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Art v 4 Protein Structure as a Representative Template for Allergen Profilins: Homology Modeling and Molecular Dynamics Haruna L. Barazorda-Ccahuana, Diego Ernesto Valencia, Jorge Alberto Aguilar-Pineda, ́ ez* and Badhin Gom Centro de Investigación en Ingeniería MolecularCIIM, Vicerrectorado de Investigación, Universidad Católica de Santa María, Urb. San José S/NUmacollo, Arequipa 04000, Peru
ACS Omega 2018.3:17254-17260. Downloaded from pubs.acs.org by 95.181.176.100 on 12/21/18. For personal use only.
S Supporting Information *
ABSTRACT: Profilins are panallergenic proteins of clinical relevance. We compared three groups of three-dimensional structures from six vegetable profilins. The first group was composed of crystal structures obtained by X-ray diffraction. The second and the third group structures were obtained by homology modeling; the selection criteria of the templates for these were based on the best resolution structure and the highest sequence identity, respectively. All of the structures underwent a 200 ns molecular dynamics simulation. The best template for the second group was Art v 4 and for the third group was the crystal structure corresponding to each profilin, except for Zea m 12. After molecular dynamics simulation, root-mean-square deviation, rootmean-square fluctuation, and radii of gyration values were similar in all groups, except for Amb a 8 (second group) and Zea m 12 (third group). All structures had acceptable conformation quality values, demonstrated in the Ramachandran plot and Z-score. The evaluation of epitopes did not have pertinent alterations of all of the structures. Art v 4 profilin was an acceptable template to model three-dimensional profilin structures of this work. The selection of templates based on their structural resolution and the measure of the quality is a good alternative to homology modeling of proteins of the same family.
1. INTRODUCTION Profilins are proteins with a molecular weight of 12−15 kDa and are involved in actin oligomerization. They are present in vegetables, animal cells, and even in some unicellular species.1 According to the official site for the systematic allergen nomenclature (WHO/IUIS) Allergen database (http://www. allergen.org/), currently 48 vegetable profilins are reported as proteins with allergenic potential.2 Allergenic profilins have been identified in pollen,3,4 plant foods,5,6 and latex.7 The first profilin reported as an allergenic protein was Bet v 2 from birch pollen.8 The high homology and common epitope conservation from vegetable profilins are the principal causes of crossreactivity reactions.9−11 In the protein structure database obtained by experimental methods of X-ray crystallography, nuclear magnetic resonance, and electron microscopy, we found a limited number of structures, unlike the information of their amino acid sequence, which grew exponentially.12,13 Three strategies have been developed for protein structure prediction: ab initio modeling, fold recognition, and homology modeling.14−16 Homology modeling is a method widely used in biomedical and pharmacological research, demonstrating high accuracy of experimental data.17−20 This method predicts the 3D protein structure on the basis of the sequence alignment with one or more template proteins of known structure. However, in © 2018 American Chemical Society
addition to the 3D prediction of proteins, the refinement is necessary through molecular dynamics (MD) simulations, which attempt to demonstrate the proximity of atomic positions to their natural structure,21−23 showing the difference before and after the refinement.24 In this work, we observed that the most important element in homology modeling is usually the template. Our study focused on finding a template to model six vegetable profilins on the basis of two criteria; the first criterion was considering the best X-ray structural resolution and the second criterion was based on the high sequence identity percentage. Finally, MD simulations were applied to validate the best model of six vegetable profilins predicted by homology modeling.
2. RESULTS AND DISCUSSION The essential criteria for choosing a template are the X-ray resolution measures and the percentage of sequence identity. We found 15 crystal structures reported as vegetable profilins in the PDB database. The details about the 3D crystal structures of profilins are presented in Table 1. We see that PDB ID: 5EM0 and PDB ID: 4ESP had the same resolutions Received: September 5, 2018 Accepted: December 3, 2018 Published: December 13, 2018 17254
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parameters are given in Table 3. The RMSD analysis indicated that the Crys system remained within the 0.2−0.3 nm range, an acceptable value for modeled proteins.25 SM and Mdllr systems presented RMSD values similar to that of the Crys, except for two structures: Zea m 12 obtained by SM, Zea m 12 (SM) (0.34 ± 0.07 nm), and Amb a 8 obtained by Mdllr, Amb a 8 (Mdllr) (0.44 ± 0.07 nm). The average per-residue RMSF was between 0.1 and 0.15 nm, wherein Amb a 8 (Mdllr) had the highest fluctuations. The RG of all proteins reflected the conservation of their globular shape from 1.39 ± 0.01 to 1.49 ± 0.01 nm. In Figures S2−S4 of the Supporting Information, we report all of the RMSD graphs, RMSF, and RG. Taking into account the final results, we noticed that the problems for Zea m 12 (SM) were due to the template selected by Swiss-Model, in that the template presented a low resolution value (3.1 Å) and sequence identity percentage (79.1%). Zea m 12 (SM) is stabilized at 150 ns with an average RMSD value of 0.34 ± 0.07 nm and RMSF per residue of 0.13 ± 0.08 nm, which differs from that of its homologs obtained by other routes. Zea m 12 (Mdllr) reached convergence at 20 ns with an RMSD average value of 0.28 ± 0.03 nm and the largest fluctuations in residues PHE66, GLN99, ARG121, and ALA130 with an average RMSF of 0.12 ± 0.06 nm. Likewise, Zea m 12 (Crys) at 110 ns reaches stability with an average RMSD 0.25 ± 0.03 nm and RMSF for each residue in the equilibrium molecular dynamics simulations was 0.12 ± 0.04. We note something interesting in the turning radius, which shows that the models (Crys) and (Mdllr) had similar forms of average 1.41 ± 0.01 and 1.43 ± 0.02 nm, respectively. Data are shown in Table 3. Another important protein was Amb a 8, modeled with the best template (Art v 4). Amb a 8 (Mdllr) presented five βsheets in the middle of its backbone, in which C and N termini predominated the α helix and three big loops (Figure 1a). Figure 1b shows us the RMSD plot of the systems, in which we noticed that the most unstable model was Amb a 8 (Mdllr). The high fluctuation occurred in the PHE40, GLU57, and GLU79 residues (Figure 1c) located in the second loop (l2) and third β-strand (β3) by PHE40. In general, the average RMSF of the last 50 ns from Amb a 8 (Mdllr) was 0.15 ± 0.07 nm, whereas the fluctuations in Crys and SM systems were similar (average RMSF values of 0.10 ± 0.04 and 0.11 ± 0.04 nm, respectively), with the high fluctuation over the PHE43 located in l2. The radii of gyration of Crys, Mdllr, and SM systems (Figure 1d) reflected the conservation of their globular
Table 1. Quality Data of X-ray Resolution That Has Been Collected for the Crystal Structure PDB ID
resolution (Å)
5EM1 5EV0 5EVE 1A0K 3NUL 4ESP 5NZC 5NZB 1CQA 1G5U 5FDS 5FEG 5FEF 5EM0 6B6J
1.5 2.1 2.6 2.2 1.6 1.1 2.0 1.7 2.4 3.1 1.9 2.1 2.2 1.1 1.9
R-value R-value (work) 0.165 0.234 0.233 0.238 0.204 0.183 0.232 0.224 0.313 0.197 0.228 0.245 0.19 0.214
1.142 0.193 0.201 0.172 0.181 0.168 0.206 0.195 0.178 0.259 0.173 0.19 0.218 0.165 0.167
side chain outliers (%) 0 0.5 0.9 5.8 1 1.9 0 1.9 12.4 20.9 1 0.6 1.9 0 0
and similar R-values that measure the quality of the atomic model obtained by X-ray, except for the data of side chain outliers percentage, PDB ID: 5EM0 (0%) and PDB ID: 4ESP (1.9%). Considering the best resolution of the crystal structures, the best model was PDB ID: 5EM0, which is Art v 4 profilin from Artemisia vulgaris. Table 2 summarizes the percentage of sequence identity using Art v 4 as a template, with the lowest and highest values for Ara h 5 (68.7%) and Amb a 8 (89.4%), respectively. The system using Art v 4 as a template was denominated Mdllr. SM was the second system proposed; in that case, we took into account the most significant identity sequences without considering their X-ray resolution. We used Swiss-Model, and we found that five of six profilins obtained their 3D structures on the basis of their crystal structures; nevertheless, Zea m 12 used a different structure, having a 79.1% sequence identity. Additionally, we analyzed a third system composed of six crystal structures obtained from the PDB database, denominated Crys (Figure S1 of the Supporting Information). 2.1. Root-Mean-Square Deviation (RMSD), RootMean-Square Fluctuation (RMSF), and Radii of Gyration (RG) Analysis. The RMSD graph during 200 ns of MD simulations; RMSF and RG during the last 50 ns of MD; RMSDs‑imp, the Ramachandran plot, and Z-scores of the last frame of the 200 ns MD simulations; and the complete Table 2. Features of Modeled Profilins by Homology Modeling profilin
NCBI access code
Amb a 8
5EVE_A
Ara b 5
NP_179566.1
Ara h 5
4ESP_A
Bet v 2
5NZC_A
Hev b 8
1G5U_A
Zea m 12
A4KA61.1
software
template
AAa
homology (%)
M odeller Swiss-Model Modeller Swiss-Model Modeller Swiss-Model Modeller Swiss-Model Modeller Swiss-Model Modeller Swiss-Model
Art v 4 5em1.1.A Art v 4 1a0k.1.A Art v 4 4esp.1.A Art v 4 5nzb.1.A Art v 4 1g5u.1.A Art v 4 1g5u.1.A
132
89.4 100.0 72.5 100.0 68.7 100.0 77.3 100.0 73.3 100.0 73.8 79.1
131 130 132 131 130
a
Number of amino acids. 17255
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Table 3. RMSD, RMSF, RG, RMSD Superimposed, Ramachandran Plot, and Z-Score Values Ramachandran plot regions model
RMSD (nm)
RMSF (nm)
RG (nm)
Crys Mdllr SM
0.24 ± 0.02 0.44 ± 0.07 0.23 ± 0.03
0.10 ± 0.04 0.15 ± 0.07 0.11 ± 0.04
1.40 ± 0.01 1.49 ± 0.01 1.39 ± 0.01
Crys Mdllr SM
0.23 ± 0.02 0.25 ± 0.03 0.23 ± 0.02
0.11 ± 0.02 0.11 ± 0.04 0.10 ± 0.03
1.40 ± 0.01 1.41 ± 0.01 1.43 ± 0.01
Crys Mdllr SM
0.28 ± 0.04 0.26 ± 0.02 0.22 ± 0.03
0.12 ± 0.05 0.12 ± 0.04 0.10 ± 0.04
1.45 ± 0.01 1.42 ± 0.01 1.40 ± 0.01
Crys Mdllr SM
0.27 ± 0.02 0.23 ± 0.02 0.27 ± 0.04
0.10 ± 0.04 0.11 ± 0.04 0.12 ± 0.05
1.44 ± 0.01 1.41 ± 0.01 1.43 ± 0.01
Crys Mdllr SM
0.24 ± 0.03 0.24 ± 0.02 0.23 ± 0.03
0.11 ± 0.04 0.10 ± 0.03 0.12 ± 0.04
1.40 ± 0.01 1.41 ± 0.01 1.40 ± 0.01
Crys Mdllr SM
0.25 ± 0.03 0.28 ± 0.03 0.34 ± 0.07
0.12 ± 0.04 0.12 ± 0.06 0.13 ± 0.08
1.41± 0.01 1.41 ± 0.01 1.43 ± 0.02
RMSDs‑impa (nm)
favored
allowed
outlier
Z-score
117 (90.7%) 113 (87.6%) 109 (84.5%)
12 (9.3%) 16 (12.4%) 17 (13.2%)
0 (0.0%) 0 (0.0%) 3 (2.3%)
−5.84 −3.50 −6.09
113 (88.3%) 108 (84.4%) 115 (90.6%)
14 (10.9%) 18 (14.1%) 11 (8.7%)
1 (0.8%) 2 (1.6%) 1 (0.8%)
−6.02 −6.16 −6.96
112 (88.2%) 117 (91.4%) 115 (90.6%)
14 (11.0%) 11 (8.6%) 12 (9.4%)
1 (0.8%) 0 (0.0%) 0 (0.0%)
−4.45 −6.33 −5.53
112 (86.8%) 121 (93.8%) 113 (87.6%)
15 (11.6%) 6 (4.7%) 14 (10.9%)
2 (1.6%) 2 (1.6%) 2 (1.6%)
−6.29 −6.15 −5.74
109 (85.8%) 118 (92.2%) 112 (88.2%)
16 (12.6%) 10 (7.8%) 15 (11.8%)
2 (1.6%) 0 (0.0%) 0 (0.0%)
−5.80 −7.29 −7.21
118 (91.5%) 110 (86.6%) 113 (90.4%)
10 (7.8%) 16 (12.6%) 10 (8.0%)
1 (0.8%) 1 (0.8%) 2 (1.6%)
−5.88 −5.28 −5.18
Amb a 8 0.215 0.005 Ara b 5 0.013 0.007 Ara h 5 0.061 0.085 Bet v 2 0.049 0.014 Hev b 8 0.054 0.048 Zea m 12 0.015 0.133
a
Superimposition of three-dimensional structures with Crys as a reference.
shape with 1.40 ± 0.01, 1.49 ± 0.01, and 1.39 ± 0.01 nm, respectively. 2.2. Structural Validation. Twelve structures were superimposed using Crys models to give RMSD values (RMSDs‑imp). We found that the highest value of RMSD was found for Amb a 8 (Mdllr) and Zea m 12 (SM) (0.215 and 0.133 nm, respectively), whereas the other models had an RMSD value lower than 0.09 nm. The Ramachandran plot was used to find the stable conformations of the profilins, taking into account that in protein structures, the occurrence of (ϕ, ψ) angles in the disallowed region of the Ramachandran plot almost always suggests local regions of reduced accuracy.26 The results indicated that a relatively low percentage of residues have ϕ and ψ angles in the disallowed ranges; the Ramachandran plot was acceptable in all structures. On the other hand, the Z-score parameters described the overall model quality from −3.50 to −7.29, which was the range observed for the native set of proteins of the same size; Amb a 8 (Mdllr) had the less value. There are some differences in the secondary structure after molecular dynamic simulation. These structures were evaluated as shown in Figure 2. The secondary structure analysis showed us the percentage of 22.9 to 31% and 13.1 to 24.2% from the β-strand and α helix respectively. The analysis of the epitopes indicated that profilins conserved from six to nine epitopes at the end of MD simulation (Figure 3).
the best structural resolution within a list of 15 crystal structures reported in the PDB database. 5EM0 was used as a template in system Mdllr. The second template in system SM modeled five of six proposed structures with their crystal structures. Profilin Zea m 12 was modeled on the basis of a different crystal with a sequence identity percentage of 79.07 %. It can be considered a deficiency of the server because this profilin has a crystal structure (PDB ID: 5FEF). From the analysis of RMSD and RMSF in all systems, Amb a 8 in the Mdllr system as well as Zea m 12 in the SM system had the worst value. However, despite the results of the RMSD, RMSF, and RG, the Ramachandran plot and Z-score showed us that the Mdllr system behaved similarly to the other systems. In this work, we demonstrate that Art v 4 could be used as a template in the homology modeling of vegetable profilins.
4. COMPUTATIONAL DETAILS We studied six profilin sequences from National Center of Biotechnology Information (NCBI) by the following access codes: 5EVE_A (Amb a 8, Ambrosia artemisiifolia), NP_179566.1 (Ara b 5, Arabidopsis thaliana), 4ESP_A (Ara h 5, Arachis hypogaea), 5NZC_A (Bet v 2, Betula pendula), 1G5U_A (Hev b 8, Hevea brasiliensis), and A4KA61.1 (Zea m 12, Zea mays). We prepared two systems for obtaining tertiary structure on the basis of homology modeling. The first system considered a template that possesses the highest percentage of sequence identity, using the Swiss-Model server (https:// swissmodel.expasy.org).27−29 The second system took into account the best resolution of X-ray crystallography of vegetable profilins reported in Protein Data Bank (PDB) (https://www.rcsb.org),30 using the Modeller Software.31−34 Molecular dynamics (MD) simulations of three systems named as Crys (obtained from PDB), SM (Swiss-Model), and
3. CONCLUSIONS We analyzed six vegetable profilins by homology modeling and their refinement by MD simulations. For homology modeling, we have used two types of templates; the first was considering the best structural resolution and the second was the percentage of sequence identity with a random selection in the Swiss-Model server. PDB ID: 5EM0 of profilin Art v 4 had 17256
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Figure 1. Analysis of Amb a 8 profilin using Crys (green), Mdllr (magenta), and SM (blue). (a) Three-dimensional structure: α (α-helix), β (βsheet), t (turn), and l (loop). (b) RMSD. (c) RMSF. (d) RG.
Figure 2. Secondary structure analysis of six profilins. 17257
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Figure 3. Amino acid sequence and linear epitope comparison of profilins. (a) Amb a 8. (b) Ara b 5. (c) Ara h 5. (d) Bet v 2. (e) Hev b 8. (f) Zea m 12.
ÄÅ ÅÅ Å 1 RMSD(t1 , t 2) = ÅÅÅÅ N ÅÅ ∑ mi ÅÇ i = 1
Mdllr (Modeller) were carried out. The MD simulations were carried out in GROMACS 2016 software,35−38 and all-atom optimized potentials for liquid simulations39 force field, which contains intermolecular and intramolecular parameters of the systems. The proteins were located in the middle of a cubic box (6.1 nm per side), with a simple point charge40 water model (≃5000 water molecules), and ions (chlorine or sodium) were added to neutralize the system. Two MD simulations were analyzed: the first was energy minimization of the systems, using the steepest descent algorithm for 10 ns, which uses the first derivative of the potential energy for all atomic positions to find the minimum. The second simulation considered was md run for 200 ns to allow the stabilization of the system. The movement equations were solved using the Leap-Frog41 method considering 1 fs time step and periodic boundary conditions in all directions. All hydrogen bonds were constrained by a linear constraint solver (LINCS) algorithm.42 Electrostatic long-range effects were treated with particle mesh Ewald43−45 method with a tolerance of 1 × 10−5 for the contribution in real space. The van der Waals interactions were calculated using Lennard− Jones potential with a cutoff of 0.9 nm. A Nosé−Hoover thermostat was employed at 300 K. The MD was analyzed and validated by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radii of gyration (RG), Ramachandran plot, and Z-score. RMSD measures a structural change of profilins over time t for a reference structure (t2 = 0) and to obtain average distances, and can be expressed as eq 1(N: number of atoms in the protein; mi and ri: mass and position of the atom i at time t, respectively).
2É ÑÑ1/2
N
∑ mi i=1
ri(t1) − ri(t 2)
ÑÑ ÑÑ ÑÑ ÑÑ ÑÖ
(1)
RMSF allowed the measurement of fluctuation in a protein residue. RMSF measures the deviation between the position of the particle i and a reference in the usual way (eq 2), where T is the time over which the fluctuation is to be measured and rref i is the reference position of the particle i, which usually is the average position throughout the simulation. ÄÅ É1/2 2Ñ ÅÅ T ÑÑ ÅÅ 1 ÑÑ RMSFi = ÅÅÅÅ ∑ ri(t j) − r iref ÑÑÑÑ ÅÅ T t = 1 ÑÑ ÅÅÇ j ÑÑÖ (2) RG examined the root-mean-square distance of the protein atoms from their center of mass, emphasizing the global shape of the tertiary protein structure. Gromacs calculates this property using the mass mi and atom position ri by following eq 3. ij ∑ ri 2 mi yz zz R g = jjjj i z j ∑i mi zz k {
1/2
(3)
The Ramachandran plot was widely used to analyze the backbone conformation of protein structures, displaying (ϕ, ψ) angle pairs of the polypeptide chain in an easily comprehensible way, verifying that the main chain torsion angles (ϕ, ψ) are stereochemically feasible.46 These diagrams were calculated using the RAMPAGE online server.47 Additionally, the Z17258
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score48 was calculated to compare the structural quality of the models using the ProsA server.49,50 To elucidate the secondary structures of the proteins, we used the PDBSUM server, where ProMotif computed the structural characteristics.51 The ElliPro server52 based on the three-dimensional structure of a protein antigen was used to analyze the antigenic determinants (epitopes), which correspond to a portion of the macromolecule recognized by the immune system. All visualizations were done in UCSF CHIMERA software.53
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profilin, is a cross-reactive allergen of latex, plant foods and pollen. Int. Arch. Allergy Immunol. 2001, 125, 216−227. (8) Valenta, R.; Duchene, M.; Pettenburger, K.; Sillaber, C.; Valent, P.; Bettelheim, P.; Breitenbach, M.; Rumpold, H.; Kraft, D.; Scheiner, O. Identification of profilin as a novel pollen allergen; IgE autoreactivity in sensitized individuals. Science 1991, 253, 557−560. (9) van Ree, R.; Voitenko, V.; van Leeuwen, A.; Aalberse, R. C. Profilin is a cross-reactive allergen in pollen and vegetable foods. Int. Arch. Allergy Immunol. 1992, 98, 97−104. (10) Valenta, R.; Duchêne, M.; Vrtala, S.; Valent, P.; Sillaber, C.; Ferreira, F.; Tejkl, M.; Hirschwehr, R.; Ebner, C.; Kraft, D.; Scheiner, O. Profilin, a novel plant pan-allergen. Int. Arch. Allergy Immunol. 1992, 99, 271−273. (11) Uhlén, M.; Fagerberg, L.; Hallström, B. M.; Lindskog, C.; Oksvold, P.; Mardinoglu, A.; Sivertsson, Å.; Kampf, C.; Sjöstedt, E.; Asplund, A. Tissue-based map of the human proteome. Science 2015, 347, 1260419. (12) Apweiler, R.; Bairoch, A.; Wu, C. H.; Barker, W. C.; Boeckmann, B.; Ferro, S.; Gasteiger, E.; Huang, H.; Lopez, R.; Magrane, M.; et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2004, 32, D115−D119. (13) Burley, S. K.; Berman, H. M.; Kleywegt, G. J.; Markley, J. L.; Nakamura, H.; Velankar, S. Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive; Springer, 2017; pp 627−641. (14) Karplus, K.; Karchin, R.; Draper, J.; Casper, J.; MandelGutfreund, Y.; Diekhans, M.; Hughey, R. Combining local-structure, fold-recognition, and new fold methods for protein structure prediction. Proteins: Struct., Funct., Bioinf. 2003, 53, 491−496. (15) Lee, J.; Freddolino, P. L.; Zhang, Y. From Protein Structure to Function with Bioinformatics; Springer, 2017; pp 3−35. (16) Chakraborty, H. J.; Gangopadhyay, A.; Ganguli, S.; Datta, A. Applying Big Data Analytics in Bioinformatics and Medicine; IGI Global, 2018; pp 48−79. (17) Szilagyi, A.; Zhang, Y. Template-based structure modeling of protein-protein interactions. Curr. Opin. Struct. Biol. 2014, 24, 10−23. (18) Hasan, M.; Hakim, A.; Iqbal, A.; Bhuiyan, F. R.; Begum, M. K.; Sharmin, S.; Abir, R. A. Computational study and homology modeling of phenol hydroxylase: key enzyme for phenol degradation. Int. J. Comput. Bioinfo. In Silico Model. 2015, 4, 691−698. (19) Ramatenki, V.; Potlapally, S. R.; Dumpati, R. K.; Vadija, R.; Vuruputuri, U. Homology modeling and virtual screening of ubiquitin conjugation enzyme E2A for designing a novel selective antagonist against cancer. J. Recept. Signal Transduction 2015, 35, 536−549. (20) Samasil, K.; de Carvalho, L. L.; Mäenpäa,̈ P.; Salminen, T. A.; Incharoensakdi, A. Biochemical characterization and homology modeling of polyamine oxidase from cyanobacterium Synechocystis sp. PCC 6803. Plant Physiol. Biochem. 2017, 119, 159−169. (21) Xun, S.; Jiang, F.; Wu, Y.-D. Significant refinement of protein structure models using a residue-specific force field. J. Chem. Theory Comput. 2015, 11, 1949−1956. (22) Karplus, M.; Kuriyan, J. Molecular dynamics and protein function. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 6679−6685. (23) Anandakrishnan, R.; Drozdetski, A.; Walker, R. C.; Onufriev, A. V. Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations. Biophys. J. 2015, 108, 1153− 1164. (24) Feig, M.; Mirjalili, V. Protein structure refinement via molecular-dynamics simulations: What works and what does not? Proteins: Struct., Funct., Bioinf. 2016, 84, 282−292. (25) Kufareva, I.; Abagyan, R. Homology Modeling; Springer, 2011; pp 231−257. (26) Lakshmi, B.; Ramakrishnan, C.; Archunan, G.; Sowdhamini, R.; Srinivasan, N. Investigations of Ramachandran disallowed conformations in protein domain families. Int. J. Biol. Macromol. 2014, 63, 119− 125. (27) Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Cassarino, T. G.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: modelling protein tertiary and
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.8b02288. Crystal structures of six vegetable profilins (Figure S1); RMSD plot of five vegetable profilins (Figure S2); RMSF per residue plot of five vegetable profilins (Figure S3); RG plot of five vegetable profilins (Figure S4) (PDF)
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Phone: +51 982895967. ORCID
Haruna L. Barazorda-Ccahuana: 0000-0001-8791-0506 Diego Ernesto Valencia: 0000-0002-5533-2753 Badhin Gómez: 0000-0001-6539-1207 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Consejo Nacional de Ciencia, Tecnologı ́a e Innovación Tecnológica from Peruvian government through 138-2015 FONDECYT grant.
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REFERENCES
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DOI: 10.1021/acsomega.8b02288 ACS Omega 2018, 3, 17254−17260
