Computational Analysis and Predicting Ligand Binding Site in the Rose leaf curl virus and Its Betasatellite Proteins: A Step Forward for Antiviral Agent Designing

  • Avinash Marwal
  • Megha Mishra
  • Charvee Sekhsaria
  • R. K. Gaur


Computational approach was done in protein molecules of the Rose leaf curl virus and its betasatellite component isolated from Rose plants. Moreover in-depth study was done using in silico approach such as restriction map, GC profile and prediction of binding sites for ligand molecule analysis. Hence, an approach has been taken into consideration to unearth a treatment against geminiviruses, resulting in huge yield loss across the globe. This study provides a great deal of novel knowledge and will be employed for the selection of inhibitors in opposition to geminivirus proteins focusing on begomovirus and paves a way for developing antiviral agents in the near future.


Rose Geminiviruses Begomovirus Satellites Proteins Binding sites 



The authors are thankful to Science and Engineering Research Board – Department of Science and Technology, New Delhi, India, for the financial assistance (File No. YSS/2015/000265) and also to University Grant Commission, New Delhi for providing financial assistantship under Research Award for Teacher (F.30-1/2014/RA-2014-16-GE-RAJ-4696 (SA-II).


  1. Aloy P, Russell RB (2004) Ten thousand interactions for the molecular biologist. Nat Biotechnol 22:1317–1321CrossRefPubMedGoogle Scholar
  2. Bairoch A (2000) The ENZYME database in (2000). Nucleic Acids Res 28:304–305CrossRefPubMedPubMedCentralGoogle Scholar
  3. Balakrishnan M, Srivastava RC, Pokhriyal M (2010) Homology modeling and docking studies between HIV-1 protease and carbamic acid. Indian J Biotechnol 9:96–100Google Scholar
  4. Cimermancic P, Weinkam P, Rettenmaier TJ, Bichmann L, Keedy DA, Woldeyes RA, Schneidman-Duhovny D, Demerdash ON, Mitchell JC, Wells JA, Fraser JS, Sali A (2016) CryptoSite: expanding the druggable proteome by characterization and prediction of cryptic binding sites. J Mol Biol 428:709–719CrossRefPubMedPubMedCentralGoogle Scholar
  5. Gao F, Zhang C (2006) GC-profile: a web-based tool for visualizing and analyzing the variation of GC content in genomic sequences. Nucleic Acids Res 34:W686–W691CrossRefPubMedPubMedCentralGoogle Scholar
  6. Groisman EA, Ochman H (1996) Pathogenicity islands: bacterial evolution in quantum leaps. Cell 87:791–794CrossRefPubMedGoogle Scholar
  7. Hacker J, Carniel E (2001) Ecological fitness, genomic islands and bacterial pathogenicity: a Darwinian view of the evolution of microbes. EMBO Rep 2:376–381CrossRefPubMedPubMedCentralGoogle Scholar
  8. Hannum G et al (2009) Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet 5:e1000782CrossRefPubMedPubMedCentralGoogle Scholar
  9. Heinrichs A (2008) Proteomics: solving a 3D jigsaw puzzle. Nat Rev Mol Cell Biol 9:3–3CrossRefGoogle Scholar
  10. Hentschel U, Hacker J (2001) Pathogenicity islands: the tip of the iceberg. Microbes Infect 3:545–548CrossRefPubMedGoogle Scholar
  11. Ilyas M, Nawaz K, Shafiq M, Haider MS, Shahid AA (2013) Complete nucleotide sequences of two begomoviruses infecting Madagascar periwinkle (Catharanthus roseus) from Pakistan. Arch Virol 158:505–510CrossRefPubMedGoogle Scholar
  12. Ivashchenko A, Pyrkova A, Niyazova R, Alybayeva A, Baskakov K (2016) Prediction of miRNA binding sites in mRNA. Bioinformation 12(4):237–240CrossRefGoogle Scholar
  13. Källberg M, Wang H, Wang S, Peng J, Wang Z, Lu H, Xu J (2012) Template-based protein structure modeling using the RaptorX web server. Nat Protoc 7:1511–1522CrossRefPubMedPubMedCentralGoogle Scholar
  14. Koonin EV, Makarova KS, Aravind L (2001) Horizontal gene transfer in prokaryotes: quantification and classification. Annu Rev Microbiol 55:709–742CrossRefPubMedPubMedCentralGoogle Scholar
  15. Lima AT, Sobrinho RR, González-Aguilera J, Rocha CS, Silva SJ, Xavier CA, Silva FN, Duffy S, Zerbini FM (2013) Synonymous site variation due to recombination explains higher genetic variability in begomovirus populations infecting non-cultivated hosts. J Gen Virol 94:418–431CrossRefPubMedGoogle Scholar
  16. Mansoor S, Briddon RW, Zafar Y, Stanley J (2003) Geminivirus disease complexes: an emerging threat. Trends Plant Sci 8:128–134CrossRefPubMedGoogle Scholar
  17. Marwal A, Sahu AK, Choudhary DK, Gaur RK (2013a) Complete nucleotide sequence of a begomovirus associated with satellites molecules infecting a new host Tagetes patula in India. Virus Genes 47(1):194–198CrossRefPubMedGoogle Scholar
  18. Marwal A, Sahu A, Sharma P, Gaur RK (2013b) Molecular characterizations of two Begomoviruses infecting Vinca rosea and Raphanus sativus in India. Virol Sin 28(1):053–056CrossRefGoogle Scholar
  19. Marwal A, Sahu A, Gaur RK (2013c) First report of airborne begomovirus infection in Melia azedarach (Pride of India), an ornamental tree in India. Aerobiologia. doi: 10.1007/s10453-013-9319-x
  20. Marwal A, Sahu A, Gaur RK (2013d) Molecular characterization of begomoviruses and DNA satellites associated with a new host Spanish Flag (Lantana camara) in India. ISRN Virol. doi: 10.5402/2013/915703
  21. Perez A, Morrone JA, Simmerling C, Dill KA (2016) Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struct Biol 36:25–31CrossRefPubMedPubMedCentralGoogle Scholar
  22. Pingoud A, Jeltsch A (2001) Structure and function of type II restriction endonucleases. Nucleic Acids Res 29:3705–3727CrossRefPubMedPubMedCentralGoogle Scholar
  23. Qin W, Zhao G, Carson M, Jia C, Lu H (2016) Knowledge-based three-body potential for transcription factor binding site prediction. IET Syst Biol 10(1):23–29CrossRefPubMedGoogle Scholar
  24. Raj SK, Khan MS, Snehi SK, Kumar S, Khan AA (2007) Natural occurrence of a Begomovirus on Dimorphotheca sinuate in India. Aust Plant Dis Notes 2:25–26CrossRefGoogle Scholar
  25. Roberts RJ, Vincze T, Posfai J, Macelis (2003) REBASE—restriction enzymes and methyltransferases. Nucleic Acids Res 31:418–420CrossRefPubMedPubMedCentralGoogle Scholar
  26. Russell RB, Alber F, Aloy P, Davis FP, Korkin D, Pichaud M, Topf M, Sali A (2004) A structural perspective on protein-protein interactions. Curr Opin Struct Biol 14:313–324CrossRefPubMedGoogle Scholar
  27. Salwinski L, Eisenberg D (2003) Computational methods of analysis of protein-protein interactions. Curr Opin Struct Biol 13:377–382CrossRefPubMedGoogle Scholar
  28. Shin WH, Bures MG, Kihara D (2016) PatchSurfers: two methods for local molecular property-based binding ligand prediction. Methods 93:41–50CrossRefPubMedGoogle Scholar
  29. Szilagyi A, Grimm V, Arakaki AK, Skolnick J (2005) Prediction of physical protein-protein interactions. Phys Biol 2:S1–16CrossRefPubMedGoogle Scholar
  30. Taherzadeh G, Yang Y, Zhang LAWC, Zhou Y (2016) Sequence-based prediction of protein–peptide binding sites using support vector machine. J Comput Chem. doi: 10.1002/jcc.24314
  31. Urbino C, Gutiérrez S, Antolik A, Bouazza N, Doumayrou J, Granier M, Martin DP, Peterschmitt M (2013) Within-host dynamics of the emergence of tomato yellow leaf curl virus recombinants. PLoS One 8:e58375. doi: 10.1371/journal.pone.0058375 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Vincze T, Posfai J, Roberts RJ (2003) NEBcutter: a program to cleave DNA with restriction enzymes. Nucleic Acids Res 31:3688–3691CrossRefPubMedPubMedCentralGoogle Scholar
  33. Wass MN, Kelley LA, Sternberg MJE (2010) 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res 38:W469–W473CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Avinash Marwal
    • 1
  • Megha Mishra
    • 1
  • Charvee Sekhsaria
    • 1
  • R. K. Gaur
    • 1
  1. 1.Department of Biosciences, College of Arts, Science and HumanitiesMody UniversitySikarIndia

Personalised recommendations