the interaction energies of proteinlipid, proteinwater, and lipidlipid, the structural devia-tion of peptides, and the dimensions of the simulationbox were calculated to check the simulation stability.Figure 2 shows energy profiles obtained from our MD-trajectory of peptide melittin interacted with the DPPCmembrane surface during a 100 ns molecular simulationat constant pressure (The energy profiles of the otherpeptides are not shown here due to high similarity of theresults). The system was equilibrated and showed stablebehavior after ~30 ns. We observed that the proteinlipidenergy decreased over time as the proteinwater energyincreased indicating that the peptide moved from thebulk water toward the lipid bilayer to find energeticallyappropriate locations according to their properties(Figure 2). The root-mean square deviations (RMSD) ofthe backbone atoms with respect to the initial structureat 300K are given in Figure 3. The RMSD of allpeptides initially increased to values close to .4 nm after30 ns and then did not alter meaningfully during the restof simulation time. This indicates that the systemsreached an equilibrium after 30 ns (i.e. the peptidesolvent systems were trapped in a local energy minimumbefore they reach more stable conformations), in excel-lent agreement with the results presented in Figure 2.The RMSD results show that all 10 AMPs were stablealong the trajectories.

Figure 4 depicts the time-evolution of the dimensionsof the simulation box along the X and Y axes to detectlarge-scale deformations of the DPPC bilayer. The plotspresented in this figure reveals that the simulation boxremained constant over the simulation time, indicatingthat no membrane deformation occurred. Location of thepeptides relative to the lipid surface and bulk solvent thatdetermines the water/bilayer interface is also provided bycomputing the density map along the Z axis of the simu-lation box for the peptides, lipid head groups, lipid tails,and water (Figure 5). It can be seen that most parts ofall the peptides interacted with both the lipid head groupand the hydrophobic core of DPPC bilayer and thus

allow for the anchoring of the peptides to the zwitter-ionic membrane.

Correlation between membrane interaction free energyand the physical properties of AMPs

We estimated the free energy for 10 AMPs interactingwith a DPPC bilayer using direct all-atom moleculardynamics simulations. Among conventional parameters,we considered hydrophobic moment (H), hydrophobic-ity (H), isoelectric point (pI), and total charge (Q). The

Figure 2. The time-course changes of the proteinlipid, pro-teinwater, and lipidlipid interaction energies over a 100 nstrajectory were monitored to check the simulation stability. Dueto high similarity of the results, data are shown here for onesystem to make diagrams clearer. The presented results areobtained from the MD-trajectory of melittin interacted with theDPPC membrane surface.


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hydrophobic moment is a quantitative measure of thepeptide amphipathicity defined as the vector sum of thehydrophobicities of the individual amino acids (Gautieret al., 2008). While, the peptide hydrophobicity reflectsthe intrinsic tendency of a peptide to transit from anaqueous phase into a hydrophobic phase that play a cru-cial role in the partitioning of AMPs into the lipidbilayer. Figure 6 presents the wheel diagram and thevalue of the mentioned properties for 10 AMPs used forMD-simulations. The averaged hydrophobic moment andhydrophobicity for the AMPs selected from group A are.343 and .374 and are .470 and .449 for the AMPsselected from group B, respectively (Figure 6). Thisimplies that for peptides which kill both bacteria andfungi, hydrophobicity is higher than hydrophobicmoment, while for peptides which only kill bacteria,hydrophobic moment is likely more than hydrophobicity.Previously, we have also shown that the amphipathicityvalue for AMPs, which kill both bacteria and fungi, is inthe range of .45.6 and is in the range of .001.9 forantifungal peptides (AFPs) and we have found no statis-tically significant correlation between these factors andthe minimum inhibitory concentration values (Soltaniet al., 2007). Herein, we introduce a new structuralparameter (Dpr) which indicates the average distancebetween positive charged side chains throughout the 3D

structure of AMPs. According to Figure 7, among all the33 AMPs selected to calculate Dpr, the smallest Dpr isabout .7 nm and the largest one is 1.8 nm. Dpr valuesranging from 1.2 to 1.4 nm are quite frequent, while theDpr values from 1.4 to 1.5 and 1.7 to 1.8 nm show thelowest frequency. The average value of Dpr is 1.17 nmfor group A and for group B is 1.21 nm (Figure 7). Thenormalized number of positive residue per unit length isalso compared between groups A and B. In doing so, thenumber of basic residues is divided by the total residuenumber for each AMP. The results show that each pep-tide of group A has .3 positive residues per unit length,while peptides of group B have .22 positive residues perunit length in average. The obtained p value of .042from t-test analysis also reveals that the difference ofnormalized positive charge number between groups Aand B is significant. These results indicate that the posi-tive sidechains are placed closer to each other throughoutthe 3D structure of peptides killing both fungi and bacte-ria in comparison with the peptides that only killbacteria.

Figure 3. Backbone RMSD values (nm) of the peptide struc-tures in the trajectories over simulation time (ns) with respectto the starting structure. For Group A: Green: 1MLT, Blue:2G9P, Gray: 1ZRV, Violet: 1KET, Brown: 2MAG; for GroupB: Pink: 1XKM, Black: 1YTR, Orange: 1Z64, Cyan: 2JPY,Dark Gray: 2RGL.

Figure 4. Time-course changes of the simulation box dimen-sions along the X and Y axes were monitored to check themembrane stability.Note: This analysis shows no significant membranedeformations that can adversely affect the peptidelipidinteractions. The diagrams presented here are obtainedfrom the MD-trajectory of melittin interacted with theDPPC membrane surface.


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Figure 8 shows the relationship between peptidemembrane interaction free energy and structural parame-ters of AMPs. We observed that the interaction freeenergy of the AMPs with the DPPC bilayer correlates

with the Dpr parameter. This figure, by contrast, showsno rational and predictable tendency between theobtained free energies and the conventional determinantssuch as total charge, hydrophobicity, amphipathicity, and

Figure 5. The density profiles for peptides, lipid head groups, lipid tails, and water along the Z axis of the simulation box are repre-sented here. The colored lines represent the peptides. Five peptides are only shown here to make the diagram clearer. The other fivepeptides also show the same result (not shown here).

Figure 6. Wheel diagram to represent hydrophobic and hydrophilic surfaces of each peptide based on its amino acid sequence. Thedifference in the length of the arrows represents the difference in the hydrophobic moment. Colors of the circles also representthe type of amino acids; blue for basic amino acids, red for acidic residues, yellow for hydrophobic residues, and purple for polaramino acids. The exact values of the hydrophobicity , hydrophobic moment , charge (Q), and isoelectric point (pI) for eachpeptide are shown here.


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the isoelectric point of AMPs. Dpr affects the interactionfree energy of the AMPs with the polar surface of zwit-terionic membranes with a linear regression coefficientof r2 = .47 and a cubic regression coefficient of r2 = .7.The cubic equation fits significantly better than the linearequation, and this implies there might be an extremumpoint for the Dpr. The minimum of the cubic regressionline indicates that the peptide with Dpr = 1.2 nm has the

lowest membrane interaction free energy (Figure 8). Weobserved that increasing of the distance between positiveresidues from .76 to 1.2 nm decreases the AMP-mem-brane interaction free energy with the zwitterionic mem-branes containing phosphatidylcholine, by contrast,increasing of the distance between positive residues from1.2 nm to 1.3 and 1.4 nm increases the free energy(Figure 8).

Figure 7. The frequency histogram of cationic charge distance in natural AMPs. Comparison is made between peptides that kill orinhibit both fungi and bacteria (Group A) and peptides that only kill or inhibit bacteria (Group B).

Figure 8. Change in the membrane interaction free energy in response to change in structural determinants of AMPs. : Hydro-phobicity, : Hydrophobic moment, Q: Charge, pI: Isoelectric point, Dpr: average distance between cationic residues.


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Discussion

Considerable studies have been performed to understandthe molecular principles underlying AMPs action (Dathe& Wieprecht, 1999; Jang et al., 2006; Soltani,Keymanesh, & Sardari, 2008) and many efforts havealso been carried out to increase their selectivity againsta unique target, in particular against fungal organisms(Dennison, Wallace, Harris, & Phoenix, 2005; Kumaret al., 2005). There must exist a way to distinguishAMPs favorable for the bacterial lipid membranes fromthose for the eukaryotic membranes. Amino acidsequence and composition can contribute to the mem-brane interaction energy or membrane insertion propen-sity of the AMPs. We previously provided an amino acidsequence template, according to the analysis of residuedistribution in the N-terminal stretch of helical AMPsfrom natural sources, to increase antifungal selectivity(Soltani et al., 2007). Following this strategy, in the pres-ent study, we divided AMPs into two groups based ontheir selectivity: Group A, which is able to lethally affectboth bacteria and fungi and, group B which only killsbacteria. Our results reveal a disparity between the per-centages of different amino acid types contributed inthese groups. The amino acid proportions are in theorder of charged (44.83%) > hydrophobic (35.17%) >hydrophilic (20.0%) for group A and, by contrast, theorder change to hydrophobic (38.25%) > charged(32.89%) > hydrophilic (28.86%) for group B (Table 2).Consequently, the ratio between the proportions of differ-ent amino acid types could be useful in improving selec-tivity of AMPs. This is in agreement with the previousresults presented by Rosenfeld et al. which showed thatthe ratio between hydrophobicity and positive net chargeis proportional to the antimicrobial activity.

The results obtained from our simulations show thatthe interaction free energy of the AMPs with the zwitter-ionic DPPC membrane has correlation with the averagedistance between positively charge residues throughoutthe peptide 3D structure (Dpr), but it does not correlatewith the peptide total charge, Q (see Figure 8). The un-correlation between the membrane interaction free energyand the AMP total charge is in agreement with the inves-tigation of magainin II analogs, which showed that theability of AMPs net charge to control membrane target-ing is limited (Dathe et al., 2001). Experiments have alsodemonstrated that the peptide total charge is less impor-tant to modulate AMPs capability to penetrate into thezwitterionic membranes because of the weak electrostaticinteractions between AMPs and the neutral surface ofthe zwitterionic membranes (Dathe & Wieprecht, 1999).Our results disclose that Dpr value of 1.2 nm thermody-namically favors to interact with the neutral lipid bilay-ers, while for peptides with the Dpr of .76 nm, it seemslikely that there exists a higher thermodynamics barrier

for their binding to the zwitterionic membranes. Takentogether, this refers to the dominant role of the distancebetween positive charge residues, Dpr, in modulating theAMP-membrane interactions. Our results also show noacceptable correlations between the interaction freeenergy of the AMPs with the DPPC bilayer and theirhydrophobicity and hydrophobic moment. This is in con-trast with the previous data reported by Dennison et al.who showed that the increased hydrophobicity of peptidepromotes the affinity of AMPs for neutral membranes(Dennison et al., 2005). As the full hydrophobic contactsmay be established only after peptides inserting into theinternal core of the membrane, the hydrophobicity ofpeptides probably does not play as important role asdoes the electropositivity of peptides during their interac-tion with the membrane polar surface.

In conclusion, the results presented in this studyrevealed a discrepancy between amino acid compositionof antibacterial peptides and AFPs that can be used todistinguish AMPs designed to kill/inhibit bacteria fromAMPs designed to kill/inhibit eukaryotic infectiousmicro-organisms. This study also introduces a novel andapplicable structural determinant, the average distancebetween basic side chains (Dpr), which can be used toimprove antimicrobial activity, even though it is proveddifficult to make a model system to describe the mecha-nism of action of AMPs.

Supplemental data

Table S1 shows Antimicrobial Peptide Databases IDnumber of antimicrobial peptides that are able to kill/inhibit bacteria and fungi (Group A). Table S2 demon-strates Antimicrobial Peptide Databases ID number ofantimicrobial peptides, which only kill/inhibit bacteria(Group B) and Table S3 presents PDB codes of mem-brane proteins (Group C) used in this study. Supplemen-tal data for this article can be accessed here. http://dx.doi.[10.1080/07391102.2014.893204].

AcknowledgementsWe gratefully acknowledge to Ghazaleh Ghavami for her helpin running of simulations.

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Abstract Introduction Materials and methods Bioinformatics Molecular dynamics Free energy

Results Connecting biological and physical principles Simulation stability Correlation between membrane interaction free energy and the physical properties of AMPs

Discussion Supplemental dataAcknowledgementsReferences

the effect of structural parameters and positive charge distance on the interaction free energy of...

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