Plant Polypeptide Hormone Systemin Prefers Polyproline II

This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

Article Cite This: ACS Omega 2017, 2, 6831-6843

http://pubs.acs.org/journal/acsodf

Plant Polypeptide Hormone Systemin Prefers Polyproline II Conformation in Solution Saikat Dutta Chowdhury and Ansuman Lahiri* Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata 700009, West Bengal, India S Supporting Information *

ABSTRACT: Systemin, an 18 amino-acid-signaling peptide, was the first plant polypeptide hormone to be discovered. Earlier structural studies involving NMR spectroscopy indicated a lack of definite structure in solution while circular dichroism spectroscopy suggested the presence of left-handed polyproline II (PPII) conformation. Here, we report the results of molecular dynamics simulations of the peptide in explicit solvent with two different force fields, namely, ff99SBildn and ff99IDPs, both of which showed a large propensity for PPII-like conformations in spite of showing differing features for other conformational characteristics. More remarkably, the conformations with predicted chemical shifts that agreed better with the NMR observations had a larger than average PPII content, especially for the ff99IDPs force field. An independent docking calculation of the molecule with the putative receptor SR160 also retained this conformational preference for PPII structure. The results suggest PPII to be an important class of conformation for systemin which may have a role in its bioactivity.

■

INTRODUCTION Systemin was first isolated in 1991 from infected leaves of tomato (Lycopersicon esculentum) and identified as a defenserelated plant polypeptide hormone.1 It was found to be produced as a response to wounding owing to insect attack or mechanical damage. Systemin activates the production of proteinase inhibitors not only in the damaged part but also in distant parts of the plant, thus signaling a system-wide response against herbivore attack. The primary structure of systemin consisting of 18 amino acids (AVQSKPPSKRDPPKMQTD) is largely polar, with a number of acidic and basic residues. It also contains four proline residues at positions 6, 7 and 12, 13. The amino acid arrangement has been described as pseudopalindromic.1 Apart from tomato, closely related sequences to systemin have been observed in some other species of the Solanaceae family including potato (Solanum tuberosum), bell pepper (Capsicum annuum), and black nightshade (Solanum nigrum).2−6 There are also a few species outside the Solanaceae family where the presence of systemin-like sequences has been detected.7,8 A number of experimental studies have been directed toward the elucidation of the conformational characteristics of tomato systemin. A proton nuclear magnetic resonance (NMR) spectroscopy study of systemin concluded that no persistent secondary structure could be observed at neutral pH.9 The chemical shifts observed were found to be similar to the chemical shifts predicted for random coils.9 On the other hand, circular dichroism (CD) studies of tomato systemin at different © 2017 American Chemical Society

temperatures reported a distinct presence of significant amount of polyproline II (PPII) conformation (Figure S1) at low temperature (5 °C) which gradually became less distinct from the random coil state as the temperature was raised up to 80 °C.10 The difference in the interpretation of the NMR data9 and the CD data10 may have arisen because of the lack of 3JNHα coupling data in the NMR study by Russell et al.9 at normal pH. In reports on other peptides, the 3JNHα value of about 5 Hz has been used for inferring the presence of PPII conformation as opposed to the β structure which has been ascribed a value of about 10 Hz.11 The coupling results show good correlation with low-temperature CD studies investigating characteristic CD signatures for the PPII structure.12 Another NMR study of tomato systemin at low pH (pH 3.2) reported the presence of the cis-isomer of proline giving rise to a Z-like β-sheet structure.13 A later CD study also reported a random coil structure for systemin with some evidence of the presence of β-sheet and β-turn motifs.14 Currently, an active debate is going on about whether peptides with apparent conformational characteristics showing random coil nature can actually possess some amount of polyproline-like structure.15 This becomes very relevant for the present case because systemin has a substantial proline-rich region. The sequence also lacks aromatic residues which are Received: May 28, 2017 Accepted: September 18, 2017 Published: October 17, 2017 6831

DOI: 10.1021/acsomega.7b00691 ACS Omega 2017, 2, 6831−6843

ACS Omega

Article

we predicted proton chemical shifts from the conformational ensembles and compared the chemical shift distributions with the experimentally observed shifts. We also investigated the propensity of the formation of PPII conformation in different regions of the systemin sequence using a polyproline assignment algorithm. To acquire further insights into the extent of similarity or difference in the conformational ensembles generated by the two force fields, ff99SBildn and ff99IDPs, we carried out principal component analysis (PCA)29,30 followed by cluster analysis. The majority of the variance in a data set is usually captured by the first few principal components (PCs). As conformational flexibility is dependent on the potential energy of the molecule, the PCA also reveals the characteristics of the underlying free-energy landscape. In the case of clustering MD data, one essentially wants to cluster conformations based on their underlying free-energy landscape to find out the metastable states of the protein. Because both the force fields tested by us were derived from the AMBER ff99SB force field, we have also performed shorter 150 ns MD simulations on the representative conformations of the largest clusters from both the AMBER-derived force fields using the CHARMM36m force field, a very recent force field for CHARMM31 recommended for small peptides and IDPs32 to check the consistency of our results. We also reported preliminary results on the probable location and preferred conformations of systemin in its interaction with a putative 160 kD receptor (SR160).33

known to reduce the propensity for taking up a polyproline-like structure.16 A large number of studies now indicate the existence of a heterogeneous group of small- to medium-sized peptides with unusual proline-rich sequence characteristics (for a recent review, see ref 17). It is considered likely that these sequence characteristics give rise to some structural propensities that help these molecules to carry out a large number of biological functions. The occurrence of the left-handed PPII conformation is considered to be essential for the biological activity of these proline-rich peptides.17 A structure−function study on tomato systemin has established the first 14 residues to be responsible for binding to its putative receptor, whereas the last 4 residues were considered important for activation.18 Recently, a DNA aptamer has been designed to detect systemin in solution.19 The G-rich aptamer sequence is thought to fold into a quadruplex structure and can selectively bind to the systemin sequence or some of its fragments but does not bind if the sequence is scrambled. This observation suggests that the systemin sequence may have some conformational preferences that facilitate its specific binding. Previously, alanine scanning and deletion studies reported differential modulation of the activity of systemin, in particular, a major loss of function owing to alanine substitution at positions 13 and 17 and a comparatively less extensive loss of function owing to substitution at position 12.20 We used replica-exchange molecular dynamics (REMD) simulation in an implicit solvent environment to study the conformational ensembles of the wild-type systemin along with its 17 variants to observe that the conformational ensembles of the 17 variants differed in their population distributions, suggesting a less flexible structure for alanine substitutions at positions 12 and 13 but not for position 17.21 The simulations revealed very little preference for the wild-type sequence to adopt α-helical and β-sheet structures. On the other hand, two regions containing diproline segments showed a tendency to adopt PPII structures.21 In this work, we have carried out extensive molecular dynamics (MD) simulation of tomato systemin in explicit solvent using two different force fields, ff99SBildn22 and ff99IDPs.23 Calculations have shown that the conformational ensembles of relatively unstructured proteins simulated with different force fields can show large differences.24 The AMBER ff99SBildn force field22 incorporates corrections to the AMBER ff99SB force field25 and is currently recommended for protein simulations. The improvement was made by modifying the side-chain torsion potentials of four amino acids (isoleucine, leucine, aspartic acid, and asparagine) to better reproduce NMR data. However, although the performance of this force field has shown considerable success in simulating the folding of small proteins, its performance for unstructured proteins is not so well-studied. We have also tested another force field, the AMBER ff99IDPs, that has been constructed and subsequently tested for a more accurate description of the conformational features of intrinsically disordered proteins.23,26 The reason for the development of this force field was that Φ/Ψ dihedral distribution of ordered and disordered regions, extracted from the protein data bank (PDB) entries, was significantly different from simulated distributions.23 To correct these discrepancies, the authors used residue-specific grid-based energy correction maps (CMAP)27,28 for the Φ/Ψ dihedral distribution of eight disorder-promoting residues (A, G, P, R, Q, S, E, and K). The conformational ensembles generated from the MD simulations were then structurally characterized. Additionally,

■

RESULTS AND DISCUSSION Distribution of the Radius of Gyration. Computational study of the ensemble of conformations adopted by intrinsically unstructured proteins and peptides is in principle capable of providing a wealth of structural information but as yet faces a problem of disagreement between predictions made utilizing different force fields.24 We have used two different AMBERderived force fields in our MD calculations for the evaluation and comparison of the conformational ensembles of systemin and also a CHARMM force field for checking the consistency of our major results. In Figure 1, we have plotted the distribution of the radius of gyration (Rg) of systemin for investigating the ranges for the linear dimensions of the peptide

Figure 1. Distribution of the radius of gyration of systemin for calculations involving the ff99SBildn and ff99IDPs force fields. 6832


ACS Omega

Article

Figure 2. Residue-wise secondary structure propensity. Two force fields are represented in columns (a) ff99SBildn and (b) ff99IDPs, where the Xaxis denotes residues and the Y-axis denotes the propensity for the respective secondary structures.

Figure 3. Residue-wise plot of the PPII propensities for the (a) ff99SBildn and (b) ff99IDPs force fields.

as predicted by these two force fields. The overall features of the distributions were similar for both the force fields. The distributions showed a large population near 9 Å and a small amount of population extending up to 16 Å for both the force fields. For the sampling of conformations over 600 ns, the average value of the Rg was calculated as about 9.7 Å for both the ff99SBildn and ff99IDPs force fields. We have also plotted the time evolution of the Rg and root-mean-square deviation (rmsd) from the initial structure (Figures S2 and S3) for both trajectories. These results showed that after the initial fluctuations, the ff99IDPs force field sampled conformations with relatively less overall fluctuation compared to the conformations sampled by the ff99SBildn force field. Compared to the AMBER family of force fields, calculations using the CHARMM36m force field sampled conformations with larger Rg values irrespective of the starting peptide conformation (Figure S4) showing its preference for generating extended conformations for the peptide. One can compare the average values of the radius of gyration (Rg) of systemin from our simulation with that expected on the basis of the assumption that the polypeptide is a random coil with conformational distributions governed by the Flory isolated-pair hypothesis.34 On the basis of this hypothesis,

Kohn et al.35 proposed that Rg of a random coil polypeptide with N residues should scale as Rg = R0Nν, where R0 = 1.927 Å and ν = 0.598. For an 18 amino acid polypeptide, the estimated Rg based on the scaling relationship35 was obtained as 10.85 Å. This implied that although systemin did not seem to possess any persistent secondary structure, it did not behave as a completely random coil either. Secondary Structure Propensities. We calculated the residue-wise secondary structure propensities (Figure 2) from the trajectories of the 600 ns MD simulations. SchweitzerStenner has proposed to consider the conformational distribution of a polypeptide as a random coil-like structure if it samples the allowed Ramachandran region almost uniformly, leading to an approximate 30% propensities for the righthanded α-helix, the β-sheet, and the left-handed PPII conformations.36 Our DSSP37 calculations on the trajectories for both the force fields (Figure 2) showed almost no propensity for the residues to adopt the right-handed α-helix conformation, and accordingly, the conformational distribution of systemin could not be called a random coil. The secondary structure propensities as assigned by the DSSP algorithm showed a very insignificant tendency to adopt β-sheet, α-helix, or 310 helix conformations. On the other hand, 6833


ACS Omega

Article

Figure 4. Residue-wise time series plots for the presence of PPII conformation as assigned by the PROSS algorithm for the (a) ff99SBildn and (b) ff99IDPs force fields. Red color indicates the presence of the PPII structure, whereas blue color indicates the absence of the PPII structure.

systemin sequence (Figure 3). However, there were noticeable differences in the predicted residue-wise PPII propensities for the two force fields. Among these differences, the most prominent was the PPII propensities of Pro7 and Pro13 residues. The ff99IDPs force field showed stronger PPII propensities for these two residues compared to the ff99SBildn. Although the region with the PPII structure as indicated by the PROSS program in our simulated ensembles did not cover very long stretches as might be expected for trans-polyproline and for proteins/peptides with a large content of proline such as collagen, the findings were consistent with reports that showed that PPII conformations could occur over relatively short stretches.40,41 The amino acids in the central region of systemin were not found to be favorable for the formation of the PPII structure. In Figure 4, we have plotted the time evolution of the PPII propensities in systemin for the two force fields. Apparently, the ff99IDPs force field generated stabler and more concentrated PPII segments near the proline residues compared to the ff99SBildn force field. PROSS calculations showed (Figure S6) that, in general, the trajectory generated by the CHARMM36m force-field-sampled conformations in which the PPII structure was favored over the whole peptide in comparison with the conformations sampled by the AMBER family of force fields. However, the propensity of the PPII structure for the diproline regions was not as high as

it identified specific regions having a strong preference for the bend or the turn conformations. We also observed (Figure S5) that these bend or turn structures were relatively stable over the course of the simulations once they were formed. Secondary structure calculations using the DSSP program showed that there was no significant type of secondary structures other than bends in the ensembles sampled by the CHARMM36m force field (data not shown). The PPII helical conformation is increasingly being associated with unstructured proteins and peptides, and it is also being suggested as the third most abundant conformation after α-helix and β-sheet structures.38 Not surprisingly, proline is present in many such PPII helices.39 The presence of two diproline segments in systemin together with the experimental observation10 of PPII-like conformation at low temperatures and our earlier observation from implicit solvent simulation21 prompted us to analyze the ensembles generated by the two force fields for the occurrence of the PPII structure. Because DSSP did not provide any information about the possibility of formation of the PPII helix structure, we used the PPII assignment method embodied in the PROSS (http:// folding.chemistry.msstate.edu/utils/pross.html) structure assignment tool. For both the ff99SBildn and ff99IDPs force fields, the PPII propensities showed a more or less symmetrical distribution commensurate with the pseudopalindromic nature of the 6834


ACS Omega

Article

Figure 5. Residue-wise contribution (Å) to the first three PCs for the (a) ff99SBildn and (b) ff99IDPs force fields. X-axis corresponds to residue numbers and Y-axis shows the contribution for individual PCs.

first four PCs were used for the ff99IDPs data. In Figure S9, we plotted the SSR/SST ratio for both the force fields. Inspection of the elbow point showed that a cluster count of 5 for both the force fields could be an optimum choice. After clustering the ff99SBildn data on a six-dimensional PC subspace and the ff99IDPs data on a four-dimensional PC subspace, the conformations were plotted in the first two PC planes (Figure 6). Analysis of the plot showed that conformations sampled by the two force fields were not identical. The overall spread of the conformations sampled by the ff99SBildn force field indicated a relatively flat free-energy landscape compared to that obtained from the ff99IDPs force field. Bioinformatic Analysis of Systemin Chemical Shifts. Experimental observations indicate that, at neutral pH and physiological temperature, the peptide hormone systemin does not have a strong structural preference. The first proton NMR study of the molecule, in fact, assigned most of the residues, except for a few C-terminal ones, to the random coil state.9 Within the intervening period after the experimental study of systemin conformation by proton NMR spectroscopy, there has been a lot of activity on the structural bioinformatics front that

in the conformations sampled with the AMBER family of force fields. This observation was also reflected in the timeline plots (Figure S7) of the PPII structure from the ensemble generated by the CHARMM36m force field. Principal Component Analysis. For the analysis of the PCA data, we have plotted the scree plots (Figure S8) for both the force fields to estimate the proportion of variance captured by each PCs based on their eigenvalue rank. Interestingly, PC1 of ff99IDPs ensemble accounted for around 73% of the variance alone. Figure 5 shows the residue-wise contribution to the first three PCs for both the force fields. As PCA captures the essential motion of the molecule and PC1 accounts for the majority of the variance within the data, we found that both the force fields sampled conformations in which the central region of the peptide molecule was more rigid than both the terminal regions. Clustering. The conformations were clustered using the PC subspace data. Simple Euclidean distance of points in the PC subspace was used as the similarity measure between two conformations. On the basis of the scree test (Figure S8), we utilized the first six PCs for the ff99SBildn data, whereas the 6835


ACS Omega

Article

Figure 6. Clustered conformations for the (a) ff99SBildn and (b) ff99IDPs force fields were projected on the PC1−PC2 plane. (In both the plots, cluster 1 is red, cluster 2 is green, cluster 3 is blue, cluster 4 is cyan, and cluster 5 is purple.)

Figure 7. Overall neighbor-corrected structural propensity plot for systemin.50

chemical shift of a residue is dependent on the sequence also.46 Wishart et al.46 measured 1H, 13C, and 15N chemical shifts from Gly−Gly−X−Y−Gly−Gly peptides (where X can be any of the 20 amino acids and Y can be either Ala or Pro) in a consistent experimental condition and compared these shift values systematically. They found significant differences among the chemical shifts owing to the presence of the prolines in the Yposition of the hexapeptide. A lot of work has been devoted to discriminate between the chemical shift data that came out of a structured region and the so-called “random coil” region. A basic step in this regard has been the compilation of random coil chemical shift libraries by various groups.47−49 In Table S1, we have reported the results of calculations of chemical shifts using the ncIDP predictor49 of the α-protons of systemin from sequence information only, assuming the polypeptide to be entirely in the random coil state. These shift values have been compared subsequently to the experimental shifts reported by Russell et al.9

strove to use the chemical shift information to gain insight into the behavior of proteins and peptides which show some degree of structural disorder. Chemical shifts are relatively accurate parameters that reflect the local structural properties of a nucleus. In proteins, they get influenced by the primary, secondary, and tertiary structure of the protein. Proton chemical shifts in proteins are sensitive to several factors such as conformation, hydrogen bonding, electric field, ring currents, and temperatures.42,43 Also, the degree of sensitivity varies depending on the chemical groups. Several studies44,45 showed that α-proton chemical shifts and the secondary structure are related in such a way that α-helical conformation induces upfield shift, whereas β-sheets induce downfield shift from random coil values. Hydrogen bonds strongly influence amide proton chemical shifts which show a sensitive dependence on the hydrogen bond donor−acceptor distance. Ring currents of the nearby aromatic groups are other important contributors which significantly affect the α-proton chemical shifts. The 6836


ACS Omega

Article

Figure 8. Histograms of the predicted α-proton chemical shifts from the ff99SBildn and ff99IDPs force fields. In each histogram, yellow color indicates the ff99SBildn and blue color indicates the ff99IDPs force fields. The red dot indicates the position of the experimental chemical shift.

The random coil chemical shifts calculated according to three different prescriptions proposed by Schwarzinger et al.,47 Wang and Jardetzky,48 and Tamiola et al.49 showed some difference among themselves. The closest set of predictions according to the rmsd from the experiment came from the library proposed by Tamiola et al.49 The calculation of the data set in this library utilized a method of taking into account the context dependence of an amino acid residue and its consequence on the chemical shift values. The calculations showed that when context dependence was taken into account, the Russell et al.9 data were quite close to the Tamiola et al.49 prediction. This was consistent with the suggestion that systemin may have a substantial amount of strongly disordered regions. Tamiola and Mulder50 also proposed a method to investigate the residue-wise structural propensity, given an amino acid sequence and chemical shift data with the help of their neighbor-corrected chemical shift library. As shown in Figure 7,

the neighbor-corrected structural propensity calculations identified the regions 11−13 as having significant structural propensity relative to the other regions. Although their original method classified the structures as either right-handed α-helical or extended β-sheet, those calculations might be too restrictive for the systemin sequence. If we just infer from these calculations the degree of expected structural disorder locally from the given chemical shift data, the data strongly point to the region spanning residues 11−13 in systemin as having structural properties significantly different from a random coil. Chemical Shift Prediction from Conformational Ensembles. In Figure 8, we have plotted the distribution of the α-proton chemical shift calculated using the shift prediction protocol SPARTA+51 on our simulation trajectories obtained with the two force fields. In general, the predictions strongly overlapped only for a few residues and in some cases showed nonoverlapping values with the experimentally determined values. The predictions using both the force fields better 6837


ACS Omega

Article

Figure 10 shows the scatter plots of PPII propensity versus αproton chemical shift rmsd for both the force fields. The plots show slightly weak negative correlation between the chemical shift rmsd and the PPII propensity calculated as −0.256 for the ff99SBildn force field and −0.413 for the ff99IDPs force field. For comparison, the correlation is even weaker for PPII versus Rg (0.193 and 0.29 for the two force fields ff99SBildn and ff99IDPs, respectively). Comparison of Structures from the Two Force Fields. We isolated 20 structures based on the lowest α-proton chemical shift rmsds (listed in Table S2) from the conformational ensembles generated with both the force fields (Figure 11). Although the ff99SBildn force field showed distinct groups of structures, the structures obtained from the ff99IDPs force field were more homogeneous and showed slightly extended conformations. In Figure 12, we have plotted the residue-wise PPII propensity for both the force fields. It is clear from the figure that for both the force fields, the conformations showing low rmsd values (averaged over the lowest 20 structures) for αproton chemical shifts were assigned a larger propensity for the PPII structure than the average over the entire ensemble. However, there was some difference in the nature of the increased PPII propensities for the two force fields. For the ff99SBildn force field, the increase in the PPII content was noticeable for the residues which were already showing significant PPII propensity over the whole trajectory. Only Val2 and Met15 showed a marked increase in the PPII content for the 20 structures with the lowest α-proton chemical shift rmsd values compared to that of the whole trajectory. By contrast, the ff99IDPs force field had very different PPII propensities between the 20 structures with the lowest αproton chemical shift rmsd and the entire trajectory. Residues which already had high PPII content for the entire trajectory showed only slight increase in the PPII content. On the other hand, residues Val2, Gln3, Ser4, Ser8, Lys9, Arg10, and Gln16 showed noticeable increase in the PPII content. Owing to this increase in the PPII content of additional residues in the 20 structures with the lowest α-proton chemical shift rmsd values, a stretch from residues 2−14 was seen to favor the PPII conformation.

matched with the experimental data for Lys5, Pro6, Asp11, and Pro12. To have a qualitative idea of the force field that generated conformational ensembles closer to the experimental chemical shifts, we calculated and plotted the quantity (|exp − ildn| − | exp − idp|), where the |...| symbol denotes the rmsd value of the simulated quantity from the experimental chemical shift,9 (exp) denotes the α-proton chemical shift of a residue, and ildn and idp denote the ensembles generated from the ff99SBildn and ff99IDPs force fields, respectively (Figure 9). We have also

Figure 9. Plot of difference in magnitudes of the rmsd discrepancy of α-proton shift prediction derived from the two force fields used.

plotted the quantity (|exp − rc|), where the |...| symbol denotes absolute value, exp is the experimental α-proton chemical shift of a residue from Russell et al.,9 and rc denotes the chemical shift of a residue predicted using the random coil chemical shift library proposed by Tamiola et al.49 From Figure 9, it is apparent that the best model for the experimental data is still provided by the random coil chemical shift library. Between the two force fields, the ff99SBildn force field generated conformations for which the SPARTA+ predictions were apparently closer to the experimental shift values.

Figure 10. Scatter plots of α-proton chemical shift rmsd and PPII propensity of both force fields (a) ff99SBildn and (b) ff99IDPs. 6838


ACS Omega

Article

Figure 11. Twenty structures from simulations with the (a) ff99SBildn and (b) ff99IDPs force fields based on the lowest α-proton chemical shift rmsd values. Blue and red colors indicate N- and C-terminals, respectively.

Figure 12. Residue-wise PPII propensity from simulations with the (a) ff99SBildn and (b) ff99IDPs force fields. Blue color indicates the average PPII propensity of 20 structures based on the lowest α-proton chemical shift rmsd values, whereas red color denotes the average PPII propensity of the entire trajectory.

We also observed that residue-wise PPII propensities for conformations that showed lower chemical shift rmsd values as sampled by the ff99IDPs force field resulted in an increased overall PPII content. This feature of the ff99IDPs force field might have arisen because of the way it was parameterized with respect to its base force field, that is, ff99SBildn. During the construction of the new ff99IDPs force field, Wang et al.23 modified the backbone torsion energy term for the eight disorder-promoting residues using CMAP.27,28 They prepared the dihedral angle distribution benchmark data from 42 774 disordered fragments. Distribution of dihedral angles of the eight disorder-promoting residues into four principal regions (i.e., αL, αR, PPII, and β-region) showed significant difference compared to that of the ordered structures. A major part of this difference originated because of the increased PPII population in the benchmark data set. Over the course of the parameter optimization, Wang et al.23 systematically tried to reproduce the observed increased propensity for the PPII conformation in their ff99IDPs force field. Interaction of Systemin with Its Putative Receptor. In Figure 13, we have shown the modeling results for the interaction of systemin with a model of the extracellular domain of the SR160 receptor using the GalaxyPepDock protocol.52 All of the generated models showed the peptide to be preferentially located near the 68 amino acid long island region53 close to the C-terminal region of the modeled SR160 receptor. We also calculated the PPII propensity of the 10 peptides in their bound conformations as modeled by GalaxyPepDock. Figure 14 shows

Figure 13. Ten docked systemin peptide onto homology modeled SR160. For docking, GalaxyPepDock was used.

the average PPII propensity of these 10 conformers. It was noticed that the nature of the PPII propensity observed in these 10 modeled peptides was similar to that observed in the 20 conformations isolated on the basis of the lowest α-proton chemical shift rmsd value from the conformational ensemble generated with the ff99IDPs force field.

■

CONCLUSIONS In spite of being the first plant polypeptide hormone to be discovered, we have surprisingly little mechanistic understanding of the mode of action of systemin. A number of experimental and theoretical studies have probed its conformation in solution and came up with varied answers. 6839


ACS Omega

■

Article

MATERIALS AND METHODS

MD Simulations. The energy minimization and MD simulations were performed using the AMBER 12 program suite.54 The pmemd.MPI module was used for minimization, whereas the pmemd.cuda module was used for equilibration and production runs. The simulations were started from an uncapped systemin polypeptide in extended conformation created using the leap module. We used TIP3P water model55 for solvation in an octahedron box, where 10 Å separation was used between the peptide and box walls. Periodic boundary condition was applied during simulations. Both systems were neutralized by replacing two water molecules with two Cl−. After this, energy minimization was performed in two steps. First, the solute was kept fixed, whereas water and ions were free to move during minimization. Next, the entire system was energy-minimized. In each energy minimization process, 1000 steepest decent steps were followed by 1000 conjugate gradient steps. The energy-minimized system was then heated to 298 K in an NVT simulation for 20 ps while keeping the solute fixed. Next, the restraint on the solute was removed, and the system was equilibrated under 1 atm pressure for 100 ps. Production runs were carried out under 1 atm pressure and at 298 K temperature where a time step of 2 fs was used, and the coordinates were saved at 4 ps interval. Throughout the simulation, a 10 Å cutoff was used for the calculation of nonbonded interactions, and SHAKE algorithm56 was used to constrain the bonds involving hydrogens. Long-range electrostatic interactions were treated with the particle mesh Ewald (PME) method.57 Identical protocols were used for the two force fields, namely, AMBER ff99SBildn22 and AMBER ff99IDPs,23 employed in this work. The production runs were continued for 600 ns. We have plotted (Figures S10 and S11) the residue-wise cumulative averages of predicted α-proton chemical shifts for both ensembles to ensure that the chemical shifts were reasonably converged. The radius of gyration (Rg) and residue-wise secondary structures were calculated using the cpptraj module of AmberTools 14.58 The secondary structure characteristics were calculated using DSSP embedded in AmberTools 14. Because DSSP does not report PPII conformation, we used the dihedral angle-based secondary structure assignment tool PROSS. It uses only the backbone torsion angles for the secondary structure assignment. The torsion angle space is divided into a two-dimensional (Φ/Ψ) grid of cells referred to as mesostates. Four of these mesostates are mapped to the PPII conformation with Φ ranging from −45° to −115° and Ψ ranging from 120° to 180°. Calculated Φ/Ψ angles of each residue were then mapped to the grid and subsequently classified into different secondary structures. α-Proton chemical shifts were predicted using SPARTA+.51 Visualization and plotting were done using pymol59 and R statistical software package.60 Principal Component Analysis. PCs were obtained from the covariance matrix of the Cartesian coordinate data set. Eigenvectors and eigenvalues were calculated from this covariance matrix, and then the eigenvectors were rearranged according to their eigenvalues in decreasing order. The eigenvector with the highest eigenvalue was the first PC and so on. The first PC contains the highest proportion of variance in the data. We used the R package bio3D61 for PCA. Cartesian coordinate information of the C-α atoms of the peptide at every

Figure 14. PPII content of systemin modeled using the GalaxyPepDock protocol.

Bioinformatic analysis of the observed chemical shifts from the proton NMR study of systemin9 indicated the nearestneighbor prediction model49 to be the closest approximation to its conformation, and the polypeptide conformation was predicted to be nearly disordered. Further analysis indicated the possibility that the contiguous region spanning positions 11−14 may have some structural bias. Presence of disorder predicted by the bioinformatic analysis motivated us to study the ensemble of conformations by MD simulations. We utilized two force fields, ff99SBildn22 and one of its variants specifically designed for studying unstructured proteins, ff99IDPs.23 The ensembles generated by the two force fields were compared for distributions over different conformational characteristics which were found to be different in various respects. The radius of gyration was much more uniformly distributed over a large range in ff99SBildn than in ff99IDPs. Although the peptide was found to be flexible overall, the PCA revealed that the central region contributed very less to the first PC, implying reduced conformational variability in that region for both the force fields. The PCA of the conformations generated using the two force fields showed clear differences in their ability to sample the free-energy landscape of systemin. Although ff99SBildn showed overlapping conformational clusters indicative of a flat energy landscape, ff99IDPs partitioned the population into a relatively fewer number of segregated clusters, implying a more rugged free-energy landscape. However, our simulation data using the two force fields showed a general trend favoring PPII conformation about the two diproline segments, whereas residues at the center and toward both ends seemed to disfavor PPII conformation. Remarkably, we also observed that the conformations that showed lower rmsd values between the predicted and experimentally observed chemical shifts were more likely to show enhanced propensity for the PPII structure. It is also interesting to note that the PPII-forming regions were within the N-terminal region spanning the first 14 residues that were found to be responsible for binding with the receptor.18 This prompted us to speculate that the PPII conformation may play a role in the bioactivity of systemin. 6840


ACS Omega

Article

400 ps interval was sampled to build the 54 × 1500 input data matrix for the PCA. Clustering. We used hierarchical algorithm for clustering the PC subspace using R statistical software package.60 The number of PCs for clustering was decided based on the scree test.62 To determine an optimal number of clusters, we calculated the SSR/SST ratio using the kmeans function of the R software suit. The SSR is known as the between-cluster sum of squares, which is calculated as the sum of squared distances of each cluster mean to the global sample mean. SST is called the total sum of squares and is calculated as the sum of squared distances of each component to the global sample mean. The SSR/SST ratio lies between 0 and 1, and the value increases with the increase in the number of clusters. To determine the optimum number of clusters, one looks for an “elbow” in the curve which suggests no significant gain in new information owing to an increase in the cluster number. Homology Modeling. We have modeled the putative systemin receptor SR16033 for further studying the binding interaction with systemin. The primary sequence of SR160 from Lycopersicon peruvianum (accession number AAM48285.1) was downloaded from the NCBI protein database. It is a 1207 amino acid long protein in which 408 residues in the C-terminal region contain the transmembrane and a Ser−Thr kinase domain.53 As systemin is known to bind in the extracellular space, we have removed this region before building the receptor model of SR160. MODELLER63 version 9.15 was used for modeling SR160. For template identification, we searched the pdb_95.pir database that contains nonredundant PDB sequences at 95% sequence identity. On the basis of the sequence identity as well as crystallographic resolution, we used the brassinosteroidinsensitive 1 (BRI1, PDB ID: 3RGZ) protein as the template. It had 60% sequence identity with SR160. After target-template alignment, we found long stretches of gaps in the N and C termini (38 and 11 residues, respectively) of the target sequence, which were deleted before building the model. We generated 20 models, and the model with the lowest DOPE score64 was used for further docking. Docking. The GalaxyPepDock server52 was used for docking the systemin peptide with the modeled SR160. The method follows a fast docking protocol based on the receptor structure and the peptide ligand sequence. On the basis of the sequence characteristics of the peptide, it combines information from similar interactions in a structure database and energy optimization. It performs similarity-based docking by finding templates from the database of experimentally determined structures and building models using energy-based optimization that allows for structural flexibility. The server can therefore effectively model the structural differences between the template and target protein/peptide complexes.

■

■

using the DSSP method for AMBER data, residue-wise PPII propensity for CHARMM36m data (Figure S6), time evolution of residue-wise PPII structure for CHARMM36m data, scree plot for AMBER data, SSR/ SST ratio for different cluster counts for AMBER data, predicted α-proton chemical shifts from systemin sequence, chemical shift rmsd of the lowest 20 conformations for AMBER data, and residue-wise cumulative averages of predicted α-proton chemical shifts for AMBER data (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (A.L.). ORCID

Ansuman Lahiri: 0000-0002-7398-4114 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS S.D.C. acknowledges the support from the UGC-RFSMS Program and a subsequent CSIR Senior Research Fellowship. This research work was partially supported by the UGCMAJOR Project [F41-948/2012(SR)] and also by the departmental DST-FIST and UGC-DSA Programs.

■

REFERENCES

(1) Pearce, G.; Strydom, D.; Johnson, S.; Ryan, C. A. A Polypeptide from Tomato Leaves Induces Wound-Inducible Proteinase Inhibitor Proteins. Science 1991, 253, 895−897. (2) Constabel, C. P.; Yip, L.; Ryan, C. A. Prosystemin from potato, black nightshade, and bell pepper: primary structure and biological activity of predicted systemin polypeptides. Plant Mol. Biol. 1998, 36, 55−62. (3) Pearce, G.; Moura, D. S.; Stratmann, J.; Ryan, C. A. Production of multiple plant hormones from a single polyprotein precursor. Nature 2001, 411, 817−820. (4) Pearce, G.; Ryan, C. A. Systemic signaling in tomato plants for defense against herbivores: isolation and characterization of three novel defense-signaling glycopeptide hormones coded in a single precursor gene. J. Biol. Chem. 2003, 278, 30044−30050. (5) Pearce, G.; Siems, W. F.; Bhattacharya, R.; Chen, Y.-C.; Ryan, C. A. Three hydroxyproline-rich glycopeptides derived from a single petunia polyprotein precursor activate defensin I, a pathogen defense response gene. J. Biol. Chem. 2007, 282, 17777−17784. (6) Pearce, G.; Bhattacharya, R.; Chen, Y.-C.; Barona, G.; Yamaguchi, Y.; Ryan, C. A. Isolation and characterization of hydroxyproline-rich glycopeptide signals in black nightshade leaves. Plant Physiol. 2009, 150, 1422−1433. (7) Chen, Y.-C.; Siems, W. F.; Pearce, G.; Ryan, C. A. Six peptide wound signals derived from a single precursor protein in Ipomoea batatas leaves activate the expression of the defense gene sporamin. J. Biol. Chem. 2008, 283, 11469−11476. (8) Pearce, G.; Bhattacharya, R.; Chen, Y.-C. Peptide signals for plant defense display a more universal role. Plant Signaling Behav. 2008, 3, 1091−1092. (9) Russell, D. J.; Pearce, G.; Ryan, C. A.; Satterlee, J. D. Proton NMR assignments of systemin. J. Protein Chem. 1992, 11, 265−274. (10) Toumadje, A.; Johnson, W. C., Jr. Systemin has the characteristics of a poly(L-proline) II type helix. J. Am. Chem. Soc. 1995, 117, 7023−7024. (11) Shi, Z.; Olson, C. A.; Rose, G. D.; Baldwin, R. L.; Kallenbach, N. R. Polyproline II structure in a sequence of seven alanine residues. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 9190−9195.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.7b00691. Left-handed PPII helix and Ramachandran plot showing characteristic dihedral angles along with regular secondary structures, time evolution of radius of gyration and root-mean-square deviation for AMBER data, distribution of radius of gyration for CHARMM36m data, time evolution of residue-wise secondary structure calculated 6841


ACS Omega

Article

(12) Lam, S. L.; Hsu, V. L. NMR identification of left-handed polyproline type II helices. Biopolymers 2003, 69, 270−281. ́ sarek, G.; Kalbitzer, H. R.; Mucha, P.; Rekowski, P.; (13) Sló Kupryszewski, G.; Giel-Pietraszuk, M.; Szymański, M.; Barciszewski, J. Mechanism of the activation of proteinase inhibitor synthesis by systemin involves β-sheet structure, a specific DNA-binding protein domain. J. Struct. Biol. 1995, 115, 30−36. (14) Mucha, P.; Szyk, A.; Rekowski, P.; Kupryszewski, G.; Slósarek, G.; Barciszewski, J. Conformation of systemin, a polypeptide activator of proteinase inhibitor synthesis in plants. Collect. Czech. Chem. Commun. 1999, 64, 553−558. (15) Rath, A.; Davidson, A. R.; Deber, C. M. The structure of “unstructured” regions in peptides and proteins: role of the polyproline II helix in protein folding and recognition. Biopolymers 2005, 80, 179−185. (16) Brown, A. M.; Zondlo, N. J. A propensity scale for type II polyproline helices (PPII): aromatic amino acids in proline-rich sequences strongly disfavor PPII due to proline−aromatic interactions. Biochemistry 2012, 51, 5041−5051. (17) Vitali, A. Proline-rich peptides: Multifunctional bioactive molecules as new potential therapeutic drugs. Curr. Protein Pept. Sci. 2015, 16, 147−162. (18) Meindl, T.; Boller, T.; Felix, G. The plant wound hormone systemin binds with the N-terminal part to its receptor but needs the C-terminal part to activate it. Plant Cell 1998, 10, 1561−1570. (19) Bing, T.; Chang, T.; Yang, X.; Mei, H.; Liu, X.; Shangguan, D. G-quadruplex DNA aptamers generated for systemin. Bioorg. Med. Chem. 2011, 19, 4211−4219. (20) Pearce, G.; Johnson, S.; Ryan, C. A. Structure-activity of deleted and substituted systemin, an 18-amino acid polypeptide inducer of plant defensive genes. J. Biol. Chem. 1993, 268, 212−216. (21) Chowdhury, S. D.; Sarkar, A. K.; Lahiri, A. Effect of inactivating mutations on peptide conformational ensembles: the plant polypeptide hormone systemin. J. Chem. Inf. Model. 2016, 56, 1267−1281. (22) Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J. L.; Dror, R. O.; Shaw, D. E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Struct., Funct., Bioinf. 2010, 78, 1950−1958. (23) Wang, W.; Ye, W.; Jiang, C.; Luo, R.; Chen, H.-F. New force field on modeling intrinsically disordered proteins. Chem. Biol. Drug Des. 2014, 84, 253−269. (24) Rauscher, S.; Gapsys, V.; Gajda, M. J.; Zweckstetter, M.; de Groot, B. L.; Grubmüller, H. Structural ensembles of intrinsically disordered proteins depend strongly on force field: a comparison to experiment. J. Chem. Theory Comput. 2015, 11, 5513−5524. (25) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins: Struct., Funct., Bioinf. 2006, 65, 712−725. (26) Ye, W.; Ji, D.; Wang, W.; Luo, R.; Chen, H.-F. Test and evaluation of ff99IDPs force field for intrinsically disordered proteins. J. Chem. Inf. Model. 2015, 55, 1021−1029. (27) MacKerell, A. D.; Feig, M.; Brooks, C. L. Improved treatment of the protein backbone in empirical force fields. J. Am. Chem. Soc. 2004, 126, 698−699. (28) Mackerell, A. D.; Feig, M.; Brooks, C. L. Extending the treatment of backbone energetics in protein force fields: Limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J. Comput. Chem. 2004, 25, 1400−1415. (29) Pearson, K. On Lines and Planes of Closest Fit to System of Points in Space. Philos. Mag. 1901, 2, 559−572. (30) Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 1933, 24, 417. (31) Brooks, B. R.; Brooks, C. L., III; Mackerell, A. D., Jr.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.;

Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545−1614. (32) Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B. L.; Grubmüller, H.; MacKerell, A. D. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 2017, 14, 71−73. (33) Scheer, J. M.; Ryan, C. A. A 160-kD systemin receptor on the surface of Lycopersicon peruvianum suspension-cultured cells. Plant Cell 1999, 11, 1525−1535. (34) Flory, P. J. Statistical Mechanics of Chain Molecules; Wiley: New York, 1969; p 252. (35) Kohn, J. E.; Millett, I. S.; Jacob, J.; Zagrovic, B.; Dillon, T. M.; Cingel, N.; Dothager, R. S.; Seifert, S.; Thiyagarajan, P.; Sosnick, T. R.; Hasan, M. Z.; Pande, V. S.; Ruczinski, I.; Doniach, S.; Plaxco, K. W. Random-coil behavior and the dimensions of chemically unfolded proteins. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 12491−12496. (36) Schweitzer-Stenner, R. Conformational propensities and residual structures in unfolded peptides and proteins. Mol. BioSyst. 2012, 8, 122−133. (37) Kabsch, W.; Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22, 2577−2637. (38) Chebrek, R.; Leonard, S.; de Brevern, A. G.; Gelly, J.-C. PolyprOnline: polyproline helix II and secondary structure assignment database. Database 2014, 2014, bau102. (39) Shi, Z.; Chen, K.; Liu, Z.; Kallenbach, N. R. Conformation of the backbone in unfolded proteins. Chem. Rev. 2006, 106, 1877−1897. (40) Kentsis, A.; Mezei, M.; Gindin, T.; Osman, R. Unfolded state of polyalanine is a segmented polyproline II helix. Proteins: Struct., Funct., Bioinf. 2004, 55, 493−501. (41) Makowska, J.; Rodziewicz-Motowidło, S.; Baginska, K.; Makowski, M.; Vila, J. A.; Liwo, A.; Chmurzynski, L.; Scheraga, H. A. Further evidence for the absence of polyproline II stretch in the XAO peptide. Biophys. J. 2007, 92, 2904−2917. (42) Mulder, F. A. A.; Filatov, M. NMR chemical shift data and ab initio shielding calculations: emerging tools for protein structure determination. Chem. Soc. Rev. 2010, 39, 578−590. (43) Baxter, N. J.; Williamson, M. P. Temperature dependence of 1H chemical shifts in proteins. J. Biomol. NMR 1997, 9, 359−369. (44) Wang, Y.; Jardetzky, O. Investigation of the neighboring residue effects on protein chemical shifts. J. Am. Chem. Soc. 2002, 124, 14075− 14084. (45) Shen, Y.; Bax, A. Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J. Biomol. NMR 2007, 38, 289−302. (46) Wishart, D. S.; Bigam, C. G.; Holm, A.; Hodges, R. S.; Sykes, B. D. 1H, 13C and 15N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects. J. Biomol. NMR 1995, 5, 67−81. (47) Schwarzinger, S.; Kroon, G. J. A.; Foss, T. R.; Chung, J.; Wright, P. E.; Dyson, H. J. Sequence-dependent correction of random coil NMR chemical shifts. J. Am. Chem. Soc. 2001, 123, 2970−2978. (48) Wang, Y.; Jardetzky, O. Investigation of the neighboring residue effects on protein chemical shifts. J. Am. Chem. Soc. 2002, 124, 14075− 14084. (49) Tamiola, K.; Acar, B.; Mulder, F. A. A. Sequence-specific random coil chemical shifts of intrinsically disordered proteins. J. Am. Chem. Soc. 2010, 132, 18000−18003. (50) Tamiola, K.; Mulder, F. A. A. Using NMR chemical shifts to calculate the propensity for structural order and disorder in proteins. Biochem. Soc. Trans. 2012, 40, 1014−1020. (51) Shen, Y.; Bax, A. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J. Biomol. NMR 2010, 48, 13−22. (52) Lee, H.; Heo, L.; Lee, M. S.; Seok, C. GalaxyPepDock: a protein−peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res. 2015, 43, W431−W435. 6842


ACS Omega

Article

(53) Scheer, J. M.; Ryan, C. A. The systemin receptor SR160 from Lycopersicon peruvianum is a member of the LRR receptor kinase family. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 9585−9590. (54) Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Swails, J.; Goetz, A. W.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M. J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Salomon-Ferrer, R.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A. AMBER 12; University of California: San Francisco, 2012. (55) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926−935. (56) Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327−341. (57) Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N· log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089−10092. (58) Case, D. A.; Babin, V.; Berryman, J. T.; Betz, R. M.; Cai, Q.; Cerutti, D. S.; Cheatham, T. E., III; Darden, T. A.; Duke, R. E.; Gohlke, H.; Goetz, A. W.; Gusarov, S.; Homeyer, N.; Janowski, P.; Kaus, J.; Kolossváry, I.; Kovalenko, A.; Lee, T. S.; LeGrand, S.; Luchko, T.; Luo, R.; Madej, B.; Merz, K. M.; Paesani, F.; Roe, D. R.; Roitberg, A.; Sagui, C.; Salomon-Ferrer, R.; Seabra, G.; Simmerling, C. L.; Smith, W.; Swails, J.; Walker, R. C.; Wang, J.; Wolf, R. M.; Wu, X.; Kollman, P. A.; et al. AMBER 14; University of California: San Francisco, 2014. (59) The PyMOL Molecular Graphics System, Version 1.8; Schrödinger, LLC: 2015. (60) R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013. URL http://www.R-project.org/. (61) Grant, B. J.; Rodrigues, A. P. C.; ElSawy, K. M.; McCammon, J. A.; Caves, L. S. D. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 2006, 22, 2695−2696. (62) Cattell, R. B. The scree test for the number of factors. Multivariate Behav. Res. 1966, 1, 245−276. (63) Šali, A.; Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 1993, 234, 779−815. (64) Shen, M.-y.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006, 15, 2507−2524.

6843


Plant Polypeptide Hormone Systemin Prefers Polyproline II

Recommend Documents