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In-silico analysis predicting the best model for photosystemIID2 Protein of Spinaciaolearacea using multiple templates | Swain | Computational Molecular Biology

In-silico analysis predicting the best model for photosystemIID2 Protein of Spinaciaolearacea using multiple templates  

Pranati Swain
Orissa university of agriculture and technology, India
Author    Correspondence author
Computational Molecular Biology, 2014, Vol. 4, No. 13   doi: 10.5376/cmb.2014.04.0013
Received: 04 Dec., 2014    Accepted: 26 Dec., 2014    Published: 30 Dec., 2014
© 2014 BioPublisher Publishing Platform
This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:

Swain, 2014, In-silico analysis predicting the best model for photosystemIID2 Protein of Spinaciaolearacea using multiple templates, Computational Molecular Biology, Vol.4, No.13, 1-6 (doi: 10.5376/cmb.2014.04.0013)

Abstract

Spinach is a natural medicine against diabetes, prostate cancer, asthma, constipation, high blood pressure. Spinach acts as anti-inflammatory, antiproliferative, antioxidative. In this study the PHOTOSYSTEMII D2 protein has considered for in-silico analysis. Models of the protein were generated using 1IZLD, 3A0B, 3WU2, 4IL6 templates. The sequence retrieved from uniprot, templates were predicted by usingblastP tool, physico-chemical analysis showed the properties of protein using prot-param tool , secondary structure prediction showed helices, turns and sheets using CFFSP server, homologous models were generated using modeller9.12 tool, backbone confirmation was performed by using Rampage server and finally the best model generated by using 3A0B template having 94.0% of residues lying in favored region, 2.0% residues lying in outlier region, with 91% of query coverage and 95% of identity with photosystemQ (B) protein of Thermosynechocuccus vulcanus.

Keywords
PHOTOSYSTEMII D2 protein; Template prediction; Homology modeling; Model validation; Best model prediction

The common name of Spinaciaolearacea is spinach which belongs to the family Amaranthaceae- Chenopodiaceae andplays an important role as a source of energy. Most commonly this green leavesare used as food . from a research it has been proved that the spinach is full of vitamin C, which helps to protect all of the oxygen-sensitive phytonutrients in the spinach leaves for which the leaves look vibrant and alive. The main health-supportive nutrients found in spinach isglycerolipids. Naturally spinach is anti-inflammatory (Lomnitski et al., 2000), antiproliferative (Bergman et al., 2011), antioxidative (Sani et al., 2004). From a research among broccoli, spinach, cauliflower, cabbage, mustard greens, collard and kale the spinach showed significant protection against the occurrence of aggressive prostate cancer in male. The spinach is blessed with a natural anti-cancer carotenoid i.e, epoxyxanthophylls. It contains carotenoids i.e, beta carotene, lutein, zeaxanthin along with antioxidants i.e, flavonoid. Spinach is quite healthy as it is composed of vit.K, vit.A, vit.B1, vit.B3, vitB2, vit.E, vit.B6, iron, copper, folate, manganese, calcium, fiber, potassium, zinc, protein, choline, omega-3 fats, selenium, pantothenic acid etc. the vit.K1 and vit.K2 helps in activating the osteocalcin leading to bone-up. However spinach is full of oxalate too which is dangerous to health if taken in a huge amount. Spinach contains natural purine which causes kidney stone and gout disease if taken in a huge a amount. Basically spinach helps to fight against weak bone, high blood pressure, diabetes, asthma, prostate cancer in male, constipation,human pancreatic cancer cells (Lomnitski et al., 2000). Spinach is also helpful for energy metabolism, maintaining muscle and nerve function, heart rhythm, a healthy immune system and maintaining blood pressure. In this study we have considered the Photosystem II D2 of spinach. This protein is a plastoquinone oxidoreductase that uses light energy to abstract electrons from H2O producing oxygen and proton gradient in order to produce ATP. Photosystem II D2 is a membrane protein. PSII is composed of 1 copy each of membrane proteins PsbA, PsbB, PsbC, PsbD, PsbE, PsbF, PsbH, PsbI, PsbJ, PsbK, PsbL, PsbM, PsbT, PsbX, PsbY, PsbZ, Ycf12. In the study the physico-chemical analysis of protein has been done along with homology modeling, model validation and optimization leading in prediction of the best model using mu; tiple templates.

1 Material
and Methods
1.1 Sequence retrieval
The amino acid sequence of photosytemII D2 protein was retrieved from uniprot in fasta format. The detail information is given in Table 1.


Table 1 Information about PHOTOSYSTEMII D2 protein

 
1
.2 Physico-chemical characterization of photosytemII D2 protein
The physico-chemical properties of the protein were studied by using protparam tool. From this analysis the theoretical PI, molecular weight, aliphatic index, extinction coefficient, number of amino acids, total number of positively and negatively charged residues, atomic position, chemical formula, instability index and GRAVY (Grand average of hydropathicity) of the protein. The detail information is given in Table 2 and Table 3.


Table 2 Physico-chemical properties ofPHOTOSYSTEMII D2 protein



Table 3 Amino acid composition result


1.3
Prediction of templates
The similarity search is generally done by using BLAST tool and the protein-protein similarity search is carried out by using the blastP tool. So the retrived amino acid sequence was subjected to blastP against PDB. From the result the suitable templates were found for further study. The selected templates for model building are given in Table 4.


Table 4 List of four templates used for homology modelling


1.4
Secondary structure prediction of protein
The secondary structure of protein was predicted by using CFFSP server from where the percentage of helix, sheets and turns were found (Figure 1). The detail information is given in Table 5.


Table 5 composition ofhelices, sheets, turns



Figure 1 Secondary structure ofPHOTOSYSTEMII D2 protein


1.5 Homology modelling
The 3D structures of the photosystemII D2 protein was generated by using homology modelling concept, in which four different templates were selected for model building.The models were generated by using modeller 9.12 tool (Bilal et al., 2013, Singh et al., 2009). The align2d.py, model-single.py and evaluate-model.py files were rum on the python script by setting the target, template and number of models to be generated. Here 5 models for each template were generated and the best model was selected on the basis of lowest DOPE score. The properties which were found after structure visualisation is given in Table 6 and Table 7. the tertiary atructures of protein is given in Figure 2-Figure 5.


Figure 2 model generated using 1IZLD



Table 6 Result obtained from yasara tool



Table 7 Result obtained frompymol tool



Figure 3 model generated using 3A0B



Figure 4 model generated using3WU2



Figure 5 model generated using4IL6


1.6 Model validation
The final models were further subjected to Rampage server for the analysis of backbone confirmation of protein. The backbone confirmation for each models generated which showed the number of residues lying in allowed region, favoured region and in outlier regions. Depending upon these characters the best model is selected. The ANOLEA server was used to find out Z-score and Q-mean score. Least Z-score indicates the best model. The validated models information and backbone confirmation is given in Table 8 and Table 9.
the backbone confirmtion of protein models are given in Figure 6-Figure 9.


Table 8 Result obtained from ANOLEA-SWISS SERVER



Table 9 Backbone confirmation of models



Figure 6 model with 1IZL template



Figure 7 model with 3A0B template



Figure 8 model with 3WU2 template



Figure 9 model with 4IL6 template


1.7 Selection of best model
However the best model is selected on the basis of identity, query coverage, Z-score, Qmean score, E-value etc.
2 Result
2.1 Sequence retrieval result
The sequence of PHOTOSYSTEMII D2 protein was retrivred from uniprot with uniprotID of P06005. The function, location, catalytic activity is given below.
2.2Physico-chemical analysis result
From this analysis the amino acid composition, theoretical PI, number of positively and negatively charged residues, GRAVY, aliphatic index of the protein is found.
2.3 Selected templates
Four primer 1IZL, 4IL6, 3A0B, 3WU2 were selected after blastP run as the templates for the protein with following characters. Here more templates are selected in order to find out best model for protein with a suitable template.
2.4 Secondary structure prediction result
The secondary structure of protein which generated from CFFSP server showed the following result.
2.5 Homology modelling result
The finally generated models were visualised using PyMol visualiser. The helices were denoted with sky blue colurs and the loops were denoted with purple colours respectively. The atom count, formal charge sum, molecular surface area, solvent accessible surface area of the models were generated from PyMol and beta factor, stability of the models, VDW radius, minimized enegy were generated from Yasara tool.
2.6 Model validation analysis result
The finally generated models were submitted to Rampage server to find out the best protein. The best protein was predicted on the basis of residues lying in strong favoured region.
3 Discussions
From the above analysis the best model found for photosystemII D2 protein with the template 3A0B having 94.0% of residues lying in favoured region, 2.0% residues lying in outlier region, with 91% of query coverage and 95% of identity with photosystemQ (B) protein of Thermosynechocuccus vulcanus.
References
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Bansal et al., 2014, Computational characterization of antifreeze proteins of Typhula ishikariensis , Gray Snow Mould , JPPR9012014d
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Bergman et al., 2011, The anti oxidant activity of aqueous spinach extract: chemical identification of active fractions. Phytochemistry journal, vol.58, No.1, 143-152
http://libra.msra.cn/Publication/40412632/the-antioxidant-activity-of-aqueous-spinach-extract-chemical-identification-of-active-fractions
Bilal et al., 2013, Generation of a 3D model for human cereblon using comparative modeling, Journal of Bioinformatics and Sequence Analysis Vol. 5, No.1,10-15
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Lomnitski et al., 2000, Effects of apocynin and natural antioxidant from spinach on inducible nitricoxide synthase and cyclooxygenase-2 induction in lipo polysaccharide-inducedhepatic injury in rat. Pharmacology & toxicology journal, vol.87, No.1, 18-25b
http://www.curehunter.com/public/pubmed10987211.do
Panda et al., 2014, .In silico predictive studies of mAHR congener binding using homology modelling and molecular docking, Toxicol Ind Health. Vol.30, No.8, 765-766
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Sani et al., 2004, Potential anticancer effect of red spinach (Amaranthusgangeticus) extract , Asia Pac J Clin Nutr journal, vol.13, No.4, 396-400
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Singh et al., 2009, Comparative modeling and analysis of 3-D structure of Hsp 70, in Cancer irroratus An International Journal, vol.1, No.2, 1-4
http://researchtrend.net/bf12/1_Sharda.pdf

 

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