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Staphylococcus aureus δ-toxin in aqueous solution: Behavior in monomeric and multimeric states
Biophysical Chemistry 227 (2017) 21–28
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Staphylococcus aureus δ-toxin in aqueous solution: Behavior in monomeric and multimeric states
MARK
Maria Carolina de Araujo Meloa, Cláudio Gabriel Rodriguesa, Laercio Pol-Fachinb,c,⁎ a b c
Department of Biophysics and Radiobiology, Federal University of Pernambuco, Recife, Brazil Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil Aggeu Magalhães Research Center, Oswaldo Cruz Foundation, Recife, Brazil
A R T I C L E I N F O
A B S T R A C T
Keywords: α-Helix Hemolytic Membrane Methanol Peptide Tetramers
δ-Toxin is a 26 amino acid peptide capable of lysing several mammalian cell types and subcellular structures. Structurally, δ-toxin predominantly exhibits a α-helical secondary structure in membranes but, in aqueous solution, it adopts varying helical content. As no atomic-level data is available for this peptide in aqueous solutions and for the water-to-membrane transition, this work aims to characterize δ-toxin behavior in these conditions through molecular dynamics simulations in triplicates employing four different parameter sets. Our results, validated on previous experimental data, suggest that the peptide has from 4 to 16 residues folded as αhelix in aqueous solution, and a water-to-membrane foldamer comprising residues 14–18. Considering a previously proposed stable tetramer form in aqueous solutions, protein-protein docking and molecular dynamics studies were performed, suggesting that δ-toxin increases its α-helical content in such organization. The obtained results are expected to contribute in future studies on δ-toxin aggregation and interaction to biomembranes.
1. Introduction Staphylococcus aureus is a ubiquitous, Gram positive, non-motile coccus microorganism, which grows as grape-like clusters [1]. It is a natural human commensal, found for instance in the skin and throat [1]. As a pathogen, it is a leading cause of hospital-associated and community-associated infections worldwide [2]. Mostly all strains produce and secrete several exoproteins to convert local host tissues into nutrients for its growth [3]. As a consequence, such molecules will contribute to the bacteria colonization on target tissues and cause diseases in mammalian species [3]. Such exoproteins include several enzymes and cytotoxins, as nucleases, proteases, lipases, and four hemolysins, named as α-, β-, γ- and δ-toxins [3]. While the β-toxin hemolytic activity is attributed to the enzymatic degradation of membrane sphingolipids, the α- and γ-toxins are known to form βbarrel pores in host cell membranes [3]. On the other hand, regarding δ-toxin mechanism of action, two distinct sets of events were proposed, in which the peptide displays a concentration-dependent activity [4]. Thus, δ-toxin would either act as a surfactant to permeabilize and disrupt the cell membrane at small concentrations [5,6], or to assume a multimeric pore conformation at higher concentrations [7–10]. The δ-toxin is a 26 amino acid peptide, displaying limited or
⁎
nonexistent antimicrobial activity against bacteria [11,12]. However, it lyses and disrupts bacterial protoplasts, spheroplasts, lysosomes and lipid spherules [12]. Also, it is active against a wide range of mammalian cell types, as erythrocytes, and is capable of lysing subcellular structures, as membrane-bound organelles [4]. Structurally, δ-toxin predominantly exhibits a α-helical secondary structure in phospholipid bilayers and membrane-mimetic environments, with 15–20 folded residues [13–17]. In fact, previous MD simulation studies have explored and validated such aspects, as evaluating δ-toxin interaction with POPC lipid bilayers in atomic-level [18], and coarsegrained resolutions [19]. In aqueous solution, however, it adopts varying helical content, which is dependent on its concentration [20,21,11,22]. Although this much knowledge about δ-toxin behavior, there is no structural, atomic-level data available for this peptide in aqueous solutions. In this context, the present work intends to characterize the structure and conformation of δ-toxin in aqueous solutions and in water-to-membrane transition. For this purpose, molecular docking and unbiased molecular dynamics (MD) simulations were performed for this peptide employing four different force fields, in triplicates for each parameter set. Validation on these simulations was achieved with δtoxin structural data on aqueous and membrane-mimetic environments.
Corresponding author at: Aggeu Magalhães Research Center, Oswaldo Cruz Foundation, Recife, Brazil. E-mail address: laercio.fachin@cpqam.fiocruz.br (L. Pol-Fachin). URL: http://www.dqfnet.ufpe.br/biomat (L. Pol-Fachin).
http://dx.doi.org/10.1016/j.bpc.2017.05.015 Received 3 February 2017; Received in revised form 15 May 2017; Accepted 24 May 2017 Available online 26 May 2017 0301-4622/ © 2017 Elsevier B.V. All rights reserved.
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Based on the obtained results, δ-toxin structural behavior in aqueous solution in both monomeric and tetrameric forms could be obtained, while the foldamer for the water-to-membrane transition could be proposed. Additionally, while offering a global view on MD force field behavior on peptide folding, these data are expected to contribute in future studies on δ-hemolysin aggregation and interaction to biomembranes.
AutoDock Tools framework was employed for processing data [40], while Gasteiger partial charges were chosen. The grid maps were calculated using AutoGrid [41], in which a grid map with 126 × 126 × 126 points and a grid-point spacing of 0.325 Å was centered at each peptide center of mass. The Lamarckian genetic algorithm was used in a total of 200 runs, each one with an initial population of 150 random individuals, with a maximum number of 2.5 × 107 energy evaluations, a maximum number of 27,000 generations with mutation and crossover rates of 0.02 and 0.8, respectively. The atomic coordinates of the lowest energy conformations were clustered based a root mean square deviation (RMSD) of 2.0 Å, and sorted in order of increasing energy. The 144 output dimeric forms of the peptide were further clusterized, in which four structures were obtained. These dimers were subsequently employed in additional protein-protein docking studies, in order to generate δ-toxin tetramers, using the same docking protocol.
2. Materials and methods 2.1. Nomenclature and software The recommendations and symbols of nomenclature as proposed by IUPAC [23] were used. The manipulation and visualization of structures, as well as structure figures, were performed with VMD [24]. The secondary structure content analysis was performed with the Dictionary of Secondary Structure of Proteins (DSSP) [25] and via PDBsum generate tool [26]. All the MD calculations and remaining analysis were performed using the GROMACS package, version 4.5.1 [27], employing four different force fields: AMBER99SB-ILDN [28], CHARMM36 [29], GROMOS54A7 [30], and OPLS-AA [31].
3. Results and discussion 3.1. Monomeric δ-toxin in aqueous and membrane-mimetic environments The starting δ-toxin structures for MD simulations comprise a αhelix conformation, folded from residues 2 to 22, thus being ~80% helical. Such behavior was obtained in 2:1 proportion of methanol and water [13]. In pure methanol, which also partially mimics the membrane environments, δ-toxin is expected to present from fourteen to nineteen folded residues, thus being 54–73% helical [13–17]. Based on this data, from the four parameter sets employed during MD calculations in methanol, the correct number of folded residues was better achieved using CHARMM36 and GROMOS 54A7 force fields (Fig. 1B and D), which mostly maintained the NMR-obtained helical content (Fig. 1D, and Supporting Information, Fig. S1). On the other hand, the AMBER99SB-ILDN and OPLS-AA simulations provided less organized patterns for the peptide (Fig. 1B), with a reduced and more diffuse α-helical content (Fig. 1D). The α-helix and coil contents were in general kept constant during the final half of simulations (Supporting Information, Fig. S2). It should be highlighted, however, that in only specific MD simulations the secondary structure content still appear to vary significantly, as in model 2 AMBER99SB-ILDN calculation. As well, it is noteworthy that MD simulations employed with CHARMM force fields mostly showed no variation within this time period. Overall, the employed force fields sampled 30–75% of α-helix (Table 1) and 19–27% of coils for the entire peptide (Table 1), although some differences were observed among the replicas of the same force field (Supporting Information, Table S1). Still, some amount of bend and turn secondary structures types were observed during AMBER99SBILDN and OPLS-AA simulations (Table 1). The helical content was always higher in methanol than in water simulations (Fig. 1C–D). In aqueous solution, thus, the peptide has undergone some significant conformational modifications (Fig. 1A). In all simulations, a reduction in secondary structure content is observed for δ-toxin, in accordance to previous experimental data [21,11], and differences in the resulting structures were observed between the four employed force fields (Fig. 1A and B). As occurred using methanol as solvent, the simulations using the GROMOS 54A7 and CHARMM36 force fields provided more organized structures, in which the peptide was mostly folded from residues 8 to 22 (Fig. 1C, and Supporting Information, Fig. S1). Such structures, thus containing 15 folded residues, represent δ-toxin helicity of 58%, in agreement with a previous 53% helicity measurement from circular dichroism [20]. Moreover, the AMBER99SB-ILDN and OPLS-AA parameter sets also present equivalent profiles for δ-toxin, in which a small α-helical content could be observed (Fig. 1C). As occurred in MD simulations in methanol, the α-helix and coil contents were kept constant during the final half of simulations (Supporting Information, Fig. S3), in which variation in these secondary structure contents occurred in lesser extent
2.2. MD simulations The δ-toxin three-dimensional structure was retrieved from PDB ID 1DTC [13], containing the amino acid sequence MAQDIISTIGDLVKWIIDTVNKFTKK. It contains twenty models derived from nuclear magnetic resonance (NMR) experiments, which were clustered in order to obtain the three most representative structures for the peptide. As a consequence, three replicas for the monomeric δ-toxin MD simulations were studied, starting from each of the selected models 2, 3 and 8. Thus, twenty four systems were studied, in which each of the four parameter sets were used to study each of the three above cited models in cubic boxes, surrounded by water or methanol. Methanol was chosen as a solvent due to its capability to partially mimic the membrane environment, as previously performed in δ-toxin circular dichroism experiments [13–17], and in order to validate the peptide conformation as obtained from each parameter set. Periodic boundary conditions were employed, with at least 10 Å distance from the outside of the structure and the box edge. The SPC water model [32] was used for GROMOS 54A7 simulation in water, TIP3P water model [33] for AMBER99SBILDN and CHARMM36 MD calculations and TIP4P [33] water model for simulations with OPLS. The Lincs method [34] was applied to constrain covalent bond lengths, allowing an integration step of 2 fs after an initial energy minimization using Steepest Descents algorithm. Electrostatic interactions were calculated through the Particle-Mesh Ewald method [35]. The temperature was maintained at 310 K by coupling the system with V-rescale thermostat [36] with a relaxation time of 0.1 ps. Pressure was kept at 1 bar using a Parrinello-Rahman barostat [37,38] via an isotropic coordinate scaling with a coupling constant of 2.0 ps and a compressibility of 4.5 × 10− 5 bar. A 1 ns MD simulation with position restraints was performed as an equilibration period, and was not taken into account to calculate the average ensemble properties. Subsequently, all simulations were further extended to 200 ns. The δtoxin tetramers, obtained from molecular docking studies (described below), were also submitted to MD simulations in water using the same MD protocol. 2.3. Molecular docking As δ-toxin tetramers were previously proposed as stable multimeric forms of the peptide in aqueous solutions [39], molecular docking studies were employed to generate an ensemble of structures that may fulfill such observations. Thus, at first, each of the twenty NMR models contained in PDB ID 1DTC was employed both as a receptor and a ligand in protein-protein docking studies using Autodock4 [40]. The 22
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Fig. 1. MD simulations of δ-toxin monomers in water and methanol environments. In (A–B), representative structures for the trajectories are shown in ribbon diagram. In (C–D), the percentage of time in which each residue showed a helical character is presented, as analyzed for the second half of simulations. The graphs represent an average value for the triplicates MD simulations.
3.2. Force field differences and δ-toxin foldamer
Table 1 Secondary structure content for the monomeric δ-toxin MD simulations. Secondary structure type
Coil β-Bridge Bend Turn α-Helix 310-Helix a
As a general feature, the higher α-helical contents were sampled during MD trajectories using CHARMM36 and GROMOS 54A7, while less structures peptides were described by AMBER99SB-ILDN and OPLS-AA (Fig. 1). As such behavior is maintained in both employed solvents, it is unlikely that SPC and TIP3P water models, or the methanol solvent parameters are playing a major role over such different α-helical contents. In fact, the CHARMM family of force fields has been found to sample the Ramachandran α-helical regions in a higher proportion when compared to the AMBER family of force fields for alanine dipeptides (as reviewed in [42]). Also, a similar profile was previously observed in simulations of a fragment of human α-lactalbumin, in which the α-helical content was higher for CHARMM36, followed by AMBER99SB and OPLS-AA [43]. While, for that specific case, the results obtained with AMBER99SB matched with the experiments, those provided by CHARMM36 and GROMOS 54A7 for S. aureus δ-toxin presented better agreement with the available experimental data in the present work. Thus, instead of pointing for better or worst parameter set, the here presented data suggest that CHARMM36 and GROMOS 54A7 are better suited for δ-toxin MD simulations in comparison to AMBER99SB-ILDN and OPLS-AA.
Average percentage of secondary structure (methanol/water)a AMBER99SBILDN
CHARMM36
GROMOS 54A7
OPLS-AA
19/34 1/0 6/15 12/26 57/16 5/9
20/26 0/0 2/4 3/6 75/63 0/1
19/29 0/0 3/9 4/12 74/48 0/2
27/28 0/2 17/13 19/26 30/17 7/14
The presented values were averaged from the three MD replicas.
within this time period. In fact, from 16 to 63% of α-helix was sampled during simulations in water (Table 1), which suggests that the peptide may show from around 4 to 16 residues folded as α-helix in aqueous solution. On the other hand, around 30% of loops were sampled by the employed parameter sets (Table 1) and, as occurred in methanol, a significant amount of bend and turn secondary structure types were observed (Table 1).
23
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helical and, at higher concentrations and aggregation states, a buried and immobilized Trp15 residue [4]. Thus, in order to generate atomicresolution models for δ-toxin tetrameric organization, the twenty NMR models contained in PDB ID 1DTC were employed in protein-protein molecular docking studies, each as a receptor and a ligand. The 144 output dimeric forms of the peptide were clustered, outputting four different structures, one containing a parallel and the other three with an antiparallel orientation between each monomer. Such dimers were then employed as receptors and ligands in additional protein-protein docking studies, generating sixteen δ-toxin tetramers. Subsequently, eight of them were discarded due to a small interaction interface between their two-composing dimers. Thus, eight tetramer structures remained for further analysis (Fig. 3). Among the molecular docking output structures, one contained four parallel α-helices (Fig. 3D), while the others contained a perpendicular or more diffuse arrangements between their two-composing dimers (Fig. 3A–C and Fig. 3E–H). In order to evaluate the behavior of such tetramers in aqueous solutions, and assess whether the protein-protein contacts derived from molecular docking are maintained in such conditions, those models were refined through MD simulations using both CHARMM36 and GROMOS 54A7 force fields (Fig. 3). As a result, all sixteen systems maintained a tetrameric organization for the peptides, although most of them experienced significant conformational changes with respect to their starting structures. Moreover, the δ-toxin tetrameric organization appears to be still not sufficient to keep the peptides in a full α-helix folding (Fig. 3B, Fig. 3D–E, Fig. 3G), although an increase in the α-helical content is observed in the tetramers when compared to the monomeric forms (from 63% to ~79% for CHARMM36, and from 48% to ~72% for GROMOS 54A7) (Table 1 and Table 3). Regarding the solvent exposure of δ-toxin composing residues, a quite common feature is observed for the peptides, especially for the region comprising residues 9–20. Specifically, hydrophobic residues tend to be buried in the quaternary structure, including Ile9, Gly10, Leu12, Val13, Ile16, Ile17, and Val20, while the charged residues Asp11, Lys14 and Asp18 are highly solvent exposed (Fig. 3, lower graphs). Furthermore, the solvent accessible surface area (SASA) results are in agreement with previous conclusions based on tryptophan fluorescence probing, in which this specific multimeric state does not to contain a buried and immobilized Trp15 residue [10]. Also, as expected, an increase in the per-residue SASA in the vicinity of secondary structure losses can be observed, for instance, for Ile9 (Fig. 3C, GROMOS red curve), Leu12 (Fig. 3B, GROMOS black curve), or for a group of residues (Fig. 3E, CHARMM red and blue curves). In fact, besides N- and C-terminal unfolding, punctual losses of α-helical content mostly occur in the region 6–12, thus generating bends in the peptide structure (Fig. 3).
Table 2 Clustering analyses performed on the monomeric δ-toxin MD simulations. Cutoff (Å)
0.50 1.00 1.50 2.00 2.50 3.00
Average number of different clusters (methanol/water)a,b AMBER99SB-ILDN
CHARMM36
GROMOS 54A7
OPLS-AA
5001/5001 3910/4510 282/755 3/34 1/2 1/1
5001/5001 2146/4073 101/661 1/18 1/1 1/1
5001/5001 4883/4936 878/1959 30/108 1/3 1/1
5001/5001 2569/2720 253/37 5/1 1/1 1/1
a
The presented values were averaged from the three MD replicas. The total number of structures in the trajectories were reduced by a factor of 10 to allow matrixes calculations. b
Regarding the simulations in both water and methanol, all structures sampled in each MD simulation can be distinguished using a RMSD of 0.5 Å, but each entire trajectory form a single conformational cluster employing a 3 Å RMSD cutoff (Table 2). Markedly, the GROMOS 54A7 force field is capable of sampling a higher number of different structures when compared to all other three parameter sets in aqueous solutions (Table 2, and Supporting Information, Table S2), in spite of presenting similar RMSD values with respect to the starting structure, when compared to the other parameter set (Supporting Information, Fig. S4). Additionally, a less number of clusters are observed for the CHARMM36 force field when compared to the other force field (Table 2, and Supporting Information, Table S2). Such feature becomes evident while observing the few secondary structure variation in the final half of MD simulations employing such force field (Supporting Information, Figs. S2 and S3). Therefore, the GROMOS 54A7 force field is capable of sampling a higher number of structures when compared to the other evaluated parameter sets, employing the same time scale, and providing results that fit better with the available experimental data for δ-toxin. However, it should be highlighted that both CHARMM36 and GROMOS 54A7 parameters adequately described the peptide behavior in methanol and aqueous solutions. Based on this observation, the subsequent MD simulations were only performed with these force fields. In this context, twelve additional simulations were performed employing both the CHARMM36 and GROMOS 54A7 force fields on the most representative structures from AMBER99SB-ILDN and OPLS-AA MD simulations in water. Such simulations aimed to evaluate whether δ-toxin folds back to high α-helical contents from less structured conformations (Fig. 2), and which secondary structure determinants would facilitate the refolding process. For the MD simulations starting from three out of six structures, a significant increase in helical content was observed either only for GROMOS (Fig. 2B and C) or for none of the parameter set (Fig. 2D). On the other hand, a higher solidity can be observed among the other MD simulations, in which a gain in secondary structure is observed in both CHARMM36 and GROMOS 54A7 force fields (Fig. 2A, E and F). In such cases, the starting α-helixes comprised residues 10–18 (Fig. 2A), 13–18 (Fig. 2E), and 14–21 (Fig. 2F), thus containing from 5 to 8 folded residues, and resulted in structures showing from 11 to 21 folded residues (Fig. 2). A common feature among those starting structures is the region containing residues 14–18 folded as α-helix, which suggests that the peptide region ranging from 14 to 18 is a foldamer, that is, a nucleation point for δ-toxin folding into a full α-helix upon membrane insertion.
3.4. Insights into δ-toxin aggregation in solution and membranes As previously proposed, the first step of self-association in aqueous solutions is driven by hydrophobic interactions between the apolar sides of the peptide [10]. According to monomeric δ-toxin MD simulations, from 4 to 16 residues are folded as α-helix (Table 1), in peptide regions possibly comprising from Thr8 to Lys22, which mostly remained folded during CHARMM36 and GROMOS 54A7 trajectories (Fig. 1). Such results, together to the here proposed foldamer region (from residues 14–18) and the MD simulations for tetramers, suggest that the interactions between δ-toxin apolar sides should mostly comprise residues Ile9, Gly10, Leu12 and Val13, but mainly Ile16, Ile17, and Val20, located at the central peptide region. Moreover, previous tryptophan fluorescence probing studies also proposed that further oligomerization involves δ-toxin polar side via electrostatic interactions [10]. As already presented, during the MD trajectories, Asp11, Lys14, Trp15 and Asp18 are highly solvent exposed, and may be available for protein-protein contacts in higher aggregation complexes.
3.3. Protein-protein docking and dynamics The S. aureus δ-toxin has been proposed to be increasingly selfaggregated with increasing peptide concentration [22,4]. In this context, the first aggregation state would occur at 2 μM concentration, as a tetramer [22]. The main characteristics of the peptides in such aggregates would include δ-toxin monomers being fundamentally α24
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Fig. 2. Monomeric δ-toxin behavior as observed through MD simulations starting from the most representative structures of AMBER99SB-ILDN and OPLS-AA MD simulations in water. The representations refer to the secondary structure content for the starting structure (cyan), and for the most representative structures obtained from MD simulations employing CHARMM36 (red) and GROMOS 54A7 (green) force fields. In the graphs, the percentage of time in which each residue showed a helical character is presented, as analyzed for the second half of simulations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
upon insertion in membranes, such behavior is expected to intensify, while an inversion should occur in relation to the tetramers profile observed in aqueous solution in the present work. Thus, hydrophilic amino acid residues (as Asp11, Lys14, Asp18 and Lys22) should probably interact more intensely in the peptide-peptide interface or face to the water lumen of the pore, and hydrophobic residues (as Ile6, Leu12, Val13, Ile17 and Val20) would face the phospholipids acyl chains. Previous δ-toxin membrane-inserted models, generated based on simulated annealing and/or MD calculations, point to the same assumptions, either observing Asp11 and Lys14 pointing to the water lumen of the pore [44], or inter-peptide salt-bridges between Asp and Lys residues [19]. Altogether, significant conformational modifications are expected between δ-toxin multimers in the water-to-membrane transitions. However, further studies, especially employing experimental techniques, are still required to confirm and provide further information regarding δ-toxin insertion into biomembranes.
Independently on the number of monomers involved, a structural model for aggregated species with stacked antiparallel amphipathic rods has been proposed for δ-toxin [22,4]. Although such organization was not observed in the present work, an anti-parallel arrangement between monomers was indeed stable as an output of MD simulations (Fig. 3A, GROMOS). In fact, possibly much longer time scales (microsecond-long MD simulations) should be required to reach a unique, fully stable tetrameric form for δ-toxin from the five structures obtained. However, the here obtained results are in accordance with previous experimental data, providing new structural and conformational data on multimeric δ-toxin states not accessible from such methodologies, and considering both spatial and temporal variables. Moreover, based on the performed calculations, insights into δ-toxin conformational transition while moving from the water environment to be membrane-inserted may be suggested. In this context, in small concentrations of δ-toxin, although a minimum foldamer ranging from 14 to 18 is required, the peptide may contain up to fourteen folded amino acid residues, as such amount of secondary structure is the interface between the observed α-helical content in methanol and water (Fig. 1C–D and Table 1). As well, previous MD simulations studies observed that the peptide tends to form dimers on the water-tomembrane interface [19], in which hydrophilic residues (as Ile, Trp and Leu) can enter deeper in the membrane bilayers than hydrophilic residues (as Asp and Lys). Therefore, in higher peptide concentrations,
4. Conclusions In this work, δ-toxin has been studied through long time scales MD simulations with four different parameter sets, in triplicates. The obtained data was validated based on previous circular dichroism, tryptophan fluorescence probing and NMR data, in terms of the conformational profile adopted by the peptide. In aqueous solution, 25
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Fig. 3. Molecular docking and MD simulations of δ-toxin tetramers. In (A–H), the upper structures represent the eight molecular docking models, while the lower structures are the most representative structures obtained from MD simulations employing CHARMM36 (left) and GROMOS 54A7 (right) force fields. In the graphs, the percentage of time in which each residue showed a helical character is presented, together to the per-residue solvent accessible surface area (SASA).
was proposed to be the region comprising residues 14–18. Methodologically, among the employed force fields, the GROMOS 54A7 and CHARMM36 provided the results in better agreement with the experiments, for both water and methanol environments.
our results suggest that the peptide has from 4 to 16 residues folded as α-helix, depending on the employed force field. In this context, the involved amino acid residues, according to MD simulations, range from Thr8 to Lys22. Additionally, the δ-toxin water-to-membrane foldamer 26
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References
Table 3 Secondary structure content for the tetrameric δ-toxin MD simulations. Secondary structure type
Coil β-Bridge Bend Turn α-Helix 310-Helix
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Buckelew, A.W. Bernheimer, Surface properties of membrane systems. Transport of staphylococcal delta-toxin from aqueous to membrane phase, Biochim. Biophys. Acta 465 (1977) 378–390. [22] E. Thiaudiere, O. Siffert, J.C. Talbot, J. Bolard, J.E. Alouf, J. Dufourcq, The amphiphilic alpha-helix concept. Consequences on the structure of staphylococcal delta-toxin in solution and bound to lipids, Eur. J. Biochem. 195 (1991) 203–213. [23] IUPAC-IUBMB Commission on Biochemical Nomenclature, Nomenclature of carbohydrates, Pure Appl. Chem. 68 (1996) 1919–2008. [24] W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics, J. Mol. Graph. 14 (1996) 33–38. [25] W. Kabsch, C. Sander, Dictionary of protein secondary structure: pattern-recognition of hydrogen-bonded and geometrical features, Biopolymers 22 (1983) 2577–2637. [26] R.A. Laskowski, PDBsum new things, Nucleic Acids Res. 37 (2009) D355–D359. [27] B. Hess, C. Kutznet, D. van der Spoel, E. Lindahl, GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation, J. Chem. Theory Comput. 4 (2008) 435–447. [28] K. Lindorff-Larsen, S. Piana, K. Palmo, P. Maragakis, J.L. Klepeis, R.O. Dror, D.E. Shaw, Improved side-chain torsion potentials for the amber ff99SB protein force field, Proteins 78 (2010) 1950–1958. [29] J. Huang, A.D.J. MacKerell, CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data, J. Comput. Chem. 34 (2013) 2135–2145. [30] N. Schmid, A.P. Eichenberger, A. Choutko, S. Riniker, M. Winger, A.E. Mark, W.F. van Gunsteren, Definition and testing of the GROMOS force-field versions 54A7 and 54B7, Eur. Biophys. J. 40 (2011) 843–856. [31] W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids, J. Am. Chem. Soc. 118 (1996) 11225–11236.
Average percentage of secondary structure (CHARMM36/ GROMOS 54A7)a A
B
C
D
E
F
G
H
20/ 22 0/0 1/2 2/2 77/ 74 0/0
15/ 17 0/0 1/1 5/3 76/ 78 3/1
15/ 24 0/0 1/3 2/7 82/ 66 0/0
17/ 18 0/1 1/2 2/3 80/ 75 0/1
14/ 23 0/0 1/5 4/4 81/ 68 0/0
16/ 22 0/0 1/3 3/5 80/ 69 0/1
18/ 19 0/2 1/2 2/4 79/ 72 0/1
17/19 0/0 1/3 3/5 79/73 0/0
a
The presented values were averaged from the second half of MD simulations, in which the A-H letters refer to each of the tetrameric forms shown in Fig. 3.
Nevertheless, the GROMOS parameter set presented a higher conformational sampling in the same timescale, when compared to the other evaluated force fields, which facilitated the observation of the δ-toxin folding and unfolding process. Subsequent protein-protein molecular docking studies on the NMRderived structures generated eight different tetramer structures, which were submitted to MD simulations refinement. As a consequence, the peptides α-helical content increased in this multimeric organization, while δ-toxin apolar faces were buried from water. Still, further studies are required to evaluate δ-toxin behavior in higher aggregation complexes. Altogether, the obtained results offer a global view on MD force field behavior on peptide folding and are expected to contribute in future studies on δ-toxin interaction to biological membranes. Abbreviations MD NMR RMSD RMSF SASA
molecular dynamics nuclear magnetic resonance root-mean-square-deviation root-mean-square-fluctuation solvent accessible surface area
Conflicts of interest No conflicts of interest to report. Authors contribution M.C.A.M. and L.P.F. performed and analyzed molecular dynamics simulations. L.P.F. designed research and supervised the project. M.C.A.M., C.G.R. and L.P.F. wrote the paper and approved the final version of the manuscript. Acknowledgments This work was financially supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq Universal, grant number 454470/2014-2), by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – MEC), and by Fundação de Amparo a Ciência e Tecnologia do Estado de Pernambuco (FACEPE, grant number APQ-0398-1.06/13). The authors would like to gratefully acknowledge the Biomaterials Modelling Group (BIOMAT) and the Centro Nacional de Supercomputação (CESUP) from Universidade Federal do Rio Grande do Sul (UFRGS) for computational resources. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.bpc.2017.05.015. 27
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polypeptide, FEBS Lett. 130 (1981) 257–260. [40] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson, AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility, J. Comput. Chem. 30 (2009) 2785–2791. [41] P.J. Goodford, A computational procedure for determining energetically favorable binding sites on biologically important macromolecules, J. Med. Chem. 28 (1985) 849–857. [42] D. Caballero, J. Määttä, A.Q. Zhou, M. Sammalkorpi, C.S. O'Hern, L. Regan, Intrinsic alpha-helical and beta-sheet conformational preferences: a computational case study of alanine, Protein Sci. 23 (2014) 970–980. [43] K.K. Patapati, N.M. Glykos, Three force fields' views of the 3–10 helix, Biophys. J. 101 (2011) 1766–1771. [44] I.D. Kerr, D.G. Doak, R. Sankararamakrishnan, J. Breed, M.S.P. Sansom, Molecular modelling of staphylococcal delta-toxin ion channels by restrained molecular dynamics, Protein Eng. Des. Sel. 9 (1996) 161–171.
[32] H.J.C. Berendsen, J.R. Grigera, T.P. Straatsma, The missing term in effective pair potentials, J. Phys. Chem. 91 (1987) 6269–6271. [33] W.L. Jorgensen, J. Chandrasekhar, J.D. Madura, R.W. Impey, M.L. Klein, Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79 (1983) 926–935. [34] B. Hess, H. Bekker, H.J.C. Berendsen, J.G.E.M. Fraaije, LINCS: a linear constraint solver for molecular simulations, J. Comput. Chem. 18 (1997) 1463–1472. [35] T. Darden, D. York, L. Pedersen, Particle mesh Ewald: an N-log(N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089–10092. [36] G. Bussi, D. Donadio, M. Parrinello, Canonical sampling through velocity rescaling, J. Chem. Phys. 126 (2007) 0141011. [37] S. Nosé, M.L. Klein, Constant pressure molecular dynamics for molecular systems, Mol. Phys. 50 (1983) 1055–1076. [38] M. Parrinello, A. Rahman, Polymorphic transitions in single crystals: a new molecular dynamics method, J. Appl. Phys. 52 (1981) 7182–7190. [39] J.E. Fitton, Physicochemical studies on delta haemolysin, a staphylococcal cytolytic
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Biophysical Chemistry journal homepage: www.elsevier.com/locate/biophyschem
Staphylococcus aureus δ-toxin in aqueous solution: Behavior in monomeric and multimeric states
MARK
Maria Carolina de Araujo Meloa, Cláudio Gabriel Rodriguesa, Laercio Pol-Fachinb,c,⁎ a b c
Department of Biophysics and Radiobiology, Federal University of Pernambuco, Recife, Brazil Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil Aggeu Magalhães Research Center, Oswaldo Cruz Foundation, Recife, Brazil
A R T I C L E I N F O
A B S T R A C T
Keywords: α-Helix Hemolytic Membrane Methanol Peptide Tetramers
δ-Toxin is a 26 amino acid peptide capable of lysing several mammalian cell types and subcellular structures. Structurally, δ-toxin predominantly exhibits a α-helical secondary structure in membranes but, in aqueous solution, it adopts varying helical content. As no atomic-level data is available for this peptide in aqueous solutions and for the water-to-membrane transition, this work aims to characterize δ-toxin behavior in these conditions through molecular dynamics simulations in triplicates employing four different parameter sets. Our results, validated on previous experimental data, suggest that the peptide has from 4 to 16 residues folded as αhelix in aqueous solution, and a water-to-membrane foldamer comprising residues 14–18. Considering a previously proposed stable tetramer form in aqueous solutions, protein-protein docking and molecular dynamics studies were performed, suggesting that δ-toxin increases its α-helical content in such organization. The obtained results are expected to contribute in future studies on δ-toxin aggregation and interaction to biomembranes.
1. Introduction Staphylococcus aureus is a ubiquitous, Gram positive, non-motile coccus microorganism, which grows as grape-like clusters [1]. It is a natural human commensal, found for instance in the skin and throat [1]. As a pathogen, it is a leading cause of hospital-associated and community-associated infections worldwide [2]. Mostly all strains produce and secrete several exoproteins to convert local host tissues into nutrients for its growth [3]. As a consequence, such molecules will contribute to the bacteria colonization on target tissues and cause diseases in mammalian species [3]. Such exoproteins include several enzymes and cytotoxins, as nucleases, proteases, lipases, and four hemolysins, named as α-, β-, γ- and δ-toxins [3]. While the β-toxin hemolytic activity is attributed to the enzymatic degradation of membrane sphingolipids, the α- and γ-toxins are known to form βbarrel pores in host cell membranes [3]. On the other hand, regarding δ-toxin mechanism of action, two distinct sets of events were proposed, in which the peptide displays a concentration-dependent activity [4]. Thus, δ-toxin would either act as a surfactant to permeabilize and disrupt the cell membrane at small concentrations [5,6], or to assume a multimeric pore conformation at higher concentrations [7–10]. The δ-toxin is a 26 amino acid peptide, displaying limited or
⁎
nonexistent antimicrobial activity against bacteria [11,12]. However, it lyses and disrupts bacterial protoplasts, spheroplasts, lysosomes and lipid spherules [12]. Also, it is active against a wide range of mammalian cell types, as erythrocytes, and is capable of lysing subcellular structures, as membrane-bound organelles [4]. Structurally, δ-toxin predominantly exhibits a α-helical secondary structure in phospholipid bilayers and membrane-mimetic environments, with 15–20 folded residues [13–17]. In fact, previous MD simulation studies have explored and validated such aspects, as evaluating δ-toxin interaction with POPC lipid bilayers in atomic-level [18], and coarsegrained resolutions [19]. In aqueous solution, however, it adopts varying helical content, which is dependent on its concentration [20,21,11,22]. Although this much knowledge about δ-toxin behavior, there is no structural, atomic-level data available for this peptide in aqueous solutions. In this context, the present work intends to characterize the structure and conformation of δ-toxin in aqueous solutions and in water-to-membrane transition. For this purpose, molecular docking and unbiased molecular dynamics (MD) simulations were performed for this peptide employing four different force fields, in triplicates for each parameter set. Validation on these simulations was achieved with δtoxin structural data on aqueous and membrane-mimetic environments.
Corresponding author at: Aggeu Magalhães Research Center, Oswaldo Cruz Foundation, Recife, Brazil. E-mail address: laercio.fachin@cpqam.fiocruz.br (L. Pol-Fachin). URL: http://www.dqfnet.ufpe.br/biomat (L. Pol-Fachin).
http://dx.doi.org/10.1016/j.bpc.2017.05.015 Received 3 February 2017; Received in revised form 15 May 2017; Accepted 24 May 2017 Available online 26 May 2017 0301-4622/ © 2017 Elsevier B.V. All rights reserved.
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Based on the obtained results, δ-toxin structural behavior in aqueous solution in both monomeric and tetrameric forms could be obtained, while the foldamer for the water-to-membrane transition could be proposed. Additionally, while offering a global view on MD force field behavior on peptide folding, these data are expected to contribute in future studies on δ-hemolysin aggregation and interaction to biomembranes.
AutoDock Tools framework was employed for processing data [40], while Gasteiger partial charges were chosen. The grid maps were calculated using AutoGrid [41], in which a grid map with 126 × 126 × 126 points and a grid-point spacing of 0.325 Å was centered at each peptide center of mass. The Lamarckian genetic algorithm was used in a total of 200 runs, each one with an initial population of 150 random individuals, with a maximum number of 2.5 × 107 energy evaluations, a maximum number of 27,000 generations with mutation and crossover rates of 0.02 and 0.8, respectively. The atomic coordinates of the lowest energy conformations were clustered based a root mean square deviation (RMSD) of 2.0 Å, and sorted in order of increasing energy. The 144 output dimeric forms of the peptide were further clusterized, in which four structures were obtained. These dimers were subsequently employed in additional protein-protein docking studies, in order to generate δ-toxin tetramers, using the same docking protocol.
2. Materials and methods 2.1. Nomenclature and software The recommendations and symbols of nomenclature as proposed by IUPAC [23] were used. The manipulation and visualization of structures, as well as structure figures, were performed with VMD [24]. The secondary structure content analysis was performed with the Dictionary of Secondary Structure of Proteins (DSSP) [25] and via PDBsum generate tool [26]. All the MD calculations and remaining analysis were performed using the GROMACS package, version 4.5.1 [27], employing four different force fields: AMBER99SB-ILDN [28], CHARMM36 [29], GROMOS54A7 [30], and OPLS-AA [31].
3. Results and discussion 3.1. Monomeric δ-toxin in aqueous and membrane-mimetic environments The starting δ-toxin structures for MD simulations comprise a αhelix conformation, folded from residues 2 to 22, thus being ~80% helical. Such behavior was obtained in 2:1 proportion of methanol and water [13]. In pure methanol, which also partially mimics the membrane environments, δ-toxin is expected to present from fourteen to nineteen folded residues, thus being 54–73% helical [13–17]. Based on this data, from the four parameter sets employed during MD calculations in methanol, the correct number of folded residues was better achieved using CHARMM36 and GROMOS 54A7 force fields (Fig. 1B and D), which mostly maintained the NMR-obtained helical content (Fig. 1D, and Supporting Information, Fig. S1). On the other hand, the AMBER99SB-ILDN and OPLS-AA simulations provided less organized patterns for the peptide (Fig. 1B), with a reduced and more diffuse α-helical content (Fig. 1D). The α-helix and coil contents were in general kept constant during the final half of simulations (Supporting Information, Fig. S2). It should be highlighted, however, that in only specific MD simulations the secondary structure content still appear to vary significantly, as in model 2 AMBER99SB-ILDN calculation. As well, it is noteworthy that MD simulations employed with CHARMM force fields mostly showed no variation within this time period. Overall, the employed force fields sampled 30–75% of α-helix (Table 1) and 19–27% of coils for the entire peptide (Table 1), although some differences were observed among the replicas of the same force field (Supporting Information, Table S1). Still, some amount of bend and turn secondary structures types were observed during AMBER99SBILDN and OPLS-AA simulations (Table 1). The helical content was always higher in methanol than in water simulations (Fig. 1C–D). In aqueous solution, thus, the peptide has undergone some significant conformational modifications (Fig. 1A). In all simulations, a reduction in secondary structure content is observed for δ-toxin, in accordance to previous experimental data [21,11], and differences in the resulting structures were observed between the four employed force fields (Fig. 1A and B). As occurred using methanol as solvent, the simulations using the GROMOS 54A7 and CHARMM36 force fields provided more organized structures, in which the peptide was mostly folded from residues 8 to 22 (Fig. 1C, and Supporting Information, Fig. S1). Such structures, thus containing 15 folded residues, represent δ-toxin helicity of 58%, in agreement with a previous 53% helicity measurement from circular dichroism [20]. Moreover, the AMBER99SB-ILDN and OPLS-AA parameter sets also present equivalent profiles for δ-toxin, in which a small α-helical content could be observed (Fig. 1C). As occurred in MD simulations in methanol, the α-helix and coil contents were kept constant during the final half of simulations (Supporting Information, Fig. S3), in which variation in these secondary structure contents occurred in lesser extent
2.2. MD simulations The δ-toxin three-dimensional structure was retrieved from PDB ID 1DTC [13], containing the amino acid sequence MAQDIISTIGDLVKWIIDTVNKFTKK. It contains twenty models derived from nuclear magnetic resonance (NMR) experiments, which were clustered in order to obtain the three most representative structures for the peptide. As a consequence, three replicas for the monomeric δ-toxin MD simulations were studied, starting from each of the selected models 2, 3 and 8. Thus, twenty four systems were studied, in which each of the four parameter sets were used to study each of the three above cited models in cubic boxes, surrounded by water or methanol. Methanol was chosen as a solvent due to its capability to partially mimic the membrane environment, as previously performed in δ-toxin circular dichroism experiments [13–17], and in order to validate the peptide conformation as obtained from each parameter set. Periodic boundary conditions were employed, with at least 10 Å distance from the outside of the structure and the box edge. The SPC water model [32] was used for GROMOS 54A7 simulation in water, TIP3P water model [33] for AMBER99SBILDN and CHARMM36 MD calculations and TIP4P [33] water model for simulations with OPLS. The Lincs method [34] was applied to constrain covalent bond lengths, allowing an integration step of 2 fs after an initial energy minimization using Steepest Descents algorithm. Electrostatic interactions were calculated through the Particle-Mesh Ewald method [35]. The temperature was maintained at 310 K by coupling the system with V-rescale thermostat [36] with a relaxation time of 0.1 ps. Pressure was kept at 1 bar using a Parrinello-Rahman barostat [37,38] via an isotropic coordinate scaling with a coupling constant of 2.0 ps and a compressibility of 4.5 × 10− 5 bar. A 1 ns MD simulation with position restraints was performed as an equilibration period, and was not taken into account to calculate the average ensemble properties. Subsequently, all simulations were further extended to 200 ns. The δtoxin tetramers, obtained from molecular docking studies (described below), were also submitted to MD simulations in water using the same MD protocol. 2.3. Molecular docking As δ-toxin tetramers were previously proposed as stable multimeric forms of the peptide in aqueous solutions [39], molecular docking studies were employed to generate an ensemble of structures that may fulfill such observations. Thus, at first, each of the twenty NMR models contained in PDB ID 1DTC was employed both as a receptor and a ligand in protein-protein docking studies using Autodock4 [40]. The 22
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Fig. 1. MD simulations of δ-toxin monomers in water and methanol environments. In (A–B), representative structures for the trajectories are shown in ribbon diagram. In (C–D), the percentage of time in which each residue showed a helical character is presented, as analyzed for the second half of simulations. The graphs represent an average value for the triplicates MD simulations.
3.2. Force field differences and δ-toxin foldamer
Table 1 Secondary structure content for the monomeric δ-toxin MD simulations. Secondary structure type
Coil β-Bridge Bend Turn α-Helix 310-Helix a
As a general feature, the higher α-helical contents were sampled during MD trajectories using CHARMM36 and GROMOS 54A7, while less structures peptides were described by AMBER99SB-ILDN and OPLS-AA (Fig. 1). As such behavior is maintained in both employed solvents, it is unlikely that SPC and TIP3P water models, or the methanol solvent parameters are playing a major role over such different α-helical contents. In fact, the CHARMM family of force fields has been found to sample the Ramachandran α-helical regions in a higher proportion when compared to the AMBER family of force fields for alanine dipeptides (as reviewed in [42]). Also, a similar profile was previously observed in simulations of a fragment of human α-lactalbumin, in which the α-helical content was higher for CHARMM36, followed by AMBER99SB and OPLS-AA [43]. While, for that specific case, the results obtained with AMBER99SB matched with the experiments, those provided by CHARMM36 and GROMOS 54A7 for S. aureus δ-toxin presented better agreement with the available experimental data in the present work. Thus, instead of pointing for better or worst parameter set, the here presented data suggest that CHARMM36 and GROMOS 54A7 are better suited for δ-toxin MD simulations in comparison to AMBER99SB-ILDN and OPLS-AA.
Average percentage of secondary structure (methanol/water)a AMBER99SBILDN
CHARMM36
GROMOS 54A7
OPLS-AA
19/34 1/0 6/15 12/26 57/16 5/9
20/26 0/0 2/4 3/6 75/63 0/1
19/29 0/0 3/9 4/12 74/48 0/2
27/28 0/2 17/13 19/26 30/17 7/14
The presented values were averaged from the three MD replicas.
within this time period. In fact, from 16 to 63% of α-helix was sampled during simulations in water (Table 1), which suggests that the peptide may show from around 4 to 16 residues folded as α-helix in aqueous solution. On the other hand, around 30% of loops were sampled by the employed parameter sets (Table 1) and, as occurred in methanol, a significant amount of bend and turn secondary structure types were observed (Table 1).
23
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helical and, at higher concentrations and aggregation states, a buried and immobilized Trp15 residue [4]. Thus, in order to generate atomicresolution models for δ-toxin tetrameric organization, the twenty NMR models contained in PDB ID 1DTC were employed in protein-protein molecular docking studies, each as a receptor and a ligand. The 144 output dimeric forms of the peptide were clustered, outputting four different structures, one containing a parallel and the other three with an antiparallel orientation between each monomer. Such dimers were then employed as receptors and ligands in additional protein-protein docking studies, generating sixteen δ-toxin tetramers. Subsequently, eight of them were discarded due to a small interaction interface between their two-composing dimers. Thus, eight tetramer structures remained for further analysis (Fig. 3). Among the molecular docking output structures, one contained four parallel α-helices (Fig. 3D), while the others contained a perpendicular or more diffuse arrangements between their two-composing dimers (Fig. 3A–C and Fig. 3E–H). In order to evaluate the behavior of such tetramers in aqueous solutions, and assess whether the protein-protein contacts derived from molecular docking are maintained in such conditions, those models were refined through MD simulations using both CHARMM36 and GROMOS 54A7 force fields (Fig. 3). As a result, all sixteen systems maintained a tetrameric organization for the peptides, although most of them experienced significant conformational changes with respect to their starting structures. Moreover, the δ-toxin tetrameric organization appears to be still not sufficient to keep the peptides in a full α-helix folding (Fig. 3B, Fig. 3D–E, Fig. 3G), although an increase in the α-helical content is observed in the tetramers when compared to the monomeric forms (from 63% to ~79% for CHARMM36, and from 48% to ~72% for GROMOS 54A7) (Table 1 and Table 3). Regarding the solvent exposure of δ-toxin composing residues, a quite common feature is observed for the peptides, especially for the region comprising residues 9–20. Specifically, hydrophobic residues tend to be buried in the quaternary structure, including Ile9, Gly10, Leu12, Val13, Ile16, Ile17, and Val20, while the charged residues Asp11, Lys14 and Asp18 are highly solvent exposed (Fig. 3, lower graphs). Furthermore, the solvent accessible surface area (SASA) results are in agreement with previous conclusions based on tryptophan fluorescence probing, in which this specific multimeric state does not to contain a buried and immobilized Trp15 residue [10]. Also, as expected, an increase in the per-residue SASA in the vicinity of secondary structure losses can be observed, for instance, for Ile9 (Fig. 3C, GROMOS red curve), Leu12 (Fig. 3B, GROMOS black curve), or for a group of residues (Fig. 3E, CHARMM red and blue curves). In fact, besides N- and C-terminal unfolding, punctual losses of α-helical content mostly occur in the region 6–12, thus generating bends in the peptide structure (Fig. 3).
Table 2 Clustering analyses performed on the monomeric δ-toxin MD simulations. Cutoff (Å)
0.50 1.00 1.50 2.00 2.50 3.00
Average number of different clusters (methanol/water)a,b AMBER99SB-ILDN
CHARMM36
GROMOS 54A7
OPLS-AA
5001/5001 3910/4510 282/755 3/34 1/2 1/1
5001/5001 2146/4073 101/661 1/18 1/1 1/1
5001/5001 4883/4936 878/1959 30/108 1/3 1/1
5001/5001 2569/2720 253/37 5/1 1/1 1/1
a
The presented values were averaged from the three MD replicas. The total number of structures in the trajectories were reduced by a factor of 10 to allow matrixes calculations. b
Regarding the simulations in both water and methanol, all structures sampled in each MD simulation can be distinguished using a RMSD of 0.5 Å, but each entire trajectory form a single conformational cluster employing a 3 Å RMSD cutoff (Table 2). Markedly, the GROMOS 54A7 force field is capable of sampling a higher number of different structures when compared to all other three parameter sets in aqueous solutions (Table 2, and Supporting Information, Table S2), in spite of presenting similar RMSD values with respect to the starting structure, when compared to the other parameter set (Supporting Information, Fig. S4). Additionally, a less number of clusters are observed for the CHARMM36 force field when compared to the other force field (Table 2, and Supporting Information, Table S2). Such feature becomes evident while observing the few secondary structure variation in the final half of MD simulations employing such force field (Supporting Information, Figs. S2 and S3). Therefore, the GROMOS 54A7 force field is capable of sampling a higher number of structures when compared to the other evaluated parameter sets, employing the same time scale, and providing results that fit better with the available experimental data for δ-toxin. However, it should be highlighted that both CHARMM36 and GROMOS 54A7 parameters adequately described the peptide behavior in methanol and aqueous solutions. Based on this observation, the subsequent MD simulations were only performed with these force fields. In this context, twelve additional simulations were performed employing both the CHARMM36 and GROMOS 54A7 force fields on the most representative structures from AMBER99SB-ILDN and OPLS-AA MD simulations in water. Such simulations aimed to evaluate whether δ-toxin folds back to high α-helical contents from less structured conformations (Fig. 2), and which secondary structure determinants would facilitate the refolding process. For the MD simulations starting from three out of six structures, a significant increase in helical content was observed either only for GROMOS (Fig. 2B and C) or for none of the parameter set (Fig. 2D). On the other hand, a higher solidity can be observed among the other MD simulations, in which a gain in secondary structure is observed in both CHARMM36 and GROMOS 54A7 force fields (Fig. 2A, E and F). In such cases, the starting α-helixes comprised residues 10–18 (Fig. 2A), 13–18 (Fig. 2E), and 14–21 (Fig. 2F), thus containing from 5 to 8 folded residues, and resulted in structures showing from 11 to 21 folded residues (Fig. 2). A common feature among those starting structures is the region containing residues 14–18 folded as α-helix, which suggests that the peptide region ranging from 14 to 18 is a foldamer, that is, a nucleation point for δ-toxin folding into a full α-helix upon membrane insertion.
3.4. Insights into δ-toxin aggregation in solution and membranes As previously proposed, the first step of self-association in aqueous solutions is driven by hydrophobic interactions between the apolar sides of the peptide [10]. According to monomeric δ-toxin MD simulations, from 4 to 16 residues are folded as α-helix (Table 1), in peptide regions possibly comprising from Thr8 to Lys22, which mostly remained folded during CHARMM36 and GROMOS 54A7 trajectories (Fig. 1). Such results, together to the here proposed foldamer region (from residues 14–18) and the MD simulations for tetramers, suggest that the interactions between δ-toxin apolar sides should mostly comprise residues Ile9, Gly10, Leu12 and Val13, but mainly Ile16, Ile17, and Val20, located at the central peptide region. Moreover, previous tryptophan fluorescence probing studies also proposed that further oligomerization involves δ-toxin polar side via electrostatic interactions [10]. As already presented, during the MD trajectories, Asp11, Lys14, Trp15 and Asp18 are highly solvent exposed, and may be available for protein-protein contacts in higher aggregation complexes.
3.3. Protein-protein docking and dynamics The S. aureus δ-toxin has been proposed to be increasingly selfaggregated with increasing peptide concentration [22,4]. In this context, the first aggregation state would occur at 2 μM concentration, as a tetramer [22]. The main characteristics of the peptides in such aggregates would include δ-toxin monomers being fundamentally α24
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Fig. 2. Monomeric δ-toxin behavior as observed through MD simulations starting from the most representative structures of AMBER99SB-ILDN and OPLS-AA MD simulations in water. The representations refer to the secondary structure content for the starting structure (cyan), and for the most representative structures obtained from MD simulations employing CHARMM36 (red) and GROMOS 54A7 (green) force fields. In the graphs, the percentage of time in which each residue showed a helical character is presented, as analyzed for the second half of simulations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
upon insertion in membranes, such behavior is expected to intensify, while an inversion should occur in relation to the tetramers profile observed in aqueous solution in the present work. Thus, hydrophilic amino acid residues (as Asp11, Lys14, Asp18 and Lys22) should probably interact more intensely in the peptide-peptide interface or face to the water lumen of the pore, and hydrophobic residues (as Ile6, Leu12, Val13, Ile17 and Val20) would face the phospholipids acyl chains. Previous δ-toxin membrane-inserted models, generated based on simulated annealing and/or MD calculations, point to the same assumptions, either observing Asp11 and Lys14 pointing to the water lumen of the pore [44], or inter-peptide salt-bridges between Asp and Lys residues [19]. Altogether, significant conformational modifications are expected between δ-toxin multimers in the water-to-membrane transitions. However, further studies, especially employing experimental techniques, are still required to confirm and provide further information regarding δ-toxin insertion into biomembranes.
Independently on the number of monomers involved, a structural model for aggregated species with stacked antiparallel amphipathic rods has been proposed for δ-toxin [22,4]. Although such organization was not observed in the present work, an anti-parallel arrangement between monomers was indeed stable as an output of MD simulations (Fig. 3A, GROMOS). In fact, possibly much longer time scales (microsecond-long MD simulations) should be required to reach a unique, fully stable tetrameric form for δ-toxin from the five structures obtained. However, the here obtained results are in accordance with previous experimental data, providing new structural and conformational data on multimeric δ-toxin states not accessible from such methodologies, and considering both spatial and temporal variables. Moreover, based on the performed calculations, insights into δ-toxin conformational transition while moving from the water environment to be membrane-inserted may be suggested. In this context, in small concentrations of δ-toxin, although a minimum foldamer ranging from 14 to 18 is required, the peptide may contain up to fourteen folded amino acid residues, as such amount of secondary structure is the interface between the observed α-helical content in methanol and water (Fig. 1C–D and Table 1). As well, previous MD simulations studies observed that the peptide tends to form dimers on the water-tomembrane interface [19], in which hydrophilic residues (as Ile, Trp and Leu) can enter deeper in the membrane bilayers than hydrophilic residues (as Asp and Lys). Therefore, in higher peptide concentrations,
4. Conclusions In this work, δ-toxin has been studied through long time scales MD simulations with four different parameter sets, in triplicates. The obtained data was validated based on previous circular dichroism, tryptophan fluorescence probing and NMR data, in terms of the conformational profile adopted by the peptide. In aqueous solution, 25
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Fig. 3. Molecular docking and MD simulations of δ-toxin tetramers. In (A–H), the upper structures represent the eight molecular docking models, while the lower structures are the most representative structures obtained from MD simulations employing CHARMM36 (left) and GROMOS 54A7 (right) force fields. In the graphs, the percentage of time in which each residue showed a helical character is presented, together to the per-residue solvent accessible surface area (SASA).
was proposed to be the region comprising residues 14–18. Methodologically, among the employed force fields, the GROMOS 54A7 and CHARMM36 provided the results in better agreement with the experiments, for both water and methanol environments.
our results suggest that the peptide has from 4 to 16 residues folded as α-helix, depending on the employed force field. In this context, the involved amino acid residues, according to MD simulations, range from Thr8 to Lys22. Additionally, the δ-toxin water-to-membrane foldamer 26
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References
Table 3 Secondary structure content for the tetrameric δ-toxin MD simulations. Secondary structure type
Coil β-Bridge Bend Turn α-Helix 310-Helix
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Average percentage of secondary structure (CHARMM36/ GROMOS 54A7)a A
B
C
D
E
F
G
H
20/ 22 0/0 1/2 2/2 77/ 74 0/0
15/ 17 0/0 1/1 5/3 76/ 78 3/1
15/ 24 0/0 1/3 2/7 82/ 66 0/0
17/ 18 0/1 1/2 2/3 80/ 75 0/1
14/ 23 0/0 1/5 4/4 81/ 68 0/0
16/ 22 0/0 1/3 3/5 80/ 69 0/1
18/ 19 0/2 1/2 2/4 79/ 72 0/1
17/19 0/0 1/3 3/5 79/73 0/0
a
The presented values were averaged from the second half of MD simulations, in which the A-H letters refer to each of the tetrameric forms shown in Fig. 3.
Nevertheless, the GROMOS parameter set presented a higher conformational sampling in the same timescale, when compared to the other evaluated force fields, which facilitated the observation of the δ-toxin folding and unfolding process. Subsequent protein-protein molecular docking studies on the NMRderived structures generated eight different tetramer structures, which were submitted to MD simulations refinement. As a consequence, the peptides α-helical content increased in this multimeric organization, while δ-toxin apolar faces were buried from water. Still, further studies are required to evaluate δ-toxin behavior in higher aggregation complexes. Altogether, the obtained results offer a global view on MD force field behavior on peptide folding and are expected to contribute in future studies on δ-toxin interaction to biological membranes. Abbreviations MD NMR RMSD RMSF SASA
molecular dynamics nuclear magnetic resonance root-mean-square-deviation root-mean-square-fluctuation solvent accessible surface area
Conflicts of interest No conflicts of interest to report. Authors contribution M.C.A.M. and L.P.F. performed and analyzed molecular dynamics simulations. L.P.F. designed research and supervised the project. M.C.A.M., C.G.R. and L.P.F. wrote the paper and approved the final version of the manuscript. Acknowledgments This work was financially supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq Universal, grant number 454470/2014-2), by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – MEC), and by Fundação de Amparo a Ciência e Tecnologia do Estado de Pernambuco (FACEPE, grant number APQ-0398-1.06/13). The authors would like to gratefully acknowledge the Biomaterials Modelling Group (BIOMAT) and the Centro Nacional de Supercomputação (CESUP) from Universidade Federal do Rio Grande do Sul (UFRGS) for computational resources. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.bpc.2017.05.015. 27
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