alexa In silico Analysis and Homology Modelling of Antioxidant Proteins of Spinach | Open Access Journals
ISSN: 0974-276X
Journal of Proteomics & Bioinformatics
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

In silico Analysis and Homology Modelling of Antioxidant Proteins of Spinach

Archna Sahay1* and Madhvi Shakya2

1Department of Bioinformatics, MANIT, Bhopal-462051 M.P., India

2Department of Mathematics, MANIT, Bhopal-462051, M.P., India

*Corresponding Author:
Archna Sahay, Ph.D
Department of Bioinformatics, MANIT
Bhopal-462051 M.P., India
Tel: +91-0755-2670327-1622
Fax: +91-0755-2670562
E-mail: [email protected], [email protected]

Received Date: March 23, 2010; Accepted Date:May 03, 2010; Published Date: May 03, 2010

Citation: Sahay A, Shakya M (2010) In silico Analysis and Homology Modelling of Antioxidant Proteins of Spinach. J Proteomics Bioinform 3: 148-154. doi: 10.4172/jpb.1000134

Copyright: © 2010 Sahay A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Proteomics & Bioinformatics


Spinach is an important dietary vegetable often associated with bene fi cial health effects. In this paper, a bioinformatics and molecular modeling approach was adopted to explore properties and structure of spinach antioxidant proteins. The antioxidant proteins selected for this study are ascorbate peroxidase (APX), dehydro ascorbate reductase (DHAR), phospholipid hydroperoxide glutathione peroxidase-like protein (PHGPX) and 2-Cys peroxiredoxin BAS1 (2-CPs). Physico-chemical characterization interprets properties such as pI, EC, AI, GRAVY and instability index and provides data about these proteins and their properties. Prediction of motifs, patterns, disul fi de bridges and secondary structure were performed for functional characterization. Three dimensional structures for these proteins were not available as yet at PDB. Therefore, homology models for these antioxidant proteins were developed. The modelling of the three dimensional structure of these proteins shows that models generated by Modeller were more acceptable in comparison to that by Geno3D and Swiss Model. The models were validated using protein structure checking tools PROCHECK and WHAT IF. These structures will provide a good foundation for functional analysis of experimentally derived crystal structures.


Spinach; Computational tools; Isoelectric point; Disulphide bridge; Homology model


Spinach (Spinacia oleracea) is a flowering plant in the family of Amaranthaceae. Though Spinach is most often commonly used as a food, it has medicinal value as well. Dietary supplementation with blueberries, spinach, or spirulina reduces ischemic brain damage (Wang et al., 2005). Natural antioxidant mixture (NAO), a watersoluble extract obtained from spinach leaves has been shown to have anti-inflammatory (Lomnitski et al., 2000), antiproliferative (Nyska et al., 2003) and antioxidative properties (Bergman et al., 2001) in biological systems. A detailed analysis of the antioxidant protein sequences, their probable structures and mode of action has yet to be accomplished.

Reactive oxygen species (ROS) such as superoxide radicals (O2-), hydroxyl radical (OH-), H2O2, and hydroperoxides (ROOH) are generated by exogenous sources, including prooxidant allelochemicals. Stress/starvation is an important endogenous source that generates ROS (Ahmad and Pardini, 1990). Antioxidants and antioxidant enzymes interrupt the cascades of uncontrolled oxidation in some organelles (Shigeoka et al., 2002). The antioxidant defense is primarily constituted by the actions of glutathione peroxidase (GPX), superoxide dismutase, catalase and ascorbate peroxidase (Barbehenn, 2002). In this study the antioxidant proteins of spinach have been selected for which three dimensional structures were not available at the protein data bank (PDB). These proteins are ascorbate peroxidase (APX), dehydro ascorbate reductase (DHAR), phopholipid hydroperoxide glutathione peroxidase-like protein (PHGPX) and 2-Cys peroxiredoxin BAS1 (2-CPs).

APX exist as isoenzyme and plays an important role in the metabolism of H2O2 in higher plants. APX isoenzymes are distributed in four distinct cellular compartments: stromal APX (sAPX), thylakoid membrane-bound APX (tAPX) in chloroplast, microbody (including glyoxisome and peroxisome) membrane bound APX (mAPX), and cytosolic APX (cAPX) (Ishikawa et al., 1998). DHAR is responsible for regenerating ascorbic acid from an oxidized state. It regulates the cellular ascorbic acid redox state, which in turn affects cell responsiveness and tolerance to environmental ROS. Ascorbic acid is a major antioxidant that serves many functions in plants including involvement in the detoxification of ROS, which are produced during aerobic metabolic processes such as photosynthesis or respiration (Asada and Takahashi, 1987). Ascorbic acid also participates in the regeneration of α-tocopherol (vitamin E) from the tocopheroxyl radical (Asada, 1994).

2-Cysteine peroxiredoxins (2-CPs) constitute a ubiquitous group of peroxidases that reduce cell-toxic alkyl hydroperoxides to their corresponding alcohols (Baier and Dietz, 1999). Despite the presence of elaborate enzymatic and nonenzymatic antioxidative defense mechanisms, ROS escape from detoxification and oxidize organic compounds such as proteins, nucleic acids, terpenoids and fatty acids to the respective peroxides (Baier and Dietz, 1998). In addition, alkyl hydroperoxides are formed by enzymatic reactions in chloroplasts, e.g. lipoxygenase catalyzes peroxidation of fatty acids and other desaturated organic biomolecules, such as carotenoids (Canfield et al., 1992). Detoxification of alkyl hydroperoxides is important because they can act as long-distance mediators of oxidative damage by oxidizing other biomolecules and initiating radical chain reactions (Elstner, 1990). Another plant protein, PHGPX has been identified in several plant species, including tomato (Herbette et al., 2002). PHGPX forms reduce phospholipid and cholesterol hydroperoxides and thereby play an important role in protecting biological membranesagainst oxygen toxicity. The function of this protein in the removal of phospholipid hydroperoxides, which are generated as products of lipoxygenase catalyzed oxygenation of fatty acids has been reported (Ursini et al., 1985).

Computational tools provide researchers to understand physicochemical and structural properties of proteins. A large number of computational tools are available from different sources for making predictions regarding the identification and structure prediction of proteins. The major drawbacks of experimental methods that have been used to characterize the proteins of various organisms are the time frame involved, high cost and the fact that these methods are not amenable to high throughput techniques. In silico approaches provide a viable solution to these problems. The amino acid sequence provides most of the information required for determining and characterizing the molecule’s function, physical and chemical properties. Computationally based characterization of the features of the proteins found or predicted in completely sequenced proteomes is an important task in the search for knowledge of protein function. In this paper the in silico analysis and homology modelling studies on antioxidant proteins of spinach was reported. Three dimensional structures for these proteins were yet not available. Hence to describe its structural features and to understand molecular function, the model structures for these proteins were constructed.

Materials and Methods

Sequences of antioxidant proteins of spinach were retrieved from the SWISSPROT, a public domain protein database (Bairoch and Apweiler, 2000). Table 1 shows the protein sequences considered in this study. The antioxidant proteins sequences were retrieved in FASTA format and used for further analysis.

Antioxidant Proteins Accession No. Length Description
APX Q42459 250 Ascorbate peroxidase (Cytosolic ascorbate  peroxidase)
Q7DN63 365 Stromal ascorbate peroxidase
Q41371 309 Ascorbate peroxidase
O46921 415 Thylakoid-bound ascorbate peroxidase
Q7DN73 415 Thylakoid-bound ascorbate peroxidase
Q7GDV4 365 Stromal ascorbate peroxidase
DHAR Q9FVE4 266 Dehydro ascorbate reductase
PHGPX O23814 171 phopholipid hydroperoxide glutathione peroxidase-like protein
2-CPs O24364 265 2-Cys peroxiredoxin BAS1, chloroplastic

Table 1:Protein sequences considered for the study.

Physico-chemical characterization

For physico-chemical characterization, theoretical isoelectric point (pI), molecular weight, total number of positive and negative residues, extinction coefficient (Gill and Von Hippel, 1989), instability index (Guruprasad et al., 1990), aliphatic index (Ikai, 1980) and grand average hydropathy (GRAVY) (Kyte and Doolottle, 1982) were computed using the Expasy’s ProtParam server (Gasteiger, 2005) ( The results were shown in Table 2.

Antioxidant Proteins Accession number Sequence length M.wt pI -R +R EC II AI GRAVY
APX Q42459 250 21555 5.41 37 28 21680 39.23 76.56 -0.394
Q7DN63 365 39516.3 8.46 43 46 46995 44.89 67.26 -0.518
Q41371 309 34471.8 6.25 40 37 16515 40.19 81.78 -0.442
O46921 415 45015.7 8.62 47 51 51465 46.7 71.37 -0.447
Q7DN73 415 44987.7 8.61 47 51 51465 45.57 71.37 -0.446
Q7GDV4 365 39544.3 8.46 43 46 46995 46.18 67.26 -0.519
DHAR Q9FVE4 266 29901.0 8.28 30 32 33982.5 39.57 78.76 -0.439
PHGPX O23814 171 19047.6 5.92 23 22 15992.5 21.90 79.77 -0.384
2-CPs O24364 265 28895.8 7.69 28 29 20002.5 47.14 84.98 -0.095

Table 2: Parameters computed using Expasy’s ProtParam tool.

Functional characterization

The SOSUI server performed the identification of transmembrane regions. Table 3 represents the transmembrane region identified for these antioxidant proteins. Disulphide bonds are important in determining the functional linkages. Table 4 shows prediction of “SS” bonds using the primary structure (protein sequence data) by the toolCYS_REC ( CYS_REC identifies the position of cysteins, total number of cysteins present and pattern, if present, of pairs in the protein sequence. Prosite is a database of protein families and domains (Falquet et al., 2002). Table 5 represents the output of Prosite that was recorded in terms of the length of amino residues of protein with specific profiles and patterns.

Antioxidant Proteins Accession No. Transmembrane region Length Type of protein

Table 3: Transmembrane regions identifi ed by SOSUI server.

Antioxidant Proteins Accession No. CYS_REC
APX Q42459 -
Q7DN63 Cys70-Cys194
Q41371 Cys82-Cys272 Cys252-Cys304
O46921 Cys70-Cys194
Q7DN73 Cys70-Cys194
Q7GDV4 Cys70-Cys194
DHAR Q9FVE4 Cys61-Cys75
PHGPX O23814 -
2-CPs O24364 -

Table 4: Disulphide (SS) bond pattern of pairs predicted, by CYS_REC.

Antioxidant Proteins Accession No. Motif Found Profile Position in the protein Description
APX Q42459 PEROXIDASE_2 PEROXIDASE_1 PEROXIDASE_4 33-250 33 - 44 155 - 165 Heme-binding peroxidases carry out a variety of biosynthetic and degradative functions using hydrogen peroxide as the electron acceptor. They play a key role in hydrogen peroxide removal in the chloroplasts and cytosol of higher plants.
Q7DN63 PEROXIDASE_1 PEROXIDASE_4 98 - 349 225 - 235
Q41371 PEROXIDASE_2 PEROXIDASE_1 PEROXIDASE_4 15-256 34 - 45 147 - 157
O46921 PEROXIDASE_1 PEROXIDASE_4 98 - 349 225 - 235
Q7DN73 PEROXIDASE_1 PEROXIDASE_4 98 - 349 225 - 235
Q7GDV4 PEROXIDASE_1 PEROXIDASE_4 98 - 349 225 - 235
DHAR Q9FVE4 - GST_NTER GST_CTER 65 - 143 129 - 266 Glutathione S-transferases (GSTs) are involved in detoxification of xenobiotic compounds and in the biosynthesis of important metabolites. The N-terminal thioredoxin-like domain participate in binding the glutathione moiety via its thioredoxin-like domain while the C-terminal domain contains several hydrophobic α-helices that specifically bind hydrophobic substrates.
PHGPX O23814 GLUTATHIONE_PEROXID_1 GLUTATHIONE_PEROXID_2 GLUTATHIONE_PEROXID_3 2-171 32 – 47 69 - 76 Glutathione peroxidase is an enzyme that catalyzes the reduction of hydroxyperoxides by glutathione. Its main function is to protect against the damaging effect of endogenously formed hydroxyperoxides.
2-CPs O24364 - THIOREDOXIN_2 73 - 232 Thioredoxins participate in various redox reactions via the reversible oxidation of an active center disulfide bond.

Table 5:Functional characterization of proteins of spinach at Prosite.

Secondary structure prediction

SOPMA (Geourjon and Deleage, 1995) was employed for calculating the secondary structural features of the antioxidant protein sequences considered for this study. The results were presented in Table 6.

Antioxidant Proteins APX DHAR PHGPX 2-CPs
Secondary structure Q42459 Q7DN63 Q41371 O46921 Q7DN73 Q7GDV4 Q9FVE4 O23814 O24364
Alpha helix 38.8% 29.86% 36.89% 37.83% 38.31% 30.41% 36.09% 23.98% 26.42%
310 helix 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Pi helix 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Beta bridge 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Extended strand 10.80% 12.88% 12.94% 11.81% 12.05% 11.23% 19.92% 21.64% 19.25%
Beta turn 8.40% 6.30% 5.18% 5.54% 4.34% 6.85% 6.39% 11.70% 6.04%
Bend region 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Random coil 42.00% 50.96 44.98% 44.82% 45.30% 51.51% 37.59% 42.69% 48.30%
Ambiguous states 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Other states 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Table 6: Calculated secondary structure elements by SOPMA.

Model building and evaluation

The modeling of the three dimensional structure of the protein was performed by three homology modeling programs, Geno 3D (Combet et al., 2002), Swissmodel (Arnold et al., 2006) and Modeller (Sali and Blundelll, 1993). The constructed 3D models were energy minimized in GROMACS force field using steepest descent minimization Algorithms (Van der Spoel et al., 2005). The overall stereochemical property of the protein was assessed by Ramchandran plot analysis (Ramachandran et al., 1963). The validation for structure models obtained from the three software tools was performed by using PROCHECK (Laskowski et al., 1996). The models were further checked with WHAT IF (Vriend, 1990). The results of PROCHECK and WHAT IF analysis was shown in Table 7 and Table 8 respectively. Structural analysis was performed and figures representations were generated with Swiss PDB Viewer (Guex and Manuel, 1997).

Server Antioxidant Proteins APX DHAR PHGPX 2-CPs
Q42459 Q9FVE4 O23814
Geno3D Residues in the most Favored Region 84.4% 79.8% 83.5% 83.0%
Residues in additionally allowed region 13.7% 16.6% 16.5% 13.9%
Residues in generously allowed region 0.9% 1.8% 0.0% 1.2%
Residues in disallowed region 0.9% 1.8% 0.0% 1.8%
Swiss model Residues in the most Favored Region 92.8% 84.0% 81.0% 90.8%
Residues in additionally allowed region 7.2% 13.3% 17.6% 8.6%
Residues in generously allowed region 0.0% 1.7% 1.4% 0.0%
Residues in disallowed region 0.0% 1.1% 0.0% 0.6%
Modeller Residues in the most Favored Region 96.2% 80.0% 95.0% 91.5%
Residues in additionally allowed region 3.3% 18.2% 5.0% 7.9%
Residues in generously allowed region 0.5% 1.8% 0.0% 0.0%
Residues in disallowed region 0.0% 0.0% 0.0% 0.6%

Table 7:Ramachandran plot calculation and Comparative analysis of the models from Geno3D, Swiss-model and Modeller computed with the PROCHECK program

Antioxidant Protein Accession No. RMS Z-score for bond angles k
APX Q42459 1.084
DHAR Q9FVE4 1.123
PHGPX O23814 1.053
2-CPs O24364 1.208

Table 8: RMS Z-score for bond angles of modeled protein structure using WHAT IF.

Results and Discussion

Table 1 shows antioxidant proteins of spinach considered in thisstudy. These antioxidant protein sequences were retrieved from the SWISSPROT, a public domain protein database. These protein sequences were retrieved in FASTA format and used for further analysis. Parameters computed using Expasy’s ProtParam tool was represented in Table II. The calculated isoelectric point (pI) will be useful because at pI, solubility is least and mobility in an electro focusing system is zero. Isoelectric point (pI) is the pH at which the surface of protein is covered with charge but net charge of protein is zero. At pI proteins are stable and compact. The computed pI value of APX (Q42459, Q41371) and PHGPX were less than 7 (pI<7) indicates that these antioxidant proteins were considered as acidic. The pI of APX (Q7DN63, O46921, Q7DN73, Q7GDV4), DHAR and 2-CPs are greater than 7 (pI> 7) reveals that these proteins were basic in character. The computed isoelctric point (pI) will be useful for developing buffer system for purification by isoelectric focusing method. Although Expasy’s ProtParam computes the extinction coefficient for 276, 278, 279, 280 and 282 nm wavelengths, 280 nm is favored because proteins absorb light strongly there while other substances commonly in protein solutions do not. Extinction coefficient of AFPs at 280 nm is ranging from 15992.5 to 51465 M–1 cm–1 with respect to the concentration of Cys, Trp and Tyr. The high extinction coefficient of APX (Q7DN63, O46921, Q7DN73, and Q7GDV4) indicates presence of high concentration of Cys, Trp and Tyr. The computed extinction coefficients help in the quantitative study of protein–protein and protein–ligand interactions in solution. The instability index provides an estimate of the stability of protein in a test tube. There are certain dipeptides, the occurrence of which is significantly different in the unstable proteins compared with those in the stable ones. This method assigns a weight value of instability. Using these weight values it is possible to compute an instability index (II). A protein whose instability index is smaller than 40 is predicted as stable, a value above 40 predicts that the protein may be unstable (Guruprasad et al., 1990). The instability index value for the spinach antioxidant proteins were found to be ranging from 21.90 to 47.14. The result classified APX (Q42459), DHAR (Q9FVE4) and PHGPX (O23814) as stable protein (Table 2).

The aliphatic index (AI) which is defined as the relative volume of a protein occupied by aliphatic side chains (A, V, I and L) is regarded as a positive factor for the increase of thermal stability of globular proteins. Aliphatic index for the antioxidant protein sequences ranged from 67.26 – 84.98. The very high aliphatic index of all antioxidant protein sequences indicates that these antioxidant proteins may be stable for a wide temperature range. The lower thermal stability of APX (Q7DN63 and Q7GDV4) was indicative of a more flexible structure when compared to other antioxidant protein. The Grand Average hydropathy (GRAVY) value for a peptide or protein is calculated as the sum of hydropathy values of all the amino acids, divided by the number of residues in the sequence. GRAVY indices of APX are ranging from - 0.095 to -0.519. This low range of value indicates the possibility of better interaction with water.

Functional analysis of these proteins includes prediction of transmembrane region, disulfide bond and identification of important motifs. SOSUI distinguishes between membrane and soluble proteins from amino acid sequences, and predicts the transmembrane helices for the former. The Transmembrane regions and their length were tabulated in Table 3. The server SOSUI classifies APX (O46921 and Q7DN73) as membrane protein and other spinach antioxidant proteins as soluble proteins. SOSUI server has identified one transmembrane region in these proteins. The transmembrane regions are rich in hydrophobic amino acids. As disulphide bridges play an important role in determining the thermostability of these proteins. CYS_REC was used to determine the Cysteine residues and disulphide bonds. Possible pairing and pattern with probability were presented in Table 4. Result shows that except APX (Q42459), PHGPX (O23814) and 2-CPs (O24364) all proteins contain disulphide linkages.

The functions of antioxidant proteins of spinach were analyzed by submitting the amino acid sequence to Prosite server. Sequence of a particular cluster of residue types, which is variously known as a pattern, motif, signature or fingerprint. These motifs, typically around 10 to 20 amino acids in length, arise because specific residues and regions thought or proved to be important to the biological function of a group of proteins are conserved in both structure and sequence during evolution (Christian et al., 2002). Prosite analysis suggested the functionality of these proteins with profiles and patterns identified for characteristic functionality were represented in Table 5.

The secondary structure of spinach antioxidant proteins were predicted by SOPMA (Self Optimized Prediction Method with Alignment) which correctly predicts 69.5% of amino acids for a state description of the secondary structure prediction (Geourjon and Deléage, 1995). The secondary structure indicates whether a givenamino acid lies in a helix, strand or coil. Secondary structure features as predicted using SOPMA were represented in Table 6. The results revealed that random coils dominated among secondary structure elements followed by alpha helix, extended strand and beta turns for all sequences. The secondary structure were predicted by using default parameters (Window width: 17, similarity threshold: 8 and number of states: 4).

Three dimensional structures are predicted for proteins where such data is unavailable. There is lack of experimental structures for these proteins considered. Out of six ascorbate peroxidase isoenzymes sequences, three dimensional structure was modeled for only Q42459 since it has been reported that the steady-state transcript level of cytosolic APX altered in stress condition (Bergman et al., 2001). The other three proteins for which the three dimensional structures were modeled includes DHAR, PHGPX and 2-CPs. The modeling of the three dimensional structure of the protein was performed by three homology modeling programs, Geno 3D, Swiss Model and Modeller. The constructed three dimensional models were energy minimized in GROMACS force field using steepest descent minimization Algorithms. The φ and ψ distribution of the Ramachandran Map generated by of non glycine, non proline residues were summarized in Table 7. A comparison of the results obtained from the Geno 3D, Swiss Model and Modeller, three different software tools in Table 7 shows that the models generated by Modeller was more acceptable in comparison to that by Geno3D and Swiss Model. The final modeled structures were visualized by Swiss PDB Viewer that was shown in Figure 1.


Figure 1: Modeled Structure of spinach proteins (A) Q42459 (B) Q9FVE4 (C) O23814 (D) O24364.

The stereo chemical quality of the predicted models and accuracy of the protein model was evaluated after the refinement process using Ramachandran Map calculations computed with the PROCHECK program. The assessment of the predicted models generated by modeller was shown in Figure 2. The main chain parameters plotted are Ramachandran plot quality, peptide bond planarity, Bad nonbonded interactions, main chain hydrogen bond energy, C-alpha chirality and over-all G factor. In the Ramachandran plot analysis, the residues were classified according to its regions in the quadrangle. The red regions in the graph indicate the most allowed regions whereas the yellow regions represent allowed regions. Glycine is represented by triangles and other residues are represented by squares. The result revealed that the modeled structure for APX, DHAR, PHGPX and 2CPS has 96.2%, 80.0%, 95.0% and 91.5% residue respectively in allowed region. The distribution of the main chain bond lengths and bond angles were found to be within the limits for these proteins. Such figures assigned by Ramachandran plot represent a good quality of the predicted models.


Figure 2: Ramachandran’s Map of spinach proteins. (A) Q42459 (B) Q9FVE4 (C) O23814 (D) O24364.

The modeled structures of spinach antioxidant proteins were also validated by other structure verification servers WHAT IF. Standard bond angles of the four models are determined using WHAT IF. The results were shown in Table VIII. The analysis revealed RMS Z-scores were almost equal to 1 suggesting high model quality. The predicted structures conformed well to the stereochemistry indicating reasonably good quality.


In this study antioxidant protein of spinach were selected. Physicochemical characterization were performed by computing theoretical isoelectric point (pI), molecular weight, total number of positive and negative residues, extinction coefficient, instability index, aliphatic index and grand average hydropathy (GRAVY). Functional analysis of these proteins was performed by SOSUI server. For these proteins disulphide linkages, motifs and profiles were predicted. Secondary structure analysis revealed that random coils dominated among secondary structure elements followed by alpha helix, extended strand and beta turns for all sequences. The modelling of the three dimensional structure of the proteins were performed by three automated homologyprograms, Geno 3D, Swiss model and Modeller. The models were validated using protein structure checking toolsPROCHECK and WHAT IF. These structures will provide a good foundation for functional analysis of experimentally derived crystal structures.


Financial assistance from Council of Scientifi c and Industrial Research (CSIR), Govt. of India, for the Research Associate Fellowship (RA) is gratefully acknowledged. The authors are grateful to the Department of Bioinformatics and Department of Mathematics, MANIT, Bhopal for providing the necessary facilities and encouragement.


  1. Ahmad S, Pardini RS (1990) Mechanisms for regulation of oxygen toxicity in phytophagus insects. Free Radical Biol Med 8: 401-413.» CrossRef » PubMed » Google Scholar
  2. Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace:a web-based environment for protein structure homology modelling. Bioinformatics 22: 195-201.» CrossRef » PubMed » Google Scholar
  3. Asada K (1994) Mechanisms for scavenging reactive molecules generated in chloroplasts under light stress. In: NR Baker, JR Bowyer eds, Photoinhibition of Photosynthesis: From Molecular Mechanisms to the Field. Oxford: Bios Scientific Publishers 129-142.» Google Scholar
  4. Asada K, Takahashi M (1987) Production and scavenging of active oxygen in photosynthesis. In: DJ Kyle, CB Osmond, CJ Arntzen eds, Photoinhibition: Topics of Photosynthesis. Elsevier Amsterdam 9: 227-287.» CrossRef » Google Scholar
  5. Baier M, Dietz KJ (1999) Protective Function of Chloroplast 2-Cysteine Peroxiredoxin in Photosynthesis. Evidence from Transgenic Arabidopsis. Plant Physiol 119: 1407-1414.» CrossRef » PubMed» Google Scholar
  6. Baier M, Dietz KJ (1998) The costs and benefits of oxygen for photosynthesizing plant cells. Prog Bot 60: 282-314.» Google Scholar
  7. Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28: 45-48.» CrossRef » PubMed » Google Scholar
  8. Barbehenn RV (2002) Gut-based antioxidant enzymes in a polyphagous and graminivorous grosshopper. J Chem Ecol 28: 1329-1347.» CrossRef » PubMed » Google Scholar
  9. Bergman M, Varshavsky L, Gottlieb HE, Grossman S (2001) The antioxidant activity of aqueous spinach extract: chemical identifi cation of active fractions. Phytochemistry 58: 143-152.» CrossRef » PubMed » Google Scholar
  10. Canfield LM, Forage JW, Valenzuela JG (1992) Carotenoids as cellular antioxidants. Proc Soc Exp Biol Med 200: 260-265.» PubMed » Google Scholar
  11. Christian JAS, Cerutti L, Hulo N, Gattiker A, Falquet L, et al. (2002) PROSITE: A documented database using patterns and profi les as motif descriptors. Brief Bioinform 3: 265-274.» CrossRef » PubMed » Google Scholar
  12. Combet C, Jambon M, Deleage G, Geourjon C (2002) Geno3D: Automatic comparative molecular modelling of protein. Bioinformatics 18: 213-214.» CrossRef » PubMed » Google Scholar
  13. Elstner EF (1990) Der Sauerstoff. Bl-Wissenschafts-Verlag. Mannheim 418- 422.» Google Scholar
  14. Falquet L, Pagni M, Bucher P, Hulo N, Sigrist CJA, et al. (2002) The PROSITE database, its status in 2002. Nucl Acids Res 30: 235-238.» CrossRef » PubMed » Google Scholar
  15. Gasteiger E (2005) Protein Identifi cation and Analysis Tools on the ExPASy Server. In: John M. Walker ed, The Proteomics Protocols Handbook, Humana Press 571-607.» CrossRef »Google Scholar
  16. Geourjon C, Deléage G (1995) SOPMA: signifi cant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 11: 681-684.» CrossRef » PubMed » Google Scholar
  17. Gill SC, Von Hippel PH (1989) Calculation of protein extinction coefficients from amino acid sequence data. Anal Biochem 182: 319- 326.» CrossRef» PubMed » Google Scholar
  18. Guex N, Manuel CP (1997) Data Modeling, Analysis and Classifi cation SWISSMODEL and the Swiss-Pdb Viewer: An environment for comparative protein modeling. Electrophoresis 18: 2714-2723.» CrossRef » PubMed » Google Scholar
  19. Guruprasad K, Reddy BVP, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Prot Eng 4: 155-164.» CrossRef » PubMed » Google Scholar
  20. Herbette S, Lenne C, Leblanc N, Julien JL, Drevet JR, et al. (2002) Two GPX-like proteins from Lycopersicon esculentum and Helianthus annus are antioxidant enzymes with phospholipid hydroperoxide glutathione peroxidase and thioredoxin peroxidase activities. Eur J Biochem 269: 2414-2420.» CrossRef » PubMed » Google Scholar
  21. Ikai AJ (1980) Thermo stability and aliphatic index of globular proteins. J Biochem 88: 1895-1898.» CrossRef » PubMed » Google Scholar
  22. Ishikawa T, Yoshimura K, Sakai K, Tamoi M, Takeda T, et al. (1998) Molecular characterization and physiological role of a glyoxysome-bound ascorbate peroxidase from spinach. Plant Cell Physiol 39: 23-34.» CrossRef » PubMed » Google Scholar
  23. Kyte J, Doolottle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157: 105- 132.» CrossRef » PubMed » Google Scholar
  24. Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8: 477-486.» CrossRef » PubMed » Google Scholar
  25. Lomnitski L, Foley J, Ben-Shaul V, Grossman S, Maronpot RR, et al. (2000) Effects of apocynin and natural antioxidant from spinach on inducible nitric oxide synthase and cyclooxygenase-2 induction in lipopolysaccharide-induced hepatic injury in rat. Pharmacol Toxicol 87: 18-25.» CrossRef » PubMed » Google Scholar
  26. Nyska A, Suttie A, Bakshi S, Lomnitski L, Grossman S, et al. (2003) Slowing tumorigenic progression in TRAMP mice and prostatic carcinoma cell lines using natural antioxidant from spinach, NAO-a comparative study of three antioxidants. Toxicol Pathol 31: 39-51. » PubMed » Google Scholar
  27. Ramachandran GN, Ramakrishnan C, Sasisekhran V (1963) Stereochemistry of polypeptide chain confi guarations. J Mol Biol 7: 95-99.» PubMed » Google Scholar
  28. Sali A, Blundelll TL (1993) Comparative protein modeling by satisfaction of spatial restraints. J Mol Biol 234: 779-815.» Google Scholar
  29. Shigeoka S, Ishikawa T, Tamoi M, Miyagawa Y, Takeda T, et al. (2002) Regulation and function of ascorbate peroxidase isoenzymes. J Exp Bot 53: 1305-1319.» CrossRef » PubMed » Google Scholar
  30. Ursini F, Maiorino M, Gregolin C (1985) The selenoenzyme phospholipid hydroperoxide glutathione peroxidase. Biochim Biophys Acta 839: 62-70.» CrossRef » PubMed » Google Scholar
  31. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, et al. (2005) GROMACS: Fast, Flexible and Free. J Comp Chem 26: 1701-1718.» CrossRef » PubMed » Google Scholar
  32. Vriend G (1990) WHAT IF: A molecular modeling and drug design program. J Mol Graph 8: 52-56.» CrossRef » PubMed » Google Scholar
  33. Wang Y, Chang CF, Chou J, Chen HL, Deng X, et al. (2005) Dietary supplementation with blueberries, spinach, or spirulina reduces ischemic brain damage. Exp Neurol 193: 75-84.» CrossRef » PubMed » Google Scholar
  34. Yoshimura K, Yabuta Y, Ishikawa T, Shigeoka S (2000) Expression of spinach ascorbate peroxidase isoenzymes in response to oxidative stresses. Plant Physiol 123: 223-234.» CrossRef » PubMed » Google Scholar
Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • 9th International Conference on Bioinformatics
    October 23-24, 2017 Paris, France
  • 9th International Conference and Expo on Proteomics
    October 23-25, 2017 Paris, France

Article Usage

  • Total views: 12262
  • [From(publication date):
    May-2010 - Jul 23, 2017]
  • Breakdown by view type
  • HTML page views : 8359
  • PDF downloads :3903

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version