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Comparative Study of Cellular Tumor Antigen p53 Protein of Fishes and Analysis of its Protein Interaction Network using Computational Approach | Kumari | Computational Molecular Biology

Comparative Study of Cellular Tumor Antigen p53 Protein of Fishes and Analysis of its Protein Interaction Network using Computational Approach  

Suchitra Kumari , Kiran D. Rasal , Jitendra K. Sundaray , Samiran Nandi , P. Jayasankar
Fish Genetics and Biotechnology Division,ICAR- Central Institute of Freshwater Aquaculture, Bhubaneswar, Orissa, 751002, India
Author    Correspondence author
Computational Molecular Biology, 2015, Vol. 5, No. 3   doi: 10.5376/cmb.2015.05.0003
Received: 10 Mar., 2015    Accepted: 15 May, 2015    Published: 21 May, 2015
© 2015 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:

Kumari et al., 2015, Comparative Study of Cellular Tumor Antigen p53 Protein of Fishes and Analysis of its Protein Interaction Network using Computational Approach, Computational Molecular Biology, Vol.5, No.3, 1-9 (doi: 10.5376/cmb.2015.05.0003)

Abstract

Progress in the field of Bioinformatics has been facilitated to understand the global network of genes and their protein products. In present study, comparative analysis of Cellular tumor antigen p53 proteins of nine fishes were carried out using Bioinformatics tools. Cellular tumor antigen p53 acts as a tumor-suppressor and having role in apoptosis, genomic stability. The results of this study indicate that, most of physico-chemical properties were almost same in Q92143 (Xiphophorus maculates) and O57538 (Xiphophorus helleri). In order to understand global network of Cellular tumor antigen p53, we have used STRING 9.1 tool and speculated that this protein interacting with several other protein but functional node -CHEK1, BCL2, MDM4 were common in Danio rerio, Oryzias latipes, Tetraodon miurus with high confidence score. The strong association interaction has seen between mdm2 and p53 with a good high score in Danio rerio. We also studied the molecular docking between Cellular tumor antigen p53 and Mdm2 of Zebrafish. Also, we have investigated conserved region present in all nine different protein sequences which specifies, that region maintained by evolution despite speciation. The present study will further support to understand the roles and associated proteins in various cellular pathways in fish.This work is also useful for the study of structural and functional analysis of p53 protein.

Keywords
Cellular tumor antigen p53; Sequence analysis; Protein interaction network; Conserved region; Bioinformatics; Physico-chemical properties; Mdm2

Although, Cellular tumor antigen p53has been discovered about thirty years ago, but remains concern most of the consecration in the fields of cancer research (Kruse and Gu, 2009; Lu et al., 2009). The Cellular tumor antigen p53 orp53 is a protein encoded by the TP53 gene which is most important a tumor suppressor gene.This gene has been well studied in humans and mammals except in some fishes. It isalso called tumor suppressor p53 or phosphor-protein p53 or antigen NY-CO-13 or p53 or Transformation-related protein 53 (TRP53) which play an important role in apoptosis i.e. programmed cell death in tumor development and genomic stability (Kruse and Gu, 2009; Storer and Zon, 2010).
The tumor suppressor p53 protein acts as a transcription factor to control expression of many genes in its interaction network, which consists of upstream regulators and downstream target genes (Fields and Jang, 1990). With its ability to respond to stress, p53 combats tumorigenesis and protects the individual at both a cellular and organismal level. P53 is a site-specific DNA-binding protein (Kern et al., 1991), that transactivates genes in its network (Fields and Jang, 1990; Lu et al., 2007). Hence, if p53 is mutated, cell growth ensues resulting in tumor formation. The activity and expression of p53 are monitored by numerous layers of regulation, mainly by ubiquitin ligases such as Mdm2 and Mdm4 at the post-translational level (Le et al., 2009). Mdm2 protein binds to p53 and inactivates it. The Mdm2 is an E3 ubiquitin ligase which up-regulated in the occurrence of active p53, where it poly-ubiquitinates tumor suppressor p53 for proteasome targeting (Oren, 1999). This is reported that mdm2 deficient zebrafish embryos show growth retardation and high levels of apoptosis (Storer and Zon, 2010) due to off-target effects of the mdm2 morpholino (Robu et al., 2007). In case of mammals, the stability and function of p53 is regulated by a number of post-translation modifications whereas in Zebrafish, regulation at both the mRNA and protein a level in response to different types of stress has been described (Brooks and Gu, 2003; Langheinrich et al., 2002; Storer and Zon, 2010). The mutation in p53 gene will inactivate its tumor suppression mechanism and also other factors, which will lead to tumor. The single amino substitution will also affect the expression of p53 (Petitjean et al., 2007). The loss of function of p53 due to mutations has been well studied in mouse model (Leng et al., 2003; Olive et al., 2004). Thus, its function regulated by post-translational regulation along with interacting p53 binding protein such as mdm2 and E3 ubiquitin ligase of p53.
In present study, we have used bioinformatics tools for comparative analysis of reported cellular tumor antigen p53protein sequences of nine different fish. We have explore the mechanism of tumor suppressor p53 gene and its protein sequence among fishes, because several works has been done on p53 in humans and mammals, but in case of fishes very little work has been reported. In recent era of Bioinformatics, several tools and algorithms has been developed for understanding biological molecules up to atomic level and predicting underlying mechanism. Further understanding of tumor suppressor p53 regulation in fishes using cellular mechanism along with protein modifications will facilitate to understand in vivo basic mechanisms that regulate the tissue specific response of p53.
1 Material and Methods
We have used different bioinformatics tools for studying the tumor suppressor p53 protein which listed along with specified purpose.
1.1 Collection of data
The UniProt is easily accessible database of protein sequence (http://www.uniprot.org/). We have retrieved total nine protein sequences from nine different fishes for our study; all reviewed (Table 1). We have retrieved protein sequences in a FASTA format.


Table 1 List of Cellular tumor antigen p53


1.2 Physico-chemical Characterization
ProtParam (http://web.expasy.org/protoparam/) is expasy tool which is useful for computation of physical and chemical parameters of given protein based on sequence. We have calculated several physico-chemical properties such as theoretical isoelectric point (pI), molecular weight, and total number of positive and negative residues, extinction coefficient, half-life, instability index, aliphatic index and grand average hydrophathy (GRAVY) of all nine retrieved protein sequences using ProtParam tool.
1.3 Alignment and Phylogenetic study
In order to study the comparison among different protein sequences, we have used global multiple sequence alignment (MSA) program for analysis of p53 protein sequences from different fishes. Now a day, multiple sequence alignment (MSA) method is widely used for assessing sequence conservation and conservation of protein domains in protein study. In this step, Clustal Omega (Sievers et al., 2011) tool was used for MSA analysis. Understanding phylogenetic relationship among different protein sequences, we have delineated evolutionary relationship of these sequences by cladogram. The Prosite, ScanProsite (de Castro et al., 2006) tool was used to identify the no. of hit for the predicted motif (http://prosite.expasy.org/ scanprosite/).
1.4 Analysis of Gene ontology and protein-protein interaction network of p53
We further studied the gene ontology of p53 for biological, molecular functions which identified using Uniprot (http://www.uniprot.org/). The STRING (Franceschini et al., 2013) (Search Tool for the Retrieval of Interacting Proteins) was used for studying the protein-protein interaction network of the p53 (http://string-db.org/).
1.5 Three dimensional structure analysis and molecular docking
The homology modeling was used to build p53 3D model based on homologous structure model. The structural templates that have highest sequence homology with our target template were identified by using PSI-BLAST (NCBI, http://blast.ncbi.nlm.nih. gov/Blast) against 3D structure available in PDB databank. The criteria used such as percent sequence identity, e-value, chain length and query coverage. The model was built by SWISS-Model using target-templates alignment. The SAVES (Structural Analysis and Verification Server) is integrated server was used for verification of models (http://nihserver. mbi.ucla.edu/SAVES/).The molecular docking was performed between p53 and Mdm2 using PatchDock followed by refine the structures using FireDock (http://bioinfo3d.cs.tau.ac.il/PatchDock/). The Patchdock based on surface patch matching and more reliable docking tools with fast search for filtering and scoring. It uses advanced data structures and spatial search pattern. It resulted into several structures, thus further filtered through FireDock. Docking score and atomic contact energy (ACE) of the both complexes were calculated using Patch Dock.
2 Results and Discussion

For comparative analysis of the Tumor suppressor antigen p53 from different fishes, we have used computational algorithms. The p53 is well studied in mammalian system along with some model fishes. Value of most of physico-chemical properties were almost same in Q92143 (Xiphophorus maculates) and O57538 (Xiphophorus helleri) like length, theoretical pI, positive R group, negative R group and aliphatic index, etc (Table 2). The value of an instability index of all protein was above 40 which indicate that all nine proteins were unstable. The Extinction coefficients(EC) value of p53 was calculated, which help in the protein- ligand and protein-protein interaction study. Q9W679 and Q9W678were greater than 7 which indicate that both proteins are basic in nature, rest of proteins were acidic in character.All the p53 protein sequences of nine different fishes were hydrophobic in nature. 


Table 2 Physico-chemical properties of protein sequences


Multiple Sequence Alignment (MSA) can give insight into sequence conservation across several species and thus allow identification of those sections of the sequence most critical to protein function (Jankun-Kelly et al., 2009). Further, performing MSA, we have seen that “MCNSSCMGGMNRR” is the conserved region (identical region) in all nine different protein sequences of p53 which indicates that this peptide sequence may have been maintained by evolution despite speciation (Figure 1). Our study of p53 phylogenetic analysis revealed that Q92143 (Xiphophorus maculates) O57538 (Xiphophorus helleri) were much closer to each other.Using Scan Prosite, we have perceived total 42 numbers of hits of “MCNSSCMGGMNRR” motif (Table 3) (Figure 2).


Table 3 No. of hits predicted by using “MCNSSCMGGMNRR”


Figure 1 The snapsort of MSA result. Here, "*" indicates identical in all sequences in the alignment; ":" indicates conserved substitutions; "." indicates semiconserved. Red color indicates the motif; dark grey color indicates the similarity; light blue color indicates metal binding; purple color indicates mutagenesis. Selected conserved region is highlighted by yellow box


Figure 2 Phylogenetic tree show the Evolutionary relationships of retrieved protein sequences by cladogram


Protein-protein interaction investigation is a wide-ranging approach to know the organization of desire proteome. The functional network protein study will be helpful for drug discovery, to understand metabolic pathways and to predict or develop genotype-phenotype associations (Wang et al., 2009; Wang and Moult, 2001). In order to understand network of p53 protein, we performed analysis using STRING 9.1 and revealed that functional node -CHEK1, BCL2, MDM4 were common in Danio rerio, Oryzias latipes, Tetraodon miurus. The interaction of mdm2 and p53 indicates the good high score in Danio rerio. Protein-protein interaction networks are major part for the system-level understanding of cellular processes. We have studied the all nine protein sequence one by one for getting protein-protein interaction network. Here our interest to know that which functional node is common of p53 network in different fishes. We have revealed protein-protein interactions network only from three different fish of p53 i.e. Danio rerio, Oryzias latipes, Tetraodon miurus (Table 4, 5 and 6) and functional node -CHEK1, BCL2, MDM2 were common in p53 protein network along with high confidence score. In STRING,
the functional interaction was analyzed by using confidence score. Interactions with score < 0.3 are considered as low confidence, scores ranging from 0.3 to 0.7 are classified as medium confidence and scores > 0.7 yield high confidence(Franceschini et al., 2013). In Danio rerio, p53 protein network showing functional association with 10 proteins and they are Cdkn1a, Mdm2, atm, Chek1, bcl2, Mdm4, Wu:fa96e12, Chek2, LOC792573, Ep300a (Figure 3). In the interaction network, there is no black line between mdm2 & p53 which indicates that there was no co-expression. We have speculated the occurrence of result to check that all 10 proteins were conserved in Danio rerio, Oryzias latipes, Tetraodon miurus or not and also found, that all nodes indicated 100% sequence conservation. 4, represents the occurrence result of zebra fish.Mdm4 and p53 having the good high score in Danio rerio, Oryzias latipes and Tetraodon miurus which indicates the strong association. Its function is to inhibit p53 and p73 mediated cell cycle arrest and apoptosis by binding its transcriptional activation domain. We have demarcated the best top ten protein-protein interaction network of p53 (Lu et al., 2009; Oren, 1999; Wang et al., 2004).


Figure 3 Protein interaction network. a) Evidence view of p53 protein network showing functional association with 10 proteins (Zebra fish). Here, a node represents proteins; an edge represents the predicted functional associations. Different line colors represent the types of evidence for the association. Red line indicates the presence of fusion evidence; yellow line text miming evidence; Light blue line indicates database evidence; Black line indicates the co-expression evidence. b.) Confidence view of p53 network (Zebra fish). In this fig stronger associations are represented by thicker lines. c.) Evidence view of p53 protein network showing functional association with 10 proteins (Oryzias latipes) d.) Confidence view of p53 network (Teraodan miurus)


Table 4 Interaction of p53 (zebra fish) with functional nodes


Table 5 Interaction of p53 (Oryzias latipes) with functional nodes


Table 6 Interaction of p53 (Teraodan miurus) with functional nodes


Mdm2 protein has shown the strong association with p53 protein in Zebra fish using STRING tool. So, to study the interaction at the structure level we have done docking. Firstly, the 3D structure of p53 of zebrafish obtained from Swiss-Model by performing homology modeling using retrived homologous strucures such as PDB ID; 3Q05_A, 3Q01_A, 3Q06_A, 4MZR_A with identity 58, 57, 58, 57 percent respectively. The obtained 3D structure was verirified with SAVES server (Figure 4). The molecular docking was performed between p53 and Mdm2 using PatchDock followed by refine the structures using FireDock. Docking score and atomic contact energy (ACE) of the both complexes were calculated using Patch DockBoth PDB structures were used for docking analysis (Figure 4). The docking between p53 and Mdm2 revealed that requires global energy 10.57 and ACE 0.18 respectively.


Figure 4 p53 model of zebrafish with conserved region in green color obtained through homology modeling. Ramachndran plot of model p53 showing, residues in most favoured regions 91.9%, Residues in additional allowed regions 5.9%, Residues in generously allowed regions 1.7% and Residues in disallowed regions 0.4% etc


3 Conclusion
This is first comprehensive study on comparative study of tumor suppressor antigen p53 among fishes. In this study, we have investigated that most of physico-chemical properties were almost same in Q92143 (Xiphophorus maculates) and O57538 (Xiphophorus helleri). After performing alignment we have seen that “MCNSSCMGGMNRR” is the conserved (identical) motif which present in all nine protein sequences of p53 and predicted overall 42 hit from the database which indicated the importance of this region. From protein-protein interactions network study, we have seen the functional node -CHEK1, BCL2, MDM4 were common in p53 protein network along with high confidence score in species Danio rerio, Oryzias latipes, Tetraodon miurus.Moreover, protein-protein interaction pathway of this tumor suppressor p53 has helped us to understand the roles and associated proteins in various cellular pathways.The docking study also confirmed that p53 having interaction with mdm2 with global energy.Thus finally, present work will support to understand more about tp53 proteins of different species including fish (Figure 5).


Figure 5 Snapshot of occurrence result. Here black color indicates the 100% sequence conservation in Zebra fish


Authors' contributions
SK and KDR designed the study and procedure of for work plan. SK, KDR, PJ, JKS and SN analyzed the data and prepared the manuscript. All authors read and approved the final manuscript
Acknowledgment
We are thankful to Indian Council of Agriculture Research, New Delhi
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