Advertisement

Biocomputational Analysis and In Silico Characterization of an Angiogenic Protein (RNase5) in Zebrafish (Danio rerio)

  • Prasanta Patra
  • Pratik Ghosh
  • Bidhan Chandra Patra
  • Manojit BhattacharyaEmail author
Article
  • 28 Downloads

Abstract

Several types of RNase protein has been identified and characterized from different group of organism using advanced biocomputational techniques. Some of these RNase have crucial role for understanding and discovering the practical mechanism of cellular angiogenesis. To characterize the zebrafish (Danio rerio) RNase5 for its angiogenic properties multiple modern bioinformatics tools and server applied in present research. For accurate structural profiling of RNase5 protein through Ramachandran plot of PROCHECK and ProSA-web servers were applied. Subsequently, the CABS-flex server has been introduced to compute the Route Mean Square Fluctuation of all atoms for dynamic system simulation with a minimal residue fluctuation. Prediction of RNase5 protein’s sub-cellular localization, the TMHMM server also applied and confirmed its extracellular or secretory nature. Moreover, the sequence alignment with human angiogenin protein authenticates higher level of sequence similarity and reveals the conserved regions within protein of interest. Additionally, molecular docking with very low ACE value − 131.67 between the VEGF-binding domain of FLT-1 protein and zebrafish RNase5 able to generate the effective confirmation of cellular angiogenic activity. The molecular dynamic simulation by Normal Mode Analysis (NMA) also performed for functional mobility and structural stability of docking complex. Therefore, such In silico efforts definitely support the proper understanding of RNase5 enzyme mediated angiogenic activity. This effort certainly provides like a potential aid towards the future study of angiogenesis mechanism, as an important phenomenon of cancer metastasis in vertebrate model.

Keywords

In silico Angiogenesis RNase5 Zebrafish 

Abbreviations

RNA

Ribonucleic acid

VEGF

Vascular endothelial growth factor

ALS

Amyotropic lateral sclerosis

3D

Three dimensional

UCSF

University of California, San Francisco

PDB

Protein data bank

RMSF

Root mean square fluctuation

MD

Molecular dynamics

T-COFFEE

Tree-based consistency objective function for alignment evaluation

ACE

Atomic contact energy

MSA

Multiple sequence alignment

Notes

Acknowledgements

This research work is supported by research project of Science for Equity Empowerment and Development (SEED), Department of Science and Technology (DST), Govt. of India (Grant No. SEED/WTP/059/2014/G).

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research Involving Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Adams SA, Subramanian V (1999) The angiogenins: an emerging family of ribonuclease related proteins with diverse cellular functions. Angiogenesis 3:189–199PubMedCrossRefGoogle Scholar
  2. Baú D, Martin AJ, Mooney C, Vullo A, Walsh I, Pollastri G (2006) Distill: a suite of web servers for the prediction of one-, two-and three-dimensional structural features of proteins. BMC Bioinform 7:402CrossRefGoogle Scholar
  3. Bauer JA, Pavlović J, Bauerová-Hlinková V (2019) Normal mode analysis as a routine part of a structural investigation. Molecules 24:3293PubMedCentralCrossRefPubMedGoogle Scholar
  4. Berman HM et al (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedPubMedCentralCrossRefGoogle Scholar
  5. Bhattacharya M, Malick RC, Mondal N, Patra P, Pal BB, Patra BC, Das BK (2019) Computational characterization of epitopic region within the outer membrane protein candidate in Flavobacterium columnare for vaccine development. J Biomol Struct Dyn.  https://doi.org/10.1080/07391102.2019.1580222 CrossRefPubMedGoogle Scholar
  6. Carmeliet P, Jain RK (2000) Angiogenesis in cancer and other diseases. Nature 407:249PubMedCrossRefGoogle Scholar
  7. Chen Y et al (2005) SPD—a web-based secreted protein database. Nucleic Acids Res 33:D169–D173PubMedCrossRefGoogle Scholar
  8. Childers MC, Daggett V (2017) Insights from molecular dynamics simulations for computational protein design. Mol Syst Des Eng 2:9–33PubMedPubMedCentralCrossRefGoogle Scholar
  9. Chou K-C, Shen H-B (2006) Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347:150–157PubMedCrossRefGoogle Scholar
  10. Consortium U (2014) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  11. D'Alessio G (1993) New and cryptic biological messages from RNases. Trends Cell Biol 3:106–109PubMedCrossRefGoogle Scholar
  12. Davis RH (2004) The age of model organisms. Nat Rev Genet 5:69PubMedCrossRefGoogle Scholar
  13. Di Tommaso P et al (2011) T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res 39:W13–W17PubMedPubMedCentralCrossRefGoogle Scholar
  14. Duhovny D, Nussinov R, Wolfson HJ (2002) Efficient unbound docking of rigid molecules. In: International workshop on algorithms in bioinformatics. Springer, Berlin, pp 185–200Google Scholar
  15. Edgar RC, Batzoglou S (2006) Multiple sequence alignment. Curr Opin Struct Biol 16:368–373PubMedCrossRefGoogle Scholar
  16. Folkman J (1971) Tumor angiogenesis: therapeutic implications. New Engl J Med 285:1182–1186PubMedCrossRefGoogle Scholar
  17. Folkman J (1984) Angiogenesis. In: Jaffe EA (ed) Biology of endothelial cells. Springer, Boston, pp 412–428CrossRefGoogle Scholar
  18. Gibbs JB (2000) Mechanism-based target identification and drug discovery in cancer research. Science 287:1969–1973PubMedCrossRefGoogle Scholar
  19. Hasan M et al (2019) Vaccinomics strategy for developing a unique multi-epitope monovalent vaccine against Marburg marburgvirus. Infect Genet Evol 70:140–157PubMedCrossRefGoogle Scholar
  20. Horton P, Park K-J, Obayashi T, Fujita N, Harada H, Adams-Collier C, Nakai K (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Res 35:W585–W587PubMedPubMedCentralCrossRefGoogle Scholar
  21. Huh W-K, Falvo JV, Gerke LC, Carroll AS, Howson RW, Weissman JS, O'shea EK (2003) Global analysis of protein localization in budding yeast. Nature 425:686PubMedCrossRefGoogle Scholar
  22. Jamroz M, Kolinski A, Kmiecik S (2013) CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res 41:W427–W431PubMedPubMedCentralCrossRefGoogle Scholar
  23. Jamroz M, Kolinski A, Kmiecik S (2014) CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics 30:2150–2154PubMedPubMedCentralCrossRefGoogle Scholar
  24. Karplus M, Petsko GA (1990) Molecular dynamics simulations in biology. Nature 347:631PubMedCrossRefGoogle Scholar
  25. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32:W526–W531PubMedPubMedCentralCrossRefGoogle Scholar
  26. Kishikawa H, Wu D, Hu G-f (2008) Targeting angiogenin in therapy of amyotropic lateral sclerosis. Expert Opin Ther Targets 12:1229–1242PubMedPubMedCentralCrossRefGoogle Scholar
  27. Kleywegt GJ, Jones TA (1996) Phi/psi-chology: Ramachandran revisited. Structure 4:1395–1400PubMedCrossRefGoogle Scholar
  28. Krogh A, Larsson B, Von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580PubMedCrossRefGoogle Scholar
  29. Kuriata A, Gierut AM, Oleniecki T, Ciemny MP, Kolinski A, Kurcinski M, Kmiecik S (2018a) CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res.  https://doi.org/10.1093/nar/gky356 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kuriata A, Kolinski A, Kurcinski M, Kmiecik S, Oleniecki T, Ciemny MP (2018b) CABS-flex standalone: a simulation environment for fast modeling of protein flexibility. Bioinformatics.  https://doi.org/10.1093/bioinformatics/bty685 CrossRefPubMedCentralPubMedGoogle Scholar
  31. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291CrossRefGoogle Scholar
  32. Laskowski RA, MacArthur MW, Thornton JM (1998) Validation of protein models derived from experiment. Curr Opin Struct Biol 8:631–639PubMedCrossRefGoogle Scholar
  33. Lassmann T, Sonnhammer EL (2005) Automatic assessment of alignment quality. Nucleic Acids Res 33:7120–7128PubMedPubMedCentralCrossRefGoogle Scholar
  34. Lavi A et al (2013) Detection of peptide-binding sites on protein surfaces: the first step toward the modeling and targeting of peptide-mediated interactions. Proteins: Struct Funct Bioinform 81:2096–2105CrossRefGoogle Scholar
  35. Li H, Chang Y-Y, Lee JY, Bahar I, Yang L-W (2017) DynOmics: dynamics of structural proteome and beyond. Nucleic Acids Res 45:W374–W380PubMedPubMedCentralGoogle Scholar
  36. López-Blanco JR, Aliaga JI, Quintana-Ortí ES, Chacón P (2014) iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res 42:W271–W276PubMedPubMedCentralCrossRefGoogle Scholar
  37. Meyers JR (2018) Zebrafish: development of a vertebrate model organism. Curr Protoc Essential Lab Tech 16:e19CrossRefGoogle Scholar
  38. Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302:205–217PubMedCrossRefGoogle Scholar
  39. Nussey SS, Whitehead SA (2013) Endocrinology: an integrated approach. CRC Press, Boca RatonGoogle Scholar
  40. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  41. Pizzo E, D'Alessio G (2007) The success of the RNase scaffold in the advance of biosciences and in evolution. Gene 406:8–12PubMedCrossRefGoogle Scholar
  42. Pizzo E et al (2011) A new RNase sheds light on the RNase/angiogenin subfamily from zebrafish. Biochem J 433:345–355PubMedCrossRefGoogle Scholar
  43. Raghavan V (2012) Developmental biology of flowering plants. Springer, New YorkGoogle Scholar
  44. Ramanathan K, Shanthi V, Sethumadhavan R (2009) In silico identification of catalytic residues in azobenzene reductase from Bacillus subtilis and its docking studies with azo dyes. Interdiscip Sci: Comput Life Sci 1:290–297CrossRefGoogle Scholar
  45. Ribas L, Piferrer F (2014) The zebrafish (Danio rerio) as a model organism, with emphasis on applications for finfish aquaculture research. Rev Aquac 6:209–240CrossRefGoogle Scholar
  46. Schneidman-Duhovny D et al (2003) Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking. Proteins: Struct Funct Bioinform 52:107–112CrossRefGoogle Scholar
  47. Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367PubMedPubMedCentralCrossRefGoogle Scholar
  48. Siegel RL, Miller KD, Jemal A (2015) Cancer statistics, 2015. CA Cancer J Clin 65:5–29PubMedCrossRefGoogle Scholar
  49. Starovasnik MA, Christinger HW, Wiesmann C, Champe MA, de Vos AM, Skelton NJ (1999) Solution structure of the VEGF-binding domain of Flt-1: comparison of its free and bound states. J Mol Biol 293:531–544PubMedCrossRefGoogle Scholar
  50. Steidinger TU, Standaert DG, Yacoubian TA (2011) A neuroprotective role for angiogenin in models of Parkinson’s disease. J Neurochem 116:334–341PubMedCrossRefGoogle Scholar
  51. Strydom D (1998) The angiogenins. Cell Mol Life Sci CMLS 54:811–824PubMedCrossRefGoogle Scholar
  52. Su EC-Y, Chiu H-S, Lo A, Hwang J-K, Sung T-Y, Hsu W-L (2007) Protein subcellular localization prediction based on compartment-specific features and structure conservation. BMC Bioinform 8:330CrossRefGoogle Scholar
  53. Waltemath D et al (2011) Reproducible computational biology experiments with SED-ML-the simulation experiment description markup language. BMC Syst Biol 5:198PubMedPubMedCentralCrossRefGoogle Scholar
  54. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410PubMedPubMedCentralCrossRefGoogle Scholar
  55. Wiesmann C, Fuh G, Christinger HW, Eigenbrot C, Wells JA, de Vos AM (1997) Crystal structure at 1.7 Å resolution of VEGF in complex with domain 2 of the Flt-1 receptor. Cell 91:695–704PubMedCrossRefGoogle Scholar
  56. Yu CS, Chen YC, Lu CH, Hwang JK (2006) Prediction of protein subcellular localization. Proteins: Struct Funct Bioinform 64:643–651CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of ZoologyVidyasagar UniversityMidnaporeIndia
  2. 2.Centre For Aquaculture Research, Extension & Livelihood, Department of Aquaculture Management & TechnologyVidyasagar UniversityMidnaporeIndia

Personalised recommendations