Effect of Inactivating Mutations on Peptide Conformational Ensembles

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Effect of Inactivating Mutations on Peptide Conformational Ensembles: The Plant Polypeptide Hormone Systemin Saikat Dutta Chowdhury, Aditya K Sarkar, and Ansuman Lahiri J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00666 • Publication Date (Web): 24 Jun 2016 Downloaded from http://pubs.acs.org on June 26, 2016

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Journal of Chemical Information and Modeling

Effect of Inactivating Mutations on Peptide Conformational Ensembles: The Plant Polypeptide Hormone Systemin Saikat Dutta Chowdhury, Aditya K. Sarkar, Ansuman Lahiri* Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, West Bengal, India Abstract As part of their basal immune mechanism against insect/herbivore attacks, plants have evolved systemic response mechanisms. Such a systemic wound response in tomato was found to involve an 18 amino acid long polypeptide called systemin, the first polypeptide hormone to be discovered in plants. Systematic alanine scanning and deletion studies showed differential modulation in its activity, particularly a major loss of function due to alanine substitution at positions 13 and 17, and to a lesser extent for position 12, respectively. We have studied the conformational ensembles of the wild-type systemin along with its 17 variants by carrying out a total of 5.76 µs replica exchange molecular dynamics simulation in implicit solvent environment. In our simulations, wild type systemin showed a lack of α-helical and β-sheet structures in conformity with earlier circular dichroism and NMR data. On the other hand, two regions containing diproline segments showed a tendency to adopt polyproline II structures. Examination

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of conformational ensembles of the 17 variants revealed a change in the population distributions, suggesting a less flexible structure for alanine substitutions at positions 12 and 13 but not for position 17. Combined with the experimental observations that positions 1-14 of systemin are important for formation of the peptide-receptor complex, this leads to the hypothesis that loss of conformational flexibility may play a role in the loss of activity of systemin due to the P12A and P13A substitutions, while T17A deactivation probably occurs due to a different reason, most likely due to the loss of the threonine phosphorylation site. We also indicate possible structural reasons why the substitution of the prolines at the 12th and 13th positions leads to a loss of conformational freedom in the peptide.

Introduction Systemin is an 18-amino acid polypeptide that was found to act as a plant systemic defense signaling molecule inducing the production of proteinase inhibitors in response to wounding in tomato1 (Solanum lycopersicum).

For quite some time, tomato systemin and its predicted highly conserved homologues in potato (Solanum tuberosum), black nightshade (Solanum nigrum) and bell pepper (Capsicum annuum), all belonging to the Solanaceae family, were the only known members of the antiherbivore defense related signaling peptide family in plants2. With the discovery of functional homologues rich in hydroxyprolines instead of prolines in tobacco3 (Nicotiana tabacum) and later in tomato4, petunia5 (Petunia hybrida), and black nightshade6, it was perceived that similar signaling function can be carried out with diverging sequences. Concurrent discovery of the existence of hydroxyproline systemins outside the Solanaceae family7,8 and suggested


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recruitment of some members from the plant elicitor peptide (PEP) family of defense-related peptides9 for performing closely similar function to that of systemin revealed a very complex scenario of generation of peptide signals from their precursor proteins, activation of signaling cascades and regulation of gene expression. It appears to be of considerable interest to investigate whether important insight into the signaling mechanisms can be gained through a better understanding of the conformational properties of the peptides and their interaction with their receptor(s).

The primary structure of systemin is somewhat unusual (Figure 1). Two pairs of proline residues form part of a pseudo-palindromic sequence which has been represented as xxQxBPPxBBxPPBxQxx where B can be either Lys or Arg1. This symmetry gave rise to the expectation of its reflection in its three dimensional structure10. Using two-dimensional nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics calculations, the authors postulated a folded Z-like β-sheet structure for systemin for a low pH environment. However, they also observed that at higher pH, the NMR data was consistent with the existence of a multiplicity of isomers for the peptide conformation leading to the inference that at physiological pH the peptide presumably adopted a random coil (RC) structure10. This was also in accordance with an earlier proton NMR study of systemin, which did not find any persistent secondary or tertiary structure in solution at neutral pH11. This report also indicated that the C-terminus of the peptide was less coil-like in nature and did not show rapid interconversion between local conformations presumably due to the presence of hydrogen bonding interactions11. A further attempt at conformational characterization of systemin with circular dichroism (CD) spectroscopy arrived at a similar conclusion on the peptide being a mixture of random coil with


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the presence of β-turn and β-sheet motifs12. In another study, the CD spectrum of systemin at low temperature was found to be consistent with the presence of about 58% polyproline II (PPII) structure13. The spectra at higher temperatures were found to increasingly correspond to disordered conformations. On the basis of their CD data as well as analysis of protein structures containing the sequence DPPK, the authors suggested the possibility of PPII structure in the central region or in a substantial population of systemin. However, it has been pointed out that the tetrapeptide sequence at the centre of systemin is not conducive to a polyproline helix conformation14.

Figure 1. Primary structure of tomato systemin. These observations necessitated the examination of the conformational characteristics of systemin from the viewpoint of the rapidly developing area devoted to the study of the so called intrinsically disordered proteins/peptides (IDPs)15-17.

Earlier experiments attempting to elucidate the primary structure-activity relationship of the peptide noted that deletions as a rule affected the activity negatively18. However, alanine scanning mutagenesis, where each amino acid of this 18 residue long peptide (except the Nterminal alanine) was systematically substituted with alanine, revealed a more complex picture by identifying three regions around Pro12, Pro13 and Thr17 where Ala substitution markedly reduced the peptide activity, the most drastic reductions being associated with the mutations involving Pro13 and Thr1718. The rest of the residue positions did not seem to affect the peptide activity on Ala substitution. Systematic mutation and deletion experiments18,19 also suggested


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that four residues in the C-terminal were necessary for systemin activity while the N-terminal 14 residues were most likely to be involved in binding to a receptor20.

Although a large amount of details of the signaling mechanism in which systemin is involved remains unknown, including the exact identity of the receptor protein(s), questions related to its conformational characteristics are relatively straightforward to address. In this work, we have used atomistic molecular dynamics simulations to explore the conformational ensembles of systemin and its 17 alanine mutants to study if the conformational distributions offer any insight into the peculiar primary structure-activity relationship of systemin outlined earlier. Such computational alanine scanning mutagenesis have already been tried in the context of proteinprotein interaction and utilized the generalized Born (GB) implicit solvent simulations21. Here, we have used a similar approach but also employed replica exchange molecular dynamics (REMD) simulations22. A series of similar studies utilizing the REMD simulations with the implicit solvent GB method have been used to explore the conformational ensembles of unstructured proteins/peptides23,24. The enhanced sampling capability offered by the REMD technique coupled with recent advances in the development of accurate protein force field parameters25 also provides an opportunity to reexamine the earlier conformational studies and compare with experimental data.

Computational Details System Preparation

The

sequence

of

wild-type

systemin

from

Solanum

lycopersicum

(AVQSKPPSKRDPPKMQTD) has been taken from Pearce et al18. Total 18 peptide systems consisting of the wild-type systemin and its 17 alanine mutants (Table 1) were built in the


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extended conformation using the LEaP module of AMBER12 package26. The N-terminal and the C-terminal were capped using the Acetyl (ACE) and N-methyl (NME) moiety respectively. In this work, mutated systemins will be referred to as XNA where X is the wild-type amino acid at the N-th position (numbering was assumed without considering capping moieties) and A represents alanine. Replica Exchange Molecular Dynamics simulation

REMD22 simulation of wild-type systemin and its 17 variants were performed using the sander module of AMBER12 program suite26. In REMD simulation, several replicas of a system are simultaneously simulated at various temperatures for a certain period of time and then exchanges of coordinates are attempted between the replicas using an exchange probability (Pexchange) defined as, Pexchange = exp[−(βi − βj )(Ei − Ej )], where i and j are any two replicas between which exchange will be attempted,

β = 1/kBT, kB is the Boltzmann constant, T is the absolute

temperature, and E is the potential energy.

In this work, a total of 16 replicas having temperatures (T): 298.00K, 303.92K, 309.99K, 316.22K, 322.66K, 329.10K, 335.91K, 342.72K, 349.76K, 356.98K, 364.37K, 371.95K, 379.74K, 387.72K, 395.90K, 404.30K, were generated for each system using the method developed by Patriksson et al.27 All simulations were carried out considering a monovalent salt concentration of 0.15M along with the generalized Born model28 which implicitly implemented the effect of water in the system. The AMBER ff99SBildn force field25 was used to model the peptides. The system was first subjected to 500 steps of steepest descent minimization followed by 500 steps of conjugate gradient under unrestrained condition. The replicas were made from this energy minimized structure and separately heated from 0K to their designated temperatures. The heating phase was performed for 200ps using the Langevin thermostat having collision


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frequency of 1ps-1. Post heating constant volume REMD simulation was performed which attempted to exchange coordinates between the replicas after every 20 ps of production run. Total 1000 Monte-Carlo exchanges were attempted with exchange probability of 0.77.

Throughout the simulation, infinite cut-off was used to calculate the non-bonded interaction energy, SHAKE29 was used to constrain the bonds involving hydrogen atom with its default tolerance of 0.00001Å. The leapfrog integrator was used to propagate the dynamics with a time step of 0.002 ps. Coordinates were written for every 2ps. Radius of Gyration

We used cpptraj of AmberTools1430 to calculate the radius of gyration (Rg). We used residue masking of 2-19 for Rg calculation. Histograms were generated in R statistical software31. Energy Calculation and Solvent Accessible Surface Area

Sander module of Amber12 was used for calculating the total energy with its decomposition terms, where imin=5 and maxcyc=1 was used. For solvent accessible surface area calculations, naccess32,33 was used with its default parameters. Per-residue Energy Decomposition

Application of energy based calculation on a per-residue level to understand the structural changes due to mutation has been successfully carried out before34-36 using molecular mechanics/generalized born surface area (MM/GBSA) method which also allows the decomposition of the energy into different interaction terms37-42. We carried out per-residue energy decomposition analysis for each system and compared the results with those from the wild-type system. Calculations were performed using MMPBSA.py implemented in AmberTools.


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Hydrogen Bond Analysis

Hydrogen bond analysis was performed in the cpptraj of AmberTools14. Default parameters (distance cutoff of 3.0 Å between acceptor and donor heavy atoms and angle cutoff of 135° between acceptor, hydrogen and donor atoms) were used to detect hydrogen bonding interaction. A shell script was then used to calculate the occurrence of a particular type of hydrogen bond. Root Mean Square Fluctuation

To further study the flexibility and local motion, we calculated the root mean square fluctuations (RMSF) of each systems using the cpptraj module. Clustering

Conformational clustering was performed based on the distance root-mean square deviation (dRMSD) metric43 calculated using RmsCalc module of the Bioshell44,45 software suite. The dRMSD (DAB) metric is defined as follows,

2 =

− ( − 1)

where A and B indicates two conformations, N is the total number of atoms, dij is the euclidean distance between the i-th and j-th atom. From the histogram of these dRMSD values, highly populated inter-conformational distance was obtained and used as a cut-off for clustering43, e.g., in the case of wild-type systemin, most common inter-conformational distance was between 2.5 Å and 3 Å (Figure 2). So we took 2 Å as our cut-off. Cut-off for all other systems was similarly found. Clustering was carried out employing average linkage criteria with the help of the Clust utility of Bioshell44,45 software. The highest populated cluster was used as the representative ensemble of respective systems. Number of conformations in the representative cluster and the corresponding merging distance cut-off for each system are given in Table 1.


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Figure 2. Inter-conformational distance distribution of wild-type systemin.


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Table 1. Merging cut-off distance used in clustering for each of the 18 systems. This cut-off was calculated from inter-conformational distance distributions. Systems

Merging cut-off (Å)

Total

Number

Clusters

of Number

of

conformations in the highest cluster

Wild-type

2.0

69

1910

V2A

3.0

23

3829

Q3A

2.5

39

2805

S4A

2.5

39

2389

K5A

2.5

34

3398

P6A

2.5

44

2100

P7A

2.5

58

1121

S8A

2.5

35

3849

K9A

2.0

73

1823

R10A

2.5

56

4600

D11A

2.5

42

2623

P12A

1.5

106

1855

P13A

1.0

129

1142

K14A

2.5

33

5423

M15A

2.5

42

2312

Q16A

2.5

44

2903

T17A

3.0

19

5157

D18A

2.5

38

4017


10

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Inter-atomic distance deviation

We calculated the inter-atomic distance deviation of the C-alpha (Cα) atoms for the conformations in the highest cluster for each system. The distance deviation measure46 (Sij) is defined as, = < − < > >

where dij is the euclidean distance between i-th and j-th Cα atoms. implies ensemble average. This metric was used to identify the rigid regions of proteins/peptides. Distance deviation between the atoms of a rigid domain will be negligible compared to distance deviation between atoms from different rigid domains. Polyproline content assignment

The structural ensembles of the systemin generated through implicit and explicit solvent simulations were analysed using the PROSS structural assignment tool47 for their residue-wise polyproline II propensity.

Results and Discussion Submission of the 18 amino acid systemin sequence (UniProt ID K4C1K6) to the D2P2 database48 showed that the entire systemin sequence was predicted to be disordered by at least 75% of the predictors in the database. We also looked at the disorder propensity of the protein sequence using the simple Russell/Linding propensity scale49. The Russell/Linding approach postulates that the disorder propensity of an individual amino acid can be estimated from the simple formula P = RC - SS where RC is the propensity of the given amino acid to be in the random coil state and SS is the same for regular secondary structures (α-helical or β-strand). The propensity for random coil as defined by the authors also include the possibility of forming


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loops, turns etc. which were outside the definition of regular secondary structures49. This analysis showed that the systemin sequence contained a stretch SKPPSKRDP (residue positions 4-12) that should be disordered according to the Russell/Linding criterion. Distribution of Radius of Gyration

Figure 3 shows the distribution of the radius of gyration (Rg) for wild-type systemin and its 17 alanine substituted systems and respective average Rgs are listed in Table S1. From the plots (Figure 3) and Table S1 it is clear that the systems P12A and P13A show a deviation in Rg distribution from that of the wild-type as well as from the rest of the systems. The lower mean values of Rg for P12A and P13A with narrow and sharp peaks in the distribution imply the prevalence of more compact conformations compared to the rest of the systems. An estimate of the deviation of these polypeptides from random coil behavior can be obtained in a simple manner. According to Kohn et al.50, Rg of a random-coil polypeptide with N residues should scale as Rg = R0Nν (where R0 is 1.927 Å and ν is 0.598). From this relationship, wild-type systemin would have Rg = 10.85 Å if it had a completely disordered structure. On the other hand, our simulations sampled a population of wild-type systemin whose average Rg was 8.369 Å, a value which is considerably less than the random-coil estimate. Although it is possible that the implicit solvent model employed in the present set of simulations lead to an altered set of parameters, those should be applicable to all the polypeptide systems studied here. Consequently, the conformational ensembles in the systems P12A and P13A show a distinctly different nature from the other systems both in their average values as well as in the shape of the distribution. Admittedly, the radius of gyration is a parameter that does not distinguish between conformations having similar linear dimensions. Still, the distribution data suggests the possibility that the P12A and P13A polypetides are significantly more conformationally constrained than the other variants.


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Figure 3. Distribution of Radius of Gyration (Rg) for wild type systemin and its 17 variants.


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Energy and Solvent Accessible Surface Area Calculation

To understand the aforementioned conformational characteristics, we calculated the total energy (along with the decomposition terms) and solvent accessible surface area (SASA) of each conformation for each system (see Computational Details). We analyzed the correlation of SASA with total energy, the van der Waals (VDW) component, and the electrostatic energy component (Figure S1, S2 and S3 in supporting information). Our analysis found significant correlation, particularly between the VDW component and SASA (Figure 4). This suggested that steric interaction between amino acid side chains might play an important role in dictating the conformational variations.

Figure 4. Correlation coefficient between total SASA and van der Waals energy term (VDW), total energy, electrostatic energy term (EEL). To elucidate the effect of steric interaction, we calculated residue-wise SASA for residues 2-17 for each system and a heat map corresponding to these calculations (Figure S4 in supporting information) showed that the residue-wise accessible surface areas were significantly different


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between the wild-type and P12A/P13A systems. A comparison of wild-type and P13A systems revealed that in P13A, residues Val2, Gln3, Thr17 showed decrease in SASA, residues Lys5, Asp11, Pro12, Gln16 showed increase in SASA, residues Pro7, Arg10, Lys14, Met15 showed significantly strong decrease in SASA and residues Ser14, Pro6, Ser8, Lys9 showed no change. Similar changes were observed in P12A although to a lesser extent for some of the residues. (a)


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(b)

Figure 5. Residue-wise solvent accessible surface area of (a) Arg10 and (b) Met15. In figure 5 we have plotted the residue-wise SASA of Arg10 and Met15 for each conformations in different systemin variants. It is clear from the data that these residues show the most striking change in SASA in P12A and P13A. To further elucidate the reason for the change in SASA of Arg10 and Met15, we compared the conformations of wild-type and P13A with the lowest VDW energies (Figure 6). From figure 6, three aspects are evident. First, Arg10 is completely buried in P13A compared to wild-type systemin. Secondly, Met15 is more buried in P13A than in the wild-type systemin and third, Arg10 and Met15 are more closely placed to each other in P13A. These observations suggested that Arg10 and Met15 in P13A may have more opportunity for hydrogen bonding among themselves and thus give rise to a less flexible core.


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Figure 6. Comparison of lowest VDW energy conformations of (a) wild-type and (b) P13A is shown. Images at lower panel is rotated 90° along Y-axis. Arg10 is shown in magenta and Met15 in yellow.


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Per-residue Energy Decomposition

We further analysed the relative energies51 between mutant and wild-type systems at residue level. Energies were further divided into backbone, side chain and total energy components. The findings are summarized in figure 7 and relative energies are given in table S2 and table S3. Most of the mutations destabilized the system except for mutations at four prolines. Among these, mutations at position 12 and 14 induced changes in a stretch of 4 continuous residues (position 10-13 and 13-16 respectively) whereas mutation at position 13 induced change in a stretch of 5 continuous residues (position 10-14). P12A and P13A shares a common stretch (position 10-13) and they both stabilized the mutant systems with respect to the wild-type.


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Figure 7. Residue-wise energy changes (∆E[m] – ∆E[wt]) between wild-type (wt) and mutants (m). Changes in total energy (∆E[m] – ∆E[wt]) greater than 1.5kcal/mol in absolute value are shown in red (positive change) and blue (negative change). Black letters in the amino acid sequence indicate changes in relative energies less than this threshold. The symbol ● implies side chain contribution, * represents equal contributions from side chain and backbone whereas blank means backbone contribution. Hydrogen bond analysis

Slosarek et al.10 concluded from their NMR experiments on wild-type systemin that Lys, Arg, Met and Gln residues participate in weak hydrogen bonding interactions. Figure 8 shows the percentage occurrence of different types of hydrogen bonds found in each system. According to our selection criteria (see Computational Details), we found 30 different hydrogen bonding


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interactions. We found that qualitatively, the hydrogen bonding pattern of wild-type systemin was maintained in all other systems except P12A and P13A. Three types of hydrogen bonds, namely (i) Arg10-side_chain to Met15-main_chain, (ii) Lys14-main_chain to Asp11-main_chain and (iii) Arg10-side_chain to Lys14-main_chain were found to occur only in P13A. We also observed that Arg10-side_chain to Asp18-side_chain and Arg10-side_chain to Lys5-main_chain hydrogen bonds had more probability of occurrence in P13A. Apart from Lys14-main_chain to Asp11-main_chain and Arg10-side_chain to Lys14-main_chain hydrogen bonds, aforementioned observations were also found in P12A.

Figure 8. Percentage of structures within each system that exhibited a particular type of hydrogen bond where side indicates side chain and main indicates main chain.


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Root Mean Square Fluctuation

In figure 9 we have compared the RMSF of each system with the results from wild-type systemin. RMSF calculations showed that in general N- and C-terminal residues were more flexible than residues in the central region (residue position 5-11) whereas residues 12, 13, 14 were moderately flexible. We noticed that most mutations did not introduce any major qualitative change in the trend apart from slight increase in flexibility in the central region. But P7A, D11A, P12A and P13A mutations introduced more noticeable changes. Flexibility of residues in the central region increased noticeably due to P7A and D11A mutations whereas P12A and P13A mutations made the systems overall less flexible than the wild-type molecule.


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Figure 9. Root mean square fluctuations (RMSF) of mutated systems (in red in each panel) compared with wild-type systemin (in black).


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Evaluation of secondary structure

To investigate the conformational characteristics of systemin and its alanine variants, we have looked at the frequency of occurrence of secondary structures adopted by each residue in our REMD simulations. Secondary structures were assigned using STRIDE52 (Figure 10) and the DSSP method53 (Figure 11) implemented in AmberTools13.

According to STRIDE calculations, residues of wild-type systemin mostly preferred the turn conformation. The frequency of this secondary structure was found to be 60-80% for residues 210 (VQSKPPSKR) and 12-15 (PPKM). In contrast, residues Asp11, Thr17, Asp18 showed a preference for the coil conformation with a frequency of 80-100%. Exception to this trend was observed for residues Ala1, Gln16 which adopted turn and coil conformation with almost equal frequency. While most of the alanine substitutions maintained the above trend, P13A was found to increase the frequency of turn conformation for Asp11 which preferred coil conformation in the wild-type peptide (Figure 10).

In the case of DSSP, turn conformation (Figure 11) was found to be relatively preferred in residues 2-4 (VQS), 7-9 (PSK) and 13-17 (PKMQT) of wild-type systemin. This observation is somewhat similar to our observation from STRIDE analysis (Figure 10) except in positions 5,6,10,12,16,17. In this case also, most of the mutations maintained this general trend except P13A which induced turn conformation at residue position 12.

The perceived difference between secondary structure assignments by STRIDE and DSSP, both of which are well-tested methods and use a closed set of criteria, should not come as a surprise. Both these methods have been seen to converge in their structure assignments for


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proteins having well-defined three-dimensional structures. The agreements were seen to be more pronounced for α-helices and β-sheets and poorer for turns. However, assignments in the terminal regions of secondary structures were in general less well-defined. Assignment strategies also differed in accommodating allowed levels of distortion within a secondary structure. These indicated that for peptides which do not have well defined structures, the secondary structure assignment results may differ from method to method54.


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Figure 10. Percentage of residue wise secondary structures calculated using STRIDE. Residue position is given along the X-axis and frequency (%) of secondary structures given in Y-axis. Secondary structures are represented as: turn(black), coil(blue), 3-10 helix(cyan), α-helix(red), πhelix(orange), extended conformation(magenta), isolated bridge(green).


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Figure 11. Percentage of residue wise secondary structures calculated using the DSSP method implemented in AmberTools13. Residue position is given along the X-axis and frequency (%) of secondary structures given in Y-axis. Secondary structures are represented as: turn(black), 3-10 helix(orange), α-helix(magenta), anti-parallel β−sheet(cyan), π-helix(green).


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Inter atomic distance deviation

It was observed from the distance deviation map (Figure 12) of the wild-type systemin that there were no distinctly rigid regions present within the peptide. However, distances between the first residue and the terminal MQTD region showed increased fluctuation compared to the rest.

Introduction of a single mutation in all the cases significantly affected this scenario. The polypeptides V2A, Q3A, S4A, K5A, P6A, P7A, S8A, R10A, D11A, K14A, M15A, and T17A showed a much stronger fluctuation of the distances between the N-terminal and C-terminal residues than in the wild-type polypeptide.

Remarkably, the polypeptides P12A and P13A showed an opposite trend. The fluctuations between the N-terminal and the C-terminal residues in the largest cluster of these systems were even less than that observed in the wild-type systemin. The distance deviation maps for these two systems, as a whole, indicated a comparatively less flexible ensemble of conformations than what was observed in the other systems.


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Figure 12. Inter atomic distance deviation map for the largest clusters for each systems. Sequences are shown along the X and Y-axis for each system.


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Persistence of polyproline helix in the conformation ensemble

The peptide backbone of systemin has been reported to adopt a significant amount of polyproline type-II helix (PPII) like conformation in solution13, particularly, at low temperature. Since neither STRIDE nor DSSP include this type of secondary structure in their peptide backbone characterization, we have calculated the residue-wise propensity to take up polyproline II structure using the PROSS structural assignment tool47 for the wild-type and mutated systems (Figure 13). In figure 13 we have plotted the residue-wise PPII propensity of each mutated systems with respect to wild-type systemin. All systems except P12A and P13A showed two peak propensities at residue position 6 and 12. But mutation P12A and P13A changed the PPII propensities of residue positions 10-13 with a characteristic left shift. We observed that all other mutations except P12A and P13A only changed the propensity of residues slightly compared to those for the wild-type.


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Figure 13. Polyproline II propensity of all mutants (in red in each panel) with respect to wild-type systemin (in black).


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Conclusion The plant signaling molecule systemin is an example of an intrinsically disordered peptide. Bioinformatic analysis indicates inherent disorder in the peptide sequence48 and explicitly predicted region 4-12 (SKPPSKRDP) out of 18 amino acids as a disordered region which may contain turn or loop like secondary structure49. We have carried out total 5.76 µs (20 ns for each replica in each system) REMD simulations of wild-type systemin and its 17 alanine variants to explore the effect of mutations in its conformational ensemble.

In the light of conformational selection postulate55, we can envisage the interaction between systemin and its receptor as the receptor selecting the most favorable conformation of systemin out of the large ensemble of conformations available to it under normal physiological conditions56. In this scenario, the effect of inactivating mutations may be seen as depopulating the set of conformations favorable for binding to the receptor out of the ensemble of conformations generated in the case of the wild-type peptide. However, it is also possible that the conformation adopted by systemin while binding to its receptor is quite different and is achievable if the molecule retained its flexibility.

Keeping in mind the aforementioned hypothesis, we explored the ensembles of each system based on Rg distribution. We found that P12A and P13A samples significantly different types of conformational ensembles compared to wild-type systemin. This result corresponded to the observations of Pearce et al.18 that showed a major reduction in activity due to alanine substitutions at positions 12 and 13. We have also seen that due to alanine substitution at position 13, Arg10 and Met15 got buried. This resulted in the occurrence of certain hydrogen bonds


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unique to P13A and increased occurrence of two existing types of hydrogen bonds involving these residues (Figure 8). Similar interactions seem to be responsible for the characteristics of the P12A ensemble.

The conformational ensemble generated by our REMD simulations clearly indicates that turn conformation is the most favorable secondary structure for the wild-type systemin. Residues that prefer this conformation are found to be in well agreement with the prediction made by Russell/Linding criterion49. Due to the inherent assumptions in the secondary structure assignment methodologies54 , we are unable to draw a conclusive remark on the effect of mutation P13A. However, both STRIDE and DSSP indicate that this mutation increases the turn content of upstream residues in comparison to the wild-type.

In the literature, there has been increasing discussion on the possibility that the conformations that are considered to be disordered may actually adopt polyproline II like conformations. Indeed, analysis of the conformations of WT systemin and its mutants by dedicated methods for assigning PPII conformations showed the presence of two distinct regions having high propensity for such conformations. Still more remarkably, the mutations P12A and P13A showed a pattern of shift in the conformational features that were not seen in WT systemin or the other mutants. Since PPII conformation has been implicated in receptor binding in many proline-rich peptides, this difference is quite suggestive.

Comparing the distance deviation maps (Figure 12), it is evident that except P12A and P13A, most of the mutations mainly increase the flexibility of the peptide termini which implies more


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conformational flexibility of systemin. Mutations like P12A and P13A are found to relatively decrease the flexibility in the conformational ensemble which is also evident from the Rg values (Table S1) and cut-off distances (which in turn depends on the interconformation distance) used for clustering (Table 1). Similar results were also observed from RMSF analysis (Figure 9).

In a comprehensive set of experiments, Meindl et al.20 looked at the ability of systemin fragments to generate the rapid medium alkalinization response or a substantial increase in MAP kinase-like activity when applied to tomato leaves in sub-nanomolar concentrations. Their results showed that the fragments containing the N-terminal 14 residues or the C-terminal four residues did not elicit any significant response or activity when added singly or in combination. In a competition assay with wild-type systemin, the N-terminal fragment demonstrated antagonistic activity, while the C-terminal fragment did not show any activity at all. Furthermore, systemin, extended at the C-terminus by a tyrosine and radioiodinated at that position, bound to a class of sites in tomato leaf membrane preparation. This binding was inhibited by unlabeled systemin but not the C-terminal fragment or unrelated peptides. The binding was also inhibited by the Nterminal fragment and the systemin-T17A mutant, the latter showing a stronger affinity for the binding site than the 14-residue N-terminal fragment. The most plausible explanation for the above observations is that the N-terminal 14 residues of the wild-type systemin are sufficient to bind to its receptor site in the membrane with considerable affinity. However, this fragment by itself lacks the ability to activate the signaling system for which the four residues in the Cterminal are required.


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Together with the results about the activity of the wild-type signaling peptide, its alanine scanning mutants and the fragment binding data for the putative binding site or receptor, our simulation results indicate a binding scenario in which the dominant conformational ensembles of systemin and its alanine mutants are essentially similar to one another except for P13A and also to some extent for P12A. The alanine substitution at the 13 position impart to the conformational ensemble a significantly different secondary structure propensity and rigidity that may be responsible for the observed loss of activity through alteration of its binding capability to the receptor. The mechanism for the loss of activity of the other mutation of systemin, namely T17A, seems to be different, involving the region which has been shown to play no role in its binding to the receptor but taking part in its activation of response.

Author Information Corresponding Author *Phone:+913323508386 extn. 329. Fax: +9123519755. E-mail: [email protected].

Notes The authors declare no competing financial interest. Author Contributions All the authors contributed to the design and execution of the experiments, analysis of the data as well as writing of the manuscript. All authors have given approval to the final version of the manuscript.


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Acknowledgements Saikat Dutta Chowdhury would like to acknowledge support from the UGC-RFSMS program and subsequent CSIR Senior Research Fellowship. Aditya Kumar Sarkar would like to acknowledge the UGC-RFSMS program for providing fellowship. This research work was partially supported by UGC-MAJOR project [F41-948/2012(SR)] and also by the departmental DST-FIST and UGC-DSA programs. The authors would like to acknowledge the excellent initial study on this problem by Ms. Ankita Pal. The authors would like to thank the editor and the three anonymous reviewers for their constructive comments which helped to improve the manuscript. Associated Content Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

Figure S1-S3, Scatter plots between total solvent accessible surface area (SASA) and different energy terms; Figure S4, Heat maps of residue wise side chain SASA for each system; Table S1, Average radius of gyration of all implicitly simulated systems; Table S2, Average energy of each system simulated in implicit solvent; Table S3, Per-residue wise relative energies of each system simulated in implicit solvent; Figure S5, Convergence plot for implicit solvent simulations. (PDF)


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For Table of Contents Use Only

Effect of Inactivating Mutations on Peptide Conformational Ensembles: The Plant Polypeptide Hormone Systemin Saikat Dutta Chowdhury, Aditya K. Sarkar, Ansuman Lahiri*


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Effect of Inactivating Mutations on Peptide Conformational Ensembles

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