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Prediction and Comparative Analysis of MHC Binding Peptides and Epitopes in Nanoviridae Nano-organisms

Dangre DM1*, Deshmukh SR2, Rathod DP2, Umare VD3 and Ullah I4
1Department of Public Health and Geriatric Medicine, Maharashtra University of Health Sciences, Regional Centre, Aurangabad, India
2Department of Biotechnology, SGB Amravati University, Amravati, Maharashtra, India
3Department of Biotechnology, Centre for Advanced Life Sciences, Deogiri College, Aurangabad, India
4Department of Biology, Gyeongsang National University, Jinju, South Korea
Corresponding Author : Devanand M.
Dangre, Department of Public Health and Geriatric Medicine,
MUHS, Regional Centre, Shivaji (Amkhas) ground,
Civil Hospital Campus, Aurangabad – 431001,
Tel: 0240-2336181, +919372757601,
Received April 01, 2010; Accepted May 03, 2010; Published May 03, 2010
Citation: Dangre DM, Deshmukh SR, Rathod DP, Umare VD, Ullah I (2010) Prediction and Comparative Analysis of MHC Binding Peptides and Epitopes in Nanoviridae Nano-organisms. J Proteomics Bioinform 3:155-172. doi:10.4172/jpb.1000135
Copyright: © 2010 Dangre DM, 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.
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Nanoviridae is a family of single stranded DNA viruses which infect the plants through their phloem tissues. The few members of these viruses are now turning towards animals. It includes two genera, Nanovirus and Babuvirus. Nanovirus includes three species namely Faba beans necrotic yellows virus (FBNYV), Milk vetch dwarf virus (MVDV) and Subterranean clover stunt virus (SCSV) while Babuvirus have two species accounted yet, namely Abaca bunchy top virus (ABTV) and Banana bunchy top virus (BBTV). The viral coat proteins are likely to be responsible for many diseases in plants as well as animals. The intra-genomic changes or post translational modifications with in SCSV have decreased the length of the sequence, but increased the antigenic potential and numbers of antigenic binding regions on the surface of the protein. We have predicted the most probable responsible antigenic determinants and MHC binders within the viruses in Nanoviridae family. MHC molecules play a crucial role in host immune response. The nonamers of antigenic determinants in Nanoviridae family viral coat proteins are highly sensitive to H-2Kd and I-Ag7 molecules. The species within particular genera shows their common epitopes along with diverse colleague epitopes. These common antigenic peptides can be used as their identifi ers. These identifi ers along with their diverse colleagues could be most informative for antidotes production against themselves. Also, these are important for synthetic peptide vaccine production against the relevant viruses.

Nanoviridae family; Antigenicity; Epitopes; MHC binders; Antidotes 
Nanoviridae is a family of single stranded DNA viruses which infect the plants through their phloem tissues. They are characterized by non enveloped, spherical virions of 17-20 nm in diameter with icosahedral symmetry and are transmitted by aphids in a persistent but not in a propagative manner. These viruses retained when the vector moults, don’t multiply in a vector. The genome of Nanoviridae viruses consists of 6 to 8 circular (+) ssDNA about 1kb in size. Each ssDNA have a common stem-loop region and are encapsidated in a separate particle. In addition to genomic DNA, satellite-lake DNA are commonly found, usually encoding for Rep proteins. These satellite rep proteins are only able to initiate replication of their genomic DNA, unlike genomic rep which promotes replication of all 6-8 viral genomic ssDNAs. 
Recently, the many plant pathogenic viruses have been found in animal feces (Zhang et al., 2006; Li et al., 2010) which indicate the prevalence of these plant viruses in animals. Viruses with small (10 kb) circular DNA genomes, either single- or double-stranded, have been found to infect vertebrates as well as plants (Fauquet et al., 2005). Blinkova and his group performed the alignment of loop nonanucleotide sequences from ChiSCVs, circoviruses, nanoviruses and a geminivirus. They found conserved regions between ChiSCV and other circular ssDNA viruses. ChiSCV ORF 1 was 15–24% similar in amino acid sequence to the replicase of the multi-segmented plant viruses of the Nanoviridae family but only 12–17% similar to the replicases of circoviruses and geminiviruses. Thus, it can be assume that ChiSCV genomes share features with plant nanoviruses The replicase genes of these viruses were most closely related to those of the much smaller (~1 kb) plant nanovirus circular DNA chromosomes (Blinkova et al., 2010). This is the main reason to make a keen interest in finding whether these plant viruses can be harmful to human being in future or not. 
Nanoviridae family of viruses includes two genera viz. Nanovirus and Babuvirus. The genus Nanovirus which is closely related to the genus Babuvirus, accommodates Subterranean clover stunt virus (SCSV) as type species and other two species: Faba bean necrotic yellows virus (FBNYV) and Milk vetch dwarf virus (MVDV) whereas the genus Babuvirus accommodates Banana bunchy top virus (BBTV) as type species and one new species: Abaca bunchy top virus (ABTV). 
The three species of Nanovirus genus mainly infect a wide range of leguminous plants along with some other crops. The brief description of each species is given as follows: 
Faba beans necrotic yellows virus (FBNYV)
It is the causal agent of economically important disease of faba beans (Vicia faba), lentil (Lens esculanta) and pasture legumes in West Asia, North Africa, Sudan and Ethiopia. FBNYV infected crops show stunting, leaf rolling, yellowing and systemic reddening symptoms. The leaves become thick, brittle and have interveinal chlorotic blotches and nodulation and yield markedly reduced (Makkouk et al., 1998, Katul et al., 1995). FBNYV particles are efficiently and persistently transmitted by the green pea aphids and Acyrthosiphon pisum. 
Milk vetch dwarf virus (MVDV)
It was first reported in Astragalus sinicus from Japan by Ohki in 1975 (Ohki et al., 1975). It is a causal agent of Spinacia oleracea, Vigna unguiculata ssp. sesquipedalis, Astragalus sinicus, Vicia sativa, Phaseolus vulgaris, Pisum sativum, Vicia faba, Datura stramonium, Nicotiana rustica, etc. and it gets transmitted by vectors like Aphis craccivora, A. gossypii, Acyrthosiphon pisum, Acyrthosiphon (Aulocorthum) solani, etc. The MVDV infected plants show yellow dwarf, stunting and leaf rolling symptoms. 
Subterranean clover stunt virus (SCSV)
It was first reported in Trifolium subterraneum from New South Wales, Australia by Grylls and Butler in 1956 (Grylls and Butler, 1956). It is a causal agent of diseases of Trifolium subterraneum, T. cernuum, Medicago lupulina, M. hispida var. denticulata, M. minima, Trifolium repens, T. glomeratum, Wisteria sinensis, Phaseolus vulgaris, Pisum sativum, Vicia faba, Trifolium dubium, T. pratense, Medicago arabica, etc. and it gets transmitted by Aphis craccivora, A. gossypii, Myzcus persicae, Macrosiphum euphorbiae, etc. SCSV infected crops show marginal chlorosis and puckering or cupping of leaflets, epinasty of leaves and the whole plant being markedly stunted with a reduction in the length of internodes. 
The genus Babuvirus includes two species, Banana bunchy top virus and Abaca bunchy top virus, which differ from Nanoviruses slightly in number of DNA components, the host range and aphid vectors. These two species are described in brief as follows: 
Banana bunchy top virus (BBTV): The BBTV disease was first reported in Musa species from Fiji by Magee in 1953 (Magee, 1953). BBTV is considered to be the most economically destructive disease of banana. The disease is widespread in Asia including China and Japan, Africa and Oceania but not in Central and South America (Dale, 1987). It is transmitted in a persistent manner only by banana aphid (Pentalonia nigronevosa). BBTV infection results in narrow, bunched leaves and stunted, fruitless plants, which eventually die. BBTV is regarded as one of the 100 most important emerging pathogens afflicting humankind. 
Abaca bunchy top virus (ABTV)
ABTV disease was first recognised in 1910 in Albay Province, The Philippines (Ocfemia, 1926) and has long been regarded as the most important biological constraint to abaca production. The disease occurs in all major production areas of abaca plants (Raymundo and Bajet, 2000). The ABTV infected plants show stunting, bunched and rossetted leaves, dark green flecks or veins clearing of leaves, upcurling and chlorosis of leaf margins etc. The symptoms and transmitting vector of ABTV disease are reminiscent of those of BBTV disease which led to the conclusion that both diseases are caused by the same virus. However, conflicting evidence was presented suggesting that two separate viruses were involved. In The Philippines, ABTD was transmitted to abaca but not to banana by P. nigronervosa (Ocfemia, 1930). BBTD was not observed in banana even when growing adjacent to abaca plantations seriously affected with ABTD (Ocfemia and Buhay, 1934). Therefore, ABTV is included as a new member of the genus Babuvirus
Nanoviridae viruses possess small single-stranded DNA (ssDNA) genomes, as opposed to the double-stranded genomes of the mammalian tumor viruses such as simian virus 40 (SV40) or the papillomaviruses (Lageix et al., 2007) but still show striking similarities with them in the way they induce host cells to enter S phase or trigger progress beyond the G1/S checkpoint (Hanley- Bowdoin et al., 2004). In Nanoviruses, Rep (M-Rep) plays a master role in initiation of replication (Timchenko et al., 2006). This would support the future host of the modified nanoviruses could be animals or human. Nevertheless, the stability of these plant viruses in the human gastro-intestinal tract may allow them to be used as a platform for oral vaccine development (Yusibov et al., 2002). Nanoviruses proteins have found to be harmful for cell cycle-regulatory proteins in animals and plants. For its potential to link viral DNA replication with key regulatory pathways of the cell cycle, Aronson and his coworkers (Aronson et al., 2000) named the FBNYV C10 protein Clink, for “cell cycle link.” This may be a clue to be alert from these plant viruses which are altering themselves for a new habitat. 
MHC binding peptides
Innovations and discoveries of archetype in immunology are achieving impressive task in vaccine and drug design, anti-dotes and their development. Development of new MHC class-I and II binding peptides prediction tools are also supporting to this task. 
The major histocompatibility complex (MHC) molecules are cell surface glycoproteins, which plays an important role in the host immune system, autoimmunity and reproductive success. MHC class-I encodes heterodimeric peptide-binding proteins as well as antigen-processing molecules such as TAP and Tapasin. MHC class- II encodes heterodimeric peptide-binding proteins and proteins that modulate antigen loading onto MHC class-II proteins in the lysosomal compartment such as MHC-II DM, MHC-II DQ, MHC-II DR, and MHC-II DP. The MHC Class-I molecules present the parts of almost all antigens to the T-cells with certain specificity. The binding mechanism appears to be the most selective step in the recognition of T-cell epitopes. The molecular mechanisms underlying this selectivity are still debated (Rhodes and Trowsdale, 1999), but a crucial factor is the complementarity between amino acids in the antigen peptide and the MHC binding pocket (Yewdell and Bennink, 1999). Successfully modeling the behavior exhibited by MHCs can be used to pre-select candidate peptides 
MHC alleles are grouped according to their structures. For class I MHC alleles, the close binding groove at both ends in MHC Class-I makes it possible to predict exactly which residues are positioned in the binding groove. For Class II MHC molecules, the binding groove is open at both ends and peptides which bind class II alleles are generally longer than those which bind class I MHCs, typically 9 to 25 residues. Moreover, the grooves of MHC Class-II alleles will only accommodate 9 to 11 residues of the target peptide (Kropshofer et al., 1993) Thus class II peptides have the potential to bind to the MHC groove in one of several registers (potential alignments between groove and antigenic peptide). A peptide binds through a network of hydrogen bonds between its backbone and the binding groove, and through interactions between the peptide side chains and pockets inside the binding groove (Madden et al., 1993; Stern et al., 1994). Interaction, within the groove, between MHC and peptide side chains is generally considered the principal determinant of binding affinity (Brusic et al., 1998). However, for MHC Class-II type alleles, a recent study speculates that binding may not be completely deterministic, and that the same peptide can have multiple possible binding cores (Tong et al., 2006). The binders and their subsets are most important for vaccine designers. 
As viruses infect a cell by entering its cytoplasm, this cytosolic,MHC class-I dependent pathway of antigen presentation is the primary way for a virus-infected cell to signal immune cells in plants. Parameters such as hydrophilicity, flexibility, accessibility, turns, exposed surface, and antigenic propensity of polypeptides chains have been correlated with the location of continuous epitopes.
Determination and identification of epitopes, their efficiency and the MHC-I and II binders are technically skillful and time consuming tasks. Thus, our predictions would be a milestone for the antidotes designers and researchers who are working continuously within the study area of Nanoviridae family viruses. 
Materials and Methods
Protein sequence analysis
We retrieved the protein sequences of interest of Nanovirus species and Babuvirus species and analyzed. Simultaneously, we extracted the protein and genomic sequences of SCSV, MVDV, FBNYV, BBTV and ABTV from NCBI ( to perform the entropy measurement and phylogenetic reconstruction among the respective species. 
Assessment of solvent accessibility regions
The calculation of surface accessible regions was based on surface accessibility scale on a product instead of an addition within the window. The accessibility profile was obtained using the formulae mentioned by Emani (Emani et al., 1985). A hexapeptide sequence with surface probability greater than 1.0 indicates an increased probability for being found on the surface. We also concentrated on the flexibility data of all the peptides to increase the prediction accuracy. 
This has been done with Karplus and Schulz flexibility prediction. In this method, flexibility scale based on mobility of protein segments on the basis of the known temperature B factors of the a-carbons of 31 proteins of known structure was constructed (Karplus and Schulz, 1985). The calculation based on a flexibility scale is similar to classical calculation, except that the center is the first amino acid of the six amino acids window length and there are three scales for describing flexibility instead of a single one. For the assessment of solvent accessible regions in proteins, assay of different measurement was performed. This data and assessment may be useful for the prediction of the participant peptides in antigenic activity, surface region peptides and useful domain(s) in the sequence (Kyte and Doolittle, 1982; Abraham and Leo, 1987; Bull and Breese, 1974; Guy, 1985; Roseman, 1988; Wilson et al., 1981; Aboderin, 1971; Chothia, 1976; Janin, 1979; Cowan and Whittaker, 1990; Rose et al., 1985; Hopp and Woods, 1981; Eisenberg et al., 1984a; Eisenberg et al., 1984b; Gomase et al., 2007). 
The comparative analysis has been done to predict the similarity and difference in hydrophobicity and hydrophilicity among the coat proteins of the species (see supplementary data). 
Protein secondary structure prediction
The keys for secondary structure prediction were the residue conformational propensities, sequence edge effects, moments of hydrophobicity, position of insertions and deletions in aligned homologous sequences, moments of conservation, auto-correlation, residue ratios and filtering (Garnier et al., 1996; Gomase et al., 2008). The comparison among the secondary structures of respective protein sequences has been performed to predict the most probable regions and structures involved in antigenicity. 
Prediction of antigenicity
The line up predicts the peptides that are likely to be antibody responsive. The antigenicity and antigenic epitopes for MHC Class-I and II have been predicted by using BepiPred server, MHC2pred server, TmhcPred server, Hopp and Woods, Kolaskar and Tongaonkar antigenicity methods. 
The Parker hydrophilicity scales were also used within the assay to predict the hydrophilic peptides. In this method, hydrophilic scale based on peptide retention times during high-performance liquid chromatography (HPLC) on a reversed-phase column was constructed (Parker et al., 2003). A window of seven residues was used for analyzing epitope region. The corresponding value of the scale was introduced for each of the seven residues and the arithmetical mean of the seven residue value was assigned to the fourth, (i+3), residue in the segment. The comparative assessment has been calculated for the accuracy of the further prediction. Predictions are based on the comparative analysis, graphical representations and the tables which reflect the occurrence of amino acids at particular positions of experimentally known epitopes. 
MHC binding peptide prediction
MHC-I and MHC-II molecules have their own specificity for the binding with their respective epitopes. We used TmhcPred server and MHC2pred server (Bhasin et al., 2003; Bhasin and Raghava, 2005) to predict the MHC Class-I and MHC Class-II epitopes. The servers allow the prediction of potential MHC class-I and class-II binding regions from antigenic sequences. The server can predict MHC binding regions or peptides for 97 MHC alleles. The server uses the matrix data in linear fashion for prediction. In MHC2Pred, the molecules from protein sequences or sequence alignments use Position Specific Scoring Matrices (PSSMs). This server uses Support Vector Machine (SVM) for the predictions. 
The resultant epitopes were the output after proteosomal cleavage. 
Comparative analysis
Finally we compared the proteomic analysis and genomic analysis mentioned above for the interpretation of resultant antigenic efficiency and rate of possible mutational changes or post translational modifications. 
Results and Discussion
The randomly selected coat proteins sequences of FBNYV (NP_619570), MVDV (BAB78734), SCSV (AAA68021), BBTV (AAQ01659) and ABTV (ACN79533) are 172, 172, 169, 170 and 170 residues longer respectively. 
Prediction of antigenic peptides
The epitopes of Nanoviridae family have been predicted by comparative analysis of greatest hydrophilic regions. Kyte-Doolittle is a widely applied scale for delineating hydrophobic character of a protein (Kyte and Doolittle, 1982). Hydropathic regions achieve a positive value. Setting window size to 5-7 is suggested to be a good value for finding putative surface-exposed regions. Regions with values above 0 are hydrophobic in character. Hopp-Woods scale was designed for predicting potentially antigenic regions of polypeptides (Figure1) on the basis of the assumption that antigenic determinants would be exposed on the surface of the protein and thus would belocated on the hydrophilic regions (Hopp and Woods, 1981). This scale was developed for predicting potential antigenic sites of globular proteins, which are likely to be rich in charged and polar residues. This scale is essentially a hydrophilic index with apolar residues assigned negative values. Moreover, using a window size of 6, the region of maximal hydrophilicity is likely to be an antigenic site. The values, greater than 0, are hydrophilic and thus likely to be exposed on the surface of folded proteins. 
The prediction of antigenicity and epitopes has been done with BepiPred server and Kolaskar and Tongaonkar antigenicity (Figure 2A-2E). The antigenicity predictions for all the coat proteins in Nanoviridae family viruses are given in Table 1. The species within particular genera shows their common epitopes along with some positive modifications and diverse colleague epitopes as well. The common and/or similar antigenic peptide regions in Nanoviruses are 76-GELVNYLIVKCNSP- 89, 109-QDMITIIAKGK-119; 76-GELVNYIIVKSSSP-89, 109-QDMISIIAKGK-119 and 73-GELVNYLIVKSNS-85, 106-QDTVTIV- 112 in FBNYV, MVDV and SCSV respectively. Whereas, the common or similar antigenic regions in Babuviruses are 63-FMLLVCKVRPGRILHWA- 79 and 63-FMLLVCKVKPGRILHWA-79 in ABTV and BBTV respectively. 
These epitopes can be a milestone for vaccine and antidotes design against their respective viruses. 
Secondary structure comparison
The Garnier and Robson method (GOR) (Garnier et al., 1978) predicted the secondary structure of antigenic proteins of 5 coat proteins of Nanoviridae family species. Each residue has been assigned for the probability of alpha helix, beta strand or random coils by using a 7 residue window model. The comparison among the secondary structures of these species reveals that random coil residues and extended strands are more involved in antigenic propensity (Figure 3). 
Accessible surface area
The protein folded the hydrophobic side chains were preferentially buried away from the external solvent. For this property measurement and analysis, the scales for solvent accessibility have been improved. These scales have proved a beneficial role in prediction of antigenic potential sites in globular proteins (Linding et al., 2003). We predicted that the coat proteins of Nanoviridae family species are globular, hydrophobic and highly flexible. GlobPlot version 2.3 was used to predict the globularity and disorder in protein sequences by Russell/Linding definition (Linding et al., 2003). The Karplus and Schulz flexibility prediction (Karplus and Schulz, 1985) reflects that the coat proteins are highly flexible and somehow related to their solvent accessibility properties. 
Solvent accessible regions segregate the molecules on the basis of their hydrophobic and hydrophilic properties. It has been assumed that often the hydrophilic molecules of peptides are active in their antigenicity. The scales are developed for predicting the potential antigenic sites of globular proteins. It is shown that viral capsids are highly flexible (Figure 4F-4J) and thus have highly active antigenic peptides. 
Also we predicted the surface accessibility by Emani method (Emani et al., 1985). The Emani surface accessibility data reveals the most probable residues and regions of the proteins being found on the surface (Figure 4A- 4E). 
The predictions when compared with antigenicity data revealed that all the surface accessible peptides or residues are not mandatory to being involved in antigenicity. 
Prediction of disordered regions
It has been postulated that disorder-to-order transitions provide a mechanism for uncoupling binding affinity and specificity. Disordered protein sequences function in some cases to mechanically uncouple structured domains, making their dynamics less constrained. Linkers of this type are important in a diverse collection of proteins, from viral attachment proteins to transcription factors (Kissinger et al., 1999). The globular domains of protein are likely to be rich in charged and polar residues. Our disordered region prediction using GlobPlot version 2.3 (Linding et al., 2003) reveals four disordered regions containing proteins among five (Table 3). Nanoviruses (FBNYV, MVDV and SCSV) shows more disordered regions than Babuviruses (ABTV and BBTV). In fact, BBTV do not have any disordered peptide region. The whole peptide regions of BBTV confer the globular protein domains and absence of any disorder (Figure 5). The globularity and disorder of proteins were analysed using following parameters: 
Propensities=Russell/Linding smooth=10 dy/dx_smooth=10; Disorder frames: peak-frame=5 join-frame=4; Globularity frames: peak-frame=74 join-frame=15. 
Prediction of MHC binding peptides
The MHC binding peptides plays an important role in immune response. Our prediction was based on cascade support vector machine, using properties of all the sequences and their residual characteristics. The correlation coefficient of 0.86 was obtained by Jack-knife validation test. This test resulted in the MHC Class I and II binding regions (Table 4 -Table 13). 
MHCs are cell surface glycoproteins important for immunogenic reactions within the host organism. The MHC molecules show response to almost all antigens from the parasitic proteins. In our assay system, we predicted the binding affinity of coat proteins from five species in Nanoviridae family. The two Nanovirus species among three are having same residual length of 172 amino acids, while rest one having 169 amino acid residues. Babuvirus species show the same amino acid length of 170 residues. All these proteins resulted in different nonamers (Table 4-Table 13). 
FBNYV and MVDV have 172 amino acids each and show 164 nonamers. However, SCSV has 169 amino acid residues in its coat protein which shows 161 nonamers. Moreover, the Babuvirus species i.e. ABTV and BBTV are having 170 residues longer and shows 162 nonamers. The small MHC-I and MHC-II binders found are given in the Tables 4 - Tables 13. The binding of MHC to the respective peptide were analyzed as a log-transformed value related to the IC50 values in nM units. These epitopes are enough to elicit the desired immune response. These are the regions responsible for antigenic binding to the MHC associated with immune response of the host. 
Genomic analysis
We retrieved genomic sequences of involved viruses in our research, from NCBI ( ((ABTV) NC_010319, (BBTV) NC_003479, (FBNYV) NC_003567, (MVDV) NC_003646 and (SCSV) NC_003817). The alignment of the sequences revealed no conserved sequences (see the multiple sequence alignment below). 
CLUSTAL 2.0.12 multiple sequence alignment

NC_010319       ---------------------------GGCAGGGGGGCTTATTATTACCCCCCCTGCCCG
NC_003646       -------------------------CTGGGGCGGGGGCTTAGTATTACCCCC-GCCCCAG
NC_003817       ---------------------------------------TAGTATTACCCC--GTGCCGG
NC_003567       ----------------------GGCTCCAAGGTGGTTTTGAGTATTACCCAC----CTTG
                                                        * ********      *  *

NC_003646       GATCAGCGGAGTC--------ATTTAGA------CTCGCTATAA----------------
                *  *       *         *    **              *                

                 *       *        * **         **   **     *     * *       

                                    **    * *                            * 

                             * *           *  *                      **    

                  *      *                          *                   *  

                      * *             *                               **   

                        *           *          *           *       *       

                  *   *                    *     *   * *                   

                   *                     *     *    *                      

                                              *   *   *    * *             

                *          *  *   *   **       *  *       ***   *         *

                   *                     *      *         * *              *

                    *            *    *      *  *   *                    * *

                                 *         *        *        * *  *     *  

                   * *   *     **                      *  *  *     *       *

                 *     *            **   *        *    ** **         *     


NC_003567       ACGTCGTTTTTACCTCGGCGCCCTATAAATAGA---------------------------

NC_003479       GCGAGATC-------------------
NC_003646       TGAG-----------------------
NC_003817       ---------------------------
NC_003567       ---------------------------

This confers the high flexibility within the sequences. The entropy analysis proves the high flexibility and variability in the sequences (Figure 6). This might be cause of mutational changes or post translational modifications within the genome
A maximum parsimonious tree has been generated to check the evolutionary changes, mutational changes and/or post translational modifications and comparison with the predictions of our proteomic analyses. The evolutionary history was inferred using the Maximum Parsimony method. The most parsimonious tree with length = 1211 is shown. The consistency index is (0.853035), the retention index is (0.752022) and the composite index is 0.694890 (0.641501) for all sites and parsimony-informative sites (in parentheses). The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches (Felsenstein, 1985). The MP tree was obtained using the Close-Neighbor-Interchange algorithm (Nei and Kumar, 2000) with search level 3 (Felsenstein, 1985; Nei and Kumar, 2000) in which the initial trees were obtained with the random addition of sequences (10 replicates). The tree is drawn to scale with branch lengths calculated using the average pathway method (Nei and Kumar, 2000) and are in the units of the number of changes over the whole sequence. All positions containing gaps and missing data were eliminated from the dataset (Complete Deletion option). The reconstructed tree presumes that SCSV is distinct from rest of the Nanoviruses (Figure 7).
There were a total of 884 positions in the final dataset, out of which 371 were parsimony informative. Phylogenetic analyses were conducted in MEGA4 (Tamura et al., 2007). 
In all phylogenetic analyses of Nanoviridae isolates, ABTV and BBTV fall in the same clade, but on separate branches. From the tree, sequence length comparison and flexibility data; we can infer that the rate of mutation is high among the Nanoviridae family genera. Our genomic analysis supports the proteomic analysis of predictions. 
The comparative binding affinity analysis shows the mutational changes within SCSV made it highly potential virus among all Nanoviridae viruses against MHC Class-I molecules (Figure 8). However, MVDV shows greater affinity towards the MHC Class- II molecules as compare to other viruses within the analysis even though the SVSC has more numbers of antigenic determinants than MVDV. 
Despite the fact that, the nanoviruses attack plants mostly, the MHC binding peptide and their increasing competence forecast be a sign of future host could be a man. We should consider the severity of this issue. We confirm that the plant viruses can be harmful to humans and animals. Our finding would help to in future pharmacologists as well as agriculture scientists. 
The coat proteins of Nanoviridae family viruses are highly active for mutations. The possible mutational changes in the genome or post translational modifications within SCSV have decreased the length of the sequence but have increased the antigenic potential and numbers of antigenic binding regions on the surface of the protein. SCSV has the high antigenicity among all Nanoviridae family viruses. The probable mutational changes or post translational modifications within the epitopes may cause the selectivity and specificity of the virus to the host. Nanoviruses (FBNYV, MVDV and SCSV) contain few similar epitopes with either positive mutational changes or post translational modifications. However, Babuviruses also shows some antigenic similarity. Indeed, Nanoviruses have different epitopes as compare to Babuviruses. The MHC binding regions involve the random coils and extended strands with their flexibile property. The disorders within the surface protein sequences may play a crucial role in the generation of new antigenic binding regions. Apparently, the disordered regions do not take part in antigenicity of the protein, but can be found at the initial or terminal ends of the antigenic region on the peptides. All the surface accessible peptides or residues are not mandatory to being involved in antigenicity. The nonamers antigenic determinants in Nanoviridae family viral coat proteins are highly sensitive to H-2Kd and I-Ag7. These epitopes may play a highly informative and crucial role in antidotes production against FBNYV, MVDV, SCSV, ABTV and BBTV. One can apply one antidote for more than one species in its respective genera. The predicted MHC binding regions acts as barriers for antigens and also are responsible to generate immune response within the host. Thus, the antigenic epitopes are vital for activation of defense mechanism within the host. The species within particular genera shows their common epitopes along with some positive modifications and diverse colleague epitopes as well. The common antigenic peptide regions in Nanoviruses are 76-GELVNYLIVKCNSP-89, 109-QDMITIIAKGK-119, 76-GELVNYIIVKSSSP-89, 109-QDMISIIAKGK-119 and 73-GELVNYLIVKSNS- 85, 106-QDTVTIV-112 in FBNYV, MVDV and SCSV respectively. Whereas, the common antigenic regions in Babuviruses are 63-FMLLVCKVRPGRILHWA- 79, 89-DPTVVLEAPGLFIKPANSHLVKLVCSGE-116 and 63-FMLLVCKVKPGRILHWA-79, 90-PTTCLEAPGLFIKPEHSHLVKLVCSGE- 116 in ABTV and BBTV respectively. These common antigenic peptides can be used as their identifiers. These are also important for synthetic peptide vaccine production or antidotes production against the relevant viruses. 
We are grateful to Department of Biotechnology (DBT) – Ministry of Science and Technology, Govt. of India, for providing us the Bioinformatics Infrastructure Facility (BIF) for our research work. We are also thankful to Dr. Mrs. Nilima Kshirsagar, Hon’ble Pro-Vice Chancellor, Maharashtra University of Health Sciences, Nashik, India for the kind support. 
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