Abstract

Mungbean yellow mosaic India virus (MYMIV) is recognized as one of the most economically important viruses that affects several leguminous crops like mungbean, urdbean, cowpea, pigeonpea etc. and occurs in Indian subcontinent. In the present study, coat protein of MYMIV is being used to fi nd out highly suitable MHC binding peptides and epitopes. Thirty nine peptide regions were found to have high af fi nity to TAP binding peptides using cascade support vector machine (SVM). Few of these coat protein TAP transporters are 201- NRFFKVNNY with score 9.208, 108- KRFCIKSVY with score 8.817, 44-RWTNRPMWR with score 8.790, 134- NTVMFKLCR with score 8.672 and 41- KRRRWTNRP with score 8.498 where the scores are based on the average af fi nity of an amino acid at particular position. The SVM based method for prediction of promiscuous MHC Class II binders reported MHCII-IAb peptide regions, 30- PASAGGVPT, 127-IKSKNHTNT, 44-RWTNRPMWR, 6-YDTAFSTPI, (optimal score 1.220); MHCII-IAd peptide regions, 226-NALLLYMAC, 30-PASAGGVPT, 32-SAGGVPTNM, 236- HASNPVYAT, (optimal score 0.620); MHCII-IAg7 peptide regions, 13-PISNARRRL, 212- YNHQEAAKY, 221-ENHTENALL, 209-YVVYNHQEA, (optimal score 1.569) and MHCII- RT1.B peptide regions, 223- HTENALLLY, 188- TGGQYACKE, 168- TVKNDLRDR, 4-RTYDTAFST, (optimal score 0.932) as possible predicted binders from coat protein. The most suitable predicted segments in coat protein of MYMI virus for developing speci fi c antibodies found in this study are 56-FYRLYRSPDVPRGCEGPCKVQSF–78, 206-VNNYVVYNHQ-215 and 108-KRFCIKSVYITG-119. Fragments identi fi ed through this approach tend to be high- ef fi ciency binders, in which larger percentage of their atoms are directly involved in binding as compared to larger molecules. These fragments may, therefore, be used in cross protection and to develop begomovirus speci fi c antibodies that can be exploited in sero-diagnostics.

Highlights

  • Yellow mosaic disease of many legumes in India and other South Asian countries is caused by whitefly (Bemisia tabaci Genn.) transmitted by geminiviruses belonging to the family Geminiviridae and genus Begomovirus

  • Antigenicity prediction tools adopted in this study predict those segments from coat protein that are likely to be antigenic by eliciting an antibody response using Hopp and Woods (Hopp and Woods, 1981), Welling (Welling et al, 1985), Parker (Parker et al, 1986), B-EpiPred Server (Larsen et al, 2006) and Kolaskar and Tongaonkar (Kolaskar and Tongaonkar,1990)

  • Hopp-Woods scale was used to predict potential antigenic sites. This may be useful in predicting membrane-spanning domains, potential antigenic sites and regions that are likely exposed on the protein surface (Gomase, 2006; Janin, 1979; Abraham and Leo, 1987; Bull and Breese, 1974)

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Summary

Introduction

Yellow mosaic disease of many legumes in India and other South Asian countries is caused by whitefly (Bemisia tabaci Genn.) transmitted by geminiviruses belonging to the family Geminiviridae and genus Begomovirus. Four species viz., Mungbean yellow mosaic virus (MYMV), Mungbean yellow mosaic India virus (MYMIV), Dolichos yellow mosaic virus (DYMV) and Horsegram yellow mosaic virus (HYMV) are known to cause yellow mosaic disease in different leguminous species All these viruses are bipartite begomoviruses, have geminate (twin) particles, 18-20 nm in diameter, 30 nm long, apparently consisting of two incomplete icosahedra joined together in a structure with 22 pentameric capsomeres and 110 identical protein subunits (Qazi et al, 2007). MHC molecules have been well characterized in terms of their role in immune reactions They bind to some of the peptide fragments generated after proteolytic cleavage of antigen (Kumar et al, 2007). The present paper deals with the possibilities of exploiting coat protein of MYMIV to find out the highly suitable MHC binding peptide and have high affinity to TAP biding peptides that can be used for inducing cross protection and as immunogen to produce antiserum for the development of sero-diagnostics for begomoviruses

Materials and Methods
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