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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
E-mail: [email protected]

Received Date: April 01, 2010; Accepted Date: May 03, 2010; Published Date: 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 (Figure 1) 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.


Figure 1: Kyte-Doolittle hydrophobicity plots (A, C, E, G and I) and Hopp and Woods hydrophilicity plots (B, D, F, H and J) of coat proteins in FBNYV, MVDV, SCSV, ABTV and BBTV respectively. In Kyte-Doolittle hydrophobicity plots (A, C, E, G and I), the regions with values above 0 are hydrophobic in character. In Hopp and Woods hydrophilicity plots (B, D, F, H and J), the regions with values above 0 are hydrophilic in character.

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.


Figure 2: Kolaskar and Tongaonkar antigenicity prediction plots and Parker hydrphobicity plots of FBNYV (A and F), MVDV (B and G), SCSV (C and H), ABTV (D and I) and BBTV (E and J). Threshold = 1.000 for Kolaskar and Tongaonkar antigenicity prediction plots (A - E). Threshold = 1.384; 1.157; 1.577; 0.7790 and 558 for Parker hydrphobicity plots (F - J) respectively.

Viruses Start Position End Position Peptide Peptide Length
109 119 QDMITIIAKGK 11
66 72 MLTCTLR 7
109 119 QDMISIIAKGK 11
45 51 GAEVKPF 7
96 104 NPSLMVKES 9
106 112 QDTVTIV 7
132 140 RKFVKLGSG 9
42 47 IKLYRI 6
127 135 EVECLLRKT 9
118 125 EAGVATGT 8

Table 1: Predicted antigenic peptides of Nanoviridae family viruses.

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).


Figure 3: Secondary structures prediction plots of coat proteins in FBNYV (A), MVDV (B), SCSV (C), ABTV (D) and BBTV (E) using GOR method. Red bars indicate the probable antigenic peptide regions.

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.


Figure 4: Emani surface accessibility prediction plots (A-E) and Karplus and Schulz fl exibility plots (F-J) of FBNYV, MVDV, SCSV, ABTV and BBTV respectively. Threshold = 1.000.

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:

Viruses Start Position End Position Peptide Peptide Length
58 63 ARYKMK 6
58 63 ARYKMR 6
21 27 IAYKPPS 7
54 59 GSRYSM 6
133 138 RKTTLL 6
161 166 NYQNRI 6
23 29 AATSHDY 7
133 138 RKTTVL 6
161 166 NYQNRI 6

Table 2: Predicted surface accessible peptides of Nanoviridae family viruses.

Viruses Position Coat protein disorder No. of disorders
ABTV 118-123 EAPVGG 1
BBTV --- --- 0

Table 3: Predicted coat proteins disordered regions in Nanoviridae family viruses.


Figure 5: Protein globular domain and disorder prediction of (A) FBNYV (B) MVDV (C) SCSV (D) BBTV and (E) ABTV. Residue number on X-axis and Disorder propensity on Y-axis. Green = globular domains; Blue = Disorder and Yellow = low complexity region.

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).

Allele Rank Sequence Residue No. Score Predicted affinity
H-2Db 1 GELVNYLIV 76 3.583 Low
2 TSVALKAVL 156 2.149 Low
3 AGITQTQHL 142 2.149 Low
4 VMLTCTLRM 65 2.149 Low
H-2Dd 1 AGITQTQHL 142 2.995 Low
2 AGTDCTKSF 126 1.974 Low
3 KGKVESNGV 117 1.974 Low
4 KSFNRFIKL 132 1.791 Low
H-2Ld 1 APGELVNYL 74 5.010 High
2 SPALLVKES 99 3.401 Low
3 SPISSWSAA 88 3.401 Low
4 RPYKSSVPT 22 3.401 Low
H-2Kb 1 QTQHLYVVL 146 4.094 Moderate
2 KSFNRFIKL 132 2.995 Low
3 AGITQTQHL 142 1.335 Low
4 LEHRVYVEV 164 0.970 Low
H-2Kd 1 RYKMKKVML 59 7.783 High
2 RFIKLGAGI 136 7.560 High
3 LYVVLYTSV 150 7.272 High
4 AFTSPALLV 96 5.662 High
H-2Kk 1 LEHRVYVEV 164 4.605 Moderate
2 TEIKPEGDV 49 4.605 Moderate
3 KDEVVGTEI 43 4.605 Moderate
4 GELVNYLIV 76 3.912 Moderate

Table 4: Peptide binders of FBNVY coat protein to MHC-I molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
H-2Db 1 VSNWNRNGM 2 5.655 High
2 GELVNYIIV 76 3.583 Low
3 SPIANWAAA 88 3.496 Low
4 TSVALKVVL 156 2.149 Low
H-2Dd 1 AGISQTQHL 142 2.995 Low
2 AGTDCTKSF 126 1.974 Low
3 KGKVESNGV 117 1.974 Low
4 KSFNKFIRL 132 1.791 Low
H-2Ld 1 MPPGELVNY 73 4.682 Moderate
2 KPVVPITRV 25 4.094 Moderate
3 APALLVKES 99 3.401 Low
4 SPIANWAAA 88 3.401 Low
H-2Kb 1 QTQHLYVVM 146 4.094 Moderate
2 LEHRVYIEL 164 3.273 Low
3 KSFNKFIRL 132 2.995 Low
4 AGISQTQHL 142 1.153 Low
H-2Kd 1 RYKMRKVML 59 7.783 High
2 KFIRLGAGI 136 7.742 High
3 LYVVMYTSV 150 7.272 High
4 MYTSVALKV 154 6.579 High
H-2Kk 1 KDEVVGCEI 43 4.605 Moderate
2 GELVNYIIV 76 3.912 Moderate
3 CEIKPDGDV 49 3.912 Moderate
4 LEHRVYIEL 164 3.688 Low

Table 5: Peptide binders of MVDV coat protein to MHC-I molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
H-2Db 1 SSFSNPSLM 92 5.655 High
2 ESVLNKRDV 35 3.663 Low
3 GELVNYLIV 73 3.583 Low
4 ESVQDTVTI 103 2.236 Low
H-2Dd 1 SGISQTQHL 139 3.178 Low
2 VGGGKLESS 112 2.484 Low
3 AGKDVTKSF 123 1.974 Low
4 KSFRKFVKL 129 1.791 Low
H-2Ld 1 APGELVNYL 71 5.010 High
2 KPFADGSRY 49 4.094 Moderate
3 KPPSSKVVS 24 3.806 Moderate
4 ISQTQHLYL 141 3.624 Low
H-2Kb 1 KSFRKFVKL 129 3.178 Low
2 QTQHLYLII 143 2.890 Low
3 ISQTQHLYL 141 1.376 Low
4 SGISQTQHL 139 1.153 Low
H-2Kd 1 IYSSDAMKI 151 7.965 High
2 RYSMKKVML 56 7.783 High
3 KFVKLGSGI 133 7.742 High
4 AYKPPSSKV 22 6.579 High
H-2K 1 LETRMYIDV 161 4.605 Moderate
2 GELVNYLIV 73 3.912 Moderate
3 QTQHLYLII 143 2.302 Low
4 ESVQDTVTI 103 2.302 Low

Table 6: Peptide binders of SCSV coat protein to MHC-I molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
H-2Db 1 IVPENTIKL 36 6.674 High
2 LLRRNVTEL 137 4.094 Moderate
3 IKPANSHLV 101 3.496 Low
4 VKLVCSGEL 109 2.069 Low
H-2Dd 1 GGTSEVECL 123 3.178 Low
2 IVPENTIKL 36 3.178 Low
3 VRPGRILHW 70 2.302 Low
4 IKPANSHLV 101 1.791 Low
H-2Ld 1 LPRYFIWKM 54 3.806 Moderate
2 APGLFIKPA 96 3.401 Low
3 RPGRILHWA 71 3.401 Low
4 VPENTIKLY 37 2.890 Low
H-2Kb 1 FIWKMFMLL 58 2.667 Low
2 IVPENTIKL 36 2.069 Low
3 FLYLAFYCS 147 1.609 Low
4 CLLRKTTLL 130 0.875 Low
H-2Kd 1 FYCSSGVTI 152 7.965 High
2 DYAVDTSFI 28 7.783 High
3 SFIVPENTI 34 7.560 High
4 YFIWKMFML 57 7.377 High
H-2Kk 1 VECLLRKTT 128 3.401 Low
2 LEAPVGGGT 117 2.995 Low
3 TELDFLYLA 143 2.302 Low
4 WDVKDPTVV 85 2.302 Low

Table 7: Peptide binders of ABTV coat protein to MHC-I molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
H-2Db 1 LVPENTVKV 36 3.583 Low
2 AMIKSSWEI 79 2.331 Low
3 VKLVCSGEL 109 2.069 Low
4 CKVKPGRIL 68 1.974 Low
H-2Dd 1 TGTSDVECL 123 3.178 Low
2 VKPGRILHW 70 2.302 Low
3 IKPEHSHLV 101 1.974 Low
4 LVPENTVKV 36 1.974 Low
H-2Ld 1 VPENTVKVF 37 4.499 Moderate
2 LPRYFIWKM 54 3.806 Moderate
3 KPGRILHWA 71 3.401 Low
4 YSSLGSILV 29 2.564 Low
H-2Lb 1 ATSHDYSSL 24 3.091 Low
2 FIWKMFMLL 58 2.667 Low
3 YLYLAFYCS 147 1.609 Low
4 VTEVDYLYL 142 1.386 Low
H-2Kd 1 DYSSLGSIL 28 7.965 High
2 FYCSAGVSI 152 7.783 High
3 YFIWKMFML 57 7.377 High
4 MFMLLVCKV 62 6.173 High
H-2Kk 1 HDYSSLGSI 27 4.605 Moderate
2 VECLLRKTT 128 3.401 Low
3 LEAGVATGT 117 2.995 Low
4 TEVDYLYLA 143 2.302 Low

Table 8: Peptide binders of BBTV coat protein to MHC-I molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
I-Ab 1 YKSSVPTTR 24 1.154 High
2 PTTRVVVHQ 29 1.056 Moderate
3 QHLYVVLYT 148 1.041 Moderate
4 ITQTQHLYV 144 1.000 Moderate
I-Ad 1 PISSWSAAF 89 0.575 Moderate
2 FIKLGAGIT 137 0.505 Moderate
3 GTKGRRTPR 9 0.504 Moderate
4 TSVALKAVL 156 0.464 Low
I-Ag7 1 NGVAGTDCT 123 1.433 High
2 GKVESNGVA 118 1.318 High
3 EIKPEGDVA 50 1.316 High
4 SWSAAFTSP 92 1.310 High
RT1.B 1 ITQTQHLYV 144 0.606 Moderate
2 AFTSPALLV 96 0.602 Moderate
3 TQTQHLYVV 145 0.590 Moderate
4 AAFTSPALL 95 0.586 Moderate

Table 9: Peptide binders of FBNVY coat protein to MHC-II molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
I-Ab 1 VALKVVLEH 158 1.237 High
2 PITRVVVHQ 29 0.822 Moderate
3 ISQTQHLYV 144 0.808 Moderate
4 QHLYVVMYT 148 0.772 Moderate
I-Ad 1 GAGISQTQH 141 0.578 Moderate
2 VALKVVLEH 158 0.497 Low
3 TSVALKVVL 156 0.466 Low
4 TAPALLVKE 98 0.421 Low
I-Ag7 1 SPIANWAAA 88 2.138 High
2 WAAAFTAPA 93 1.698 High
3 PIANWAAAF 89 1.642 High
4 NWAAAFTAP 92 1.439 High
RT1.B 1 AAAFTAPAL 94 1.010 Moderate
2 AFTAPALLV 96 0.716 Moderate
3 FTAPALLVK 97 0.636 Moderate
4 ANWAAAFTA 91 0.611 Moderate

Table 10: Peptide binders of MVDV coat protein to MHC-II molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
I-Ab 1 PSLMVKESV 97 0.990 Moderate
2 RYSMKKVML 56 0.785 Moderate
3 IAYKPPSSK 21 0.776 Moderate
4 DAMKITLET 155 0.755 Moderate
I-Ad 1 KLESSGTAG 116 0.579 Moderate
2 GGGKLESSG 113 0.571 Moderate
3 GSGISTQH 138 0.541 Moderate
4 APGELVNYL 71 0.447 Low
I-Ag7 1 SGTAGKDVT 120 1.631 High
2 SRIAYKPPS 19 1.379 High
3 SPIANWSSS 85 1.360 High
4 TLTMAPGEL 67 1.309 High
RT1.B 1 TLTMAPGEL 67 0.624 Moderate
2 AMKITLETR 156 0.426 Low
3 QTQHLYLII 143 0.392 Low
4 ATLTMAPGE 66 0.379 Low

Table 11: Peptide binders of SCSV coat protein to MHC-II molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
I-Ab 1 IKLYRIEPT 42 1.076 Moderate
2 KALKKRKAV 7 1.001 Moderate
3 IWKMFMLLV 59 0.857 Moderate
4 TTLLRRNVT 135 0.820 Moderate
I-Ad 1 LEAPVGGGT 117 0.561 Moderate
2 LDFLYLAFY 145 0.547 Moderate
3 FIWKMFMLL 58 0.496 Low
4 GTSEVECLL 124 0.491 Low
I-Ag7 1 CKVRPGRIL 68 1.439 High
2 TVVLEAPGL 91 1.438 High
3 SGELEAPVG 114 1.421 High
4 SHDYAVDTS 26 1.296 High
RT1.B 1 TTSHDYAVD 24 0.773 Moderate
2 TSHDYAVDT 25 0.443 Low
3 SKATTSHDY 21 0.434 Low
4 TELDFLYLA 143 0.426 Low

Table 12: Peptide binders of ABTV coat protein to MHC-II molecules.

Allele Rank Sequence Residue No. Score Predicted affinity
I-Ab 1 RKTTVLRKN 133 1.088 Moderate
2 IWKMFMLLV 59 0.857 Moderate
3 GSILVPENT 33 0.732 Moderate
4 PGLFIKPEH 97 0.630 Moderate
I-Ad 1 DYSSLGSIL 28 0.720 Moderate
2 LEAGVATGT 117 0.661 Moderate
3 TTCLEAPGL 91 0.528 Moderate
4 FIWKMFMLL 58 0.496 Low
I-Ag7 1 SINYQNRIT 159 1.496 High
2 YLYLAFYCS 147 1.360 High
3 TEVDYLYLA 143 1.321 High
4 SGELEAGVA 114 1.318 High
RT1.B 1 ATSHDYSSL 24 0.757 Moderate
2 INQPTTCLE 87 0.537 Moderate
3 TEVDYLYLA 143 0.485 Low
4 TTCLEAPGL 91 0.473 Low

Table 13: Peptide binders of BBTV coat protein to MHC-II molecules.

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_003817 ---------------------------------------TAGTATTACCCC--GTGCCGG
* ******** * *

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.


Figure 6: Graphical representation of entropy analysis of Nanoviridae family viruses. Residue numbers or alignment positions on X-axis and entropy (Hx) on Y – axis.

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 calculatedusing 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).


Figure 7: Evolutionary relationships of Nanoviridae family viruses on the basis of genomic sequences. The most parsimonious tree is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches.

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.


Figure 8: Binding affi nity potential analysis of Nanoviridae family viruses against MHC-I (A) and MHC-II (B). Species of the genera are on X – axis and binding affi nity potential on Y – axis.


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.


  1. Aboderin AA (1971) An empirical hydrophobicity scale for alpha-amino-acids and some of its applications. Int J Biochem 2: 537-544. » CrossRef » Google Scholar
  2. Abraham DJ, Leo AJ (1987) Extension of the fragment method to calculate amino acid zwitterions and side chain partition coeffi cients. Proteins 2: 130- 152.» CrossRef » PubMed » Google Scholar
  3. Aronson MN, Meyer AD, Györgyey J, Katul L, Vetten HJ, et al. (2000) Clink, a Nanovirus-Encoded Protein, Binds both pRB and SKP1. J Virology 74: 2967- 2972.» CrossRef » PubMed » Google Scholar
  4. Bhasin M, Raghava GP (2005) Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res 33: W202-207.» CrossRef » PubMed » Google Scholar
  5. Bhasin M, Singh H, Raghava GPS (2003) MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665-666. » CrossRef » PubMed » Google Scholar
  6. Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L (1998) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14: 121-130.» CrossRef » PubMed » Google Scholar
  7. Bull HB, Breese K (1974) Surface tension of amino acid solutions: A hydrophobicity scale of the amino acid residues. Arch Biochem Biophys 161: 665-670.» CrossRef » PubMed » Google Scholar
  8. Chothia C (1976) The nature of accessible and buried surfaces in proteins. J Mol Biol 105: 1-12.» CrossRef » PubMed » Google Scholar
  9. Cowan R, Whittaker RG (1990) Hydrophobicity indices at ph 3.4 determined by HPLC. Pept Res 3: 75- 80.» PubMed » Google Scholar
  10. Dale JL (1987) Banana bunchy top: An economically important tropical plant virus disease. Adv. Virus Res 33: 301-325.» CrossRef » PubMed » Google Scholar
  11. Eisenberg D, Schwarz E, Komaromy M, Wall R (1984a) Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol 179: 125-142.» CrossRef » PubMed » Google Scholar
  12. Eisenberg D, Weiss RM, Terwilliger TC (1984b) The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci USA 81: 140- 144.» CrossRef » PubMed » Google Scholar
  13. Emani EA, Hughes JV, Perlow DS, Boger J (1985) Induction of hepatitis A virusneutralizing antibody by a virus-specifi c synthetic peptide. J Virol 55: 836-839.» CrossRef » PubMed » Google Scholar
  14. Fauquet CM, Mayo MA, Maniloff J, Desselberger U, Ball LA (2005) Virus Taxonomy. Eighth Report of the International Committee on Taxonomy of Viruses. Amsterdam: Academic Press.» CrossRef» Google Scholar
  15. Felsenstein J (1985) Confi dence limits on phylogenies: An approach using the bootstrap. Evolution 39: 783-791.» CrossRef» Google Scholar
  16. Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120: 97-120.» CrossRef » PubMed » Google Scholar
  17. Garnier J, Gibrat JF, Robson B (1996) GOR: secondary structure prediction method version IV. Methods Enzymol 266: 540-553.» PubMed » Google Scholar
  18. Gomase VS, Kale KV, Chikhale NJ, Changbhale SS (2007) Prediction of MHC Binding Peptides and Epitopes from Alfalfa mosaic virus. Curr Drug Discov Technol 4: 117-121.» CrossRef » PubMed » Google Scholar
  19. Gomase VS, Changbhale SS, Kale KV (2008) Insilico analysis of nucleocapsid protein from groundnut ringspot virus. Advancements in Information Technology and Internet Security 370-378.
  20. Grylls NE, Butler FC (1956) An aphid-transmitted virus affecting subterranean clover. J Aust Znst agric Sci 22: 73-74.»Google Scholar
  21. Guy HR (1985) Amino acid side chain partition energies and distributions of residues in soluble proteins. Biophys J 47: 61-70.» CrossRef » PubMed » Google Scholar
  22. Hanley-Bowdoin L, Settlage SB, Robertson D (2004) Reprogramming plant gene expression: a prerequisite to geminivirus DNA replication. Mol Plant Pathol 5: 149-156.» CrossRef »Google Scholar
  23. Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 78: 3824-3828.» CrossRef » PubMed » Google Scholar
  24. Janin J (1979) Surface and inside volumes in globular proteins. Nature 277: 491-492.» CrossRef » PubMed » Google Scholar
  25. Karplus PA, Schulz GE (1985) Prediction of Chain Flexibility in Proteins - A tool for the Selection of Peptide Antigens. Naturwissenschafren 72: 212-213.» CrossRef» Google Scholar
  26. Katul L, Maiss E, Vetten HJ (1995) Sequence-Analysis of A Faba Bean Necrotic Yellows Virus-Dna Component Containing A Putative Replicase Gene. J Gen Virol 76: 475-479.» CrossRef » PubMed » Google Scholar
  27. Kissinger P, Rice J, Farley T, Trim S, Jewitt K, et al. (1999) Application of Computer-assisted Interviews to Sexual Behavior Research. Am J Epidemiol 149: 950-954.» CrossRef » PubMed » Google Scholar
  28. Kropshofer H, Max H, Halder T, Kalbus M, Muller CA, et al. (1993) Self-peptides from four HLA-DR alleles share hydrophobic anchor residues near the NH2- terminal including proline as a stop signal for trimming. J Immunol 151: 4732- 4742.» CrossRef » PubMed » Google Scholar
  29. Kyte J, Doolittle RF (1982) A Simple Method for Displaying the Hydropathic Character of a Protein. J Mol Biol 157: 105-132.» CrossRef » PubMed » Google Scholar
  30. Lageix S, Catrice O, Deragon JM, Gronenborn B, Pelissier T, et al. (2007) The nanovirus-encoded Clink protein affects plant cell cycle regulation through interaction with the retinoblastoma-related protein. J Virol 81: 4177-4185.» CrossRef » PubMed » Google Scholar
  31. Li L, Kapoor A, Slikas B, Bamidele OS, Wang C, et al. (2010) Multiple diverse circoviruses infect farm animals and are commonly found in human and chimpanzee feces. J Virol 84: 1674-1682.» CrossRef » PubMed » Google Scholar
  32. Linding R, Russell RB, Neduva V, Gibson TJ (2003) GlobPlot: exploring protein sequences for globularity and disorder. Nucleic Acids Res 31: 3701-3708.» PubMed » Google Scholar
  33. Madden DR, Garboczi DN, Wiley DC (1993) The antigenic identity of peptide- MHC complexes: a comparison of the conformations of fi ve viral peptides presented by HLA-A2. Cell 75: 693-708.» CrossRef » PubMed » Google Scholar
  34. Magee CJP (1953) Some aspects of the bunchy top disease of banana and other Musa spp. Proc Royal Society of New South Wales 87: 3-18.» Google Scholar
  35. Makkouk KM, Vetten HJ, Katul L, Franz A, Madkour MA (1998) Epidemiology and control of faba bean necrotic yellows virus. In: Plant Virus Disease Control. A. Hadidi, R.K. Khetarpal and H. Koganezawa (eds.). APS Press, The American Phytopathological Society, St. Paul, Minnesota, USA pp534-540.
  36. Nei M, Kumar S (2000) Molecular Evolution and Phylogenetics. Oxford University Press, New York.
  37. Ocfemia GO (1926) Progress report on bunchy-top of abaca or manila hemp. Phytopathology 16: 894.» Google Scholar
  38. Ocfemia GO (1930) Bunchy-top of abaca or manila hemp I. A study of the cause of the disease and its method of transmission. Am J Bot 17: 1-18.» CrossRef» Google Scholar
  39. Ocfemia GO, Buhay GG (1934) Bunchy top of abaca or Manila hemp-2. Further studies on the transmission of the disease and a trial planting of abaca seedlings in a bunchy top devasted fi eld. Philippine Agriculturists 22: 267-80.» Google Scholar
  40. Ohki ST, Doi Y, Yora K (1975) Small spherical partials found in broad been plants infected with Milk- Vetch Dwarf Virus. Phytopathological note 41: 508- 510.» CrossRef» Google Scholar
  41. Parker JMR, Guo D, Bhasin M, Singh H, Raghava GPS (2003) A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665.» CrossRef » PubMed » Google Scholar
  42. Raymundo AD, Bajet NB (2000) Epidemiology and integrated management of Abaca Bunchy Top in Philippines, In: Molina A, Roa V, Bay-Petersen J, Carpio A, Joven J (Eds) Proceedings in Davao City. Philippines 89-97.» CrossRef» Google Scholar
  43. Rhodes DA, Trowsdale J (1999) Genetics and molecular genetics of the MHC. Rev in immunogenetics 1: 21-31.» PubMed » Google Scholar
  44. Rose GD, Geselowitz AR, Lesser GJ, Lee RH, Zehfus MH (1985) Hydrophobicity of amino acid residues in globular proteins. Science 229: 834-838.» CrossRef » PubMed » Google Scholar
  45. Roseman MA (1988) Hydrophilicity of polar amino acid side-chains is markedly reduced by fl anking peptide bonds. J Mol Biol 200: 513-522.» CrossRef » PubMed » Google Scholar
  46. Stern LJ, Brown JH, Jardetzky TS, Gorga JC, Urban RG, et al. (1994) Crystal structure of the human class II MHC protein HLA-DR1 complexed with an infl uenza virus peptide. Nature 368: 215-221.» CrossRef » PubMed » Google Scholar
  47. Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol 24: 1596-1599.» CrossRef » PubMed » Google Scholar
  48. Timchenko T, Katul L, Aronson M, Vega-Arreguin JC, Ramirez BC, et al. (2006) Infectivity of Nanovirus DNAs: Induction of disease by cloned genome components of Faba bean necrotic yellows virus. J Gen Virol 87: 1735-1743.» CrossRef » PubMed » Google Scholar
  49. Tong JC, Zhang GL, Tan TW, August JT, Brusic V, et al. (2006) Prediction of HLA-DQ3.2beta ligands: evidence of multiple registers in class II binding peptides. Bioinformatics 22: 1232-1238.» CrossRef » PubMed » Google Scholar
  50. Wilson KJ, Honegger A, Stotzel RP, Hughes GJ (1981) The behaviour of peptides on reverse-phase supports during high-pressure liquid chromatography. Biochem J 199: 31-41.» PubMed » Google Scholar
  51. Yewdell JW, Bennink JR (1999) Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu Rev Immunol 17: 51- 88.» CrossRef » PubMed » Google Scholar
  52. Yusibov V, Hooper DC, Spitsin SV, Fleysh N, Kean RB (2002) Expression in plants and immunogenicity of plant virus-based experimental rabies vaccine. Vaccine 20: 3155-3164.» CrossRef » PubMed » Google Scholar
  53. Zhang T, Breitbart M, Lee WH, Run J, Wei CL (2006) RNA viral community in human feces: Prevalence of plant pathogenic viruses. PLoS Biol 4: e3.» CrossRef » PubMed » Google Scholar
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