Skip to content
2000
image of Comparison of Structural Stability of Monomeric and Dimeric Forms of Bovine Seminal Ribonuclease Using Molecular Dynamics Simulation

Abstract

Background

Bovine Seminal Ribonuclease (BS-RNase) is a unique member of the RNase A family found in the bovine seminal fluid. It is recognized for its antiviral and antitumor properties, which make it a potential therapeutic agent.

Objective

This study aimed to compare the stability of monomeric and dimeric forms of BS-RNase using methods.

Method

The tertiary structures of BS-RNase as monomers and dimers were obtained from the Protein Data Bank, and missing amino acids were modeled using the Modeller server. The predicted structures were validated using SAVES 6 and ProSA web tools. Molecular dynamics simulations were performed using GROMACS, and the resulting RMSD, RMSF, and Rg plots were analyzed.

Results

The results indicated that the monomer's ERRAT score, Ramachandran plot, and Z-score were better than the dimer's. RMSD, RMSF, and Rg plots were favorable for both structures, with the monomer showing better stability than the dimer.

Conclusion

Consequently, the monomeric form of BS-RNase is more stable than its dimeric form, and the monomer can be more reliably used in pharmaceutical studies.

Loading

Article metrics loading...

/content/journals/cp/10.2174/0115701646347460250101115029
2025-01-16
2025-04-14
Loading full text...

Full text loading...

References

  1. Fry D.C. Vassilev L.T. Targeting protein–protein interactions for cancer therapy. J. Mol. Med. 2005 83 12 955 963 10.1007/s00109‑005‑0705‑x 16283145
    [Google Scholar]
  2. Berndt N. Hamilton A.D. Sebti S.M. Targeting protein prenylation for cancer therapy. Nat. Rev. Cancer 2011 11 11 775 791 10.1038/nrc3151 22020205
    [Google Scholar]
  3. Adjei A.A. Hidalgo M. Intracellular signal transduction pathway proteins as targets for cancer therapy. J. Clin. Oncol. 2005 23 23 5386 5403 10.1200/JCO.2005.23.648 15983388
    [Google Scholar]
  4. Teixeira F.J.N. The effects of leucine metabolites on performance, body composition and biochemical markers of muscle damage and inflammation. Universidade de Lisboa Portugal 2019
    [Google Scholar]
  5. Castro J. Ribó M. Vilanova M. Benito A. Strengths and challenges of secretory ribonucleases as antitumor agents. Pharmaceutics 2021 13 1 82 10.3390/pharmaceutics13010082 33435285
    [Google Scholar]
  6. Ariannejhad H. Nassiry M.R. Javadmanesh A. Ghovvati S. Dehghani H. Asoodeh A. Designing of protein structural of Ranpirnase based on bovine pancreatic ribonuclease with using molecular dynamic and static simulation. Iranian Journal of Animal Science Research 2020 12 3 351 360
    [Google Scholar]
  7. Ardelt W. Ardelt B. Darzynkiewicz Z. Ribonucleases as potential modalities in anticancer therapy. Eur. J. Pharmacol. 2009 625 1-3 181 189 10.1016/j.ejphar.2009.06.067 19825371
    [Google Scholar]
  8. Shlyakhovenko V. Possible new approach in cancer therapy. Exp. Oncol. 2016 38 1 2 8
    [Google Scholar]
  9. Schirrmann T. Krauss J. Arndt M.A.E. Rybak S.M. Dübel S. Targeted therapeutic rnases (immunornases). Expert Opin. Biol. Ther. 2009 9 1 79 95 10.1517/14712590802631862 19063695
    [Google Scholar]
  10. Ribó M. Benito A. Vilanova M. Ribonucleases. Springer Berlin, Heidelberg. 2011 55 88 10.1007/978‑3‑642‑21078‑5_3
    [Google Scholar]
  11. Rybak S. Arndt M. Schirrmann T. Dübel S. Krauss J. Ribonucleases and immunoRNases as anticancer drugs. Curr. Pharm. Des. 2009 15 23 2665 2675 10.2174/138161209788923921 19689337
    [Google Scholar]
  12. Vakili-Azghandi M. Nassiri M. Ghovvati S. Javadmanesh A. Ribonucleases as potential therapeutic agents. Agricultural Biotechnology Journal 2021 13 1 29 56
    [Google Scholar]
  13. Arraiano C.M. Andrade J.M. Domingues S. Guinote I.B. Malecki M. Matos R.G. Moreira R.N. Pobre V. Reis F.P. Saramago M. Silva I.J. Viegas S.C. The critical role of RNA processing and degradation in the control of gene expression. FEMS Microbiol. Rev. 2010 34 5 883 923 10.1111/j.1574‑6976.2010.00242.x 20659169
    [Google Scholar]
  14. Goo S.M. Cho S. The expansion and functional diversification of the mammalian ribonuclease a superfamily epitomizes the efficiency of multigene families at generating biological novelty. Genome Biol. Evol. 2013 5 11 2124 2140 10.1093/gbe/evt161 24162010
    [Google Scholar]
  15. Lu L. Li J. Moussaoui M. Boix E. Immune modulation by human secreted RNases at the extracellular space. Front. Immunol. 2018 9 1012 10.3389/fimmu.2018.01012 29867984
    [Google Scholar]
  16. Cuchillo C.M. Nogués M.V. Raines R.T. Bovine pancreatic ribonuclease: Fifty years of the first enzymatic reaction mechanism. Biochemistry 2011 50 37 7835 7841 10.1021/bi201075b 21838247
    [Google Scholar]
  17. Matousek J. Soucek J. Slavík T. Tománek M. Lee J.E. Raines R.T. Comprehensive comparison of the cytotoxic activities of onconase and bovine seminal ribonuclease. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2003 136 4 343 356 10.1016/j.cca.2003.10.005 15012906
    [Google Scholar]
  18. Chao T.Y. Raines R.T. Mechanism of ribonuclease A endocytosis: Analogies to cell-penetrating peptides. Biochemistry 2011 50 39 8374 8382 10.1021/bi2009079 21827164
    [Google Scholar]
  19. Kanwar S. Mishra P. Meena K.R. Gupta S. Kumar R. Ribonucleases and their applications. J Adv Biotechnol Bioeng. 2016 4 1 17 26 10.12970/2311‑1755.2016.04.01.3
    [Google Scholar]
  20. Gupta S.K. Shukla P. Sophisticated cloning, fermentation, and purification technologies for an enhanced therapeutic protein production: A review. Front. Pharmacol. 2017 8 419 10.3389/fphar.2017.00419 28725194
    [Google Scholar]
  21. D’Alessio G. Di Donato A. Mazzarella L. Piccoli R. Seminal ribonuclease: The importance of diversity. Ribonucleases. Elsevier 1997 383 VII 10.1016/B978‑012588945‑2/50013‑3
    [Google Scholar]
  22. D’Alessio G. Evolution of oligomeric proteins. Eur. J. Biochem. 1999 266 3 699 708 10.1046/j.1432‑1327.1999.00912.x 10583363
    [Google Scholar]
  23. Gotte G. Menegazzi M. Biological activities of secretory RNases: Focus on their oligomerization to design antitumor drugs. Front. Immunol. 2019 10 2626 10.3389/fimmu.2019.02626 31849926
    [Google Scholar]
  24. Doscher M.S. Hirs C.H.W. The heterogeneity of bovine pancreatic ribonuclease S. Biochemistry 1967 6 1 304 312 10.1021/bi00853a047 6030327
    [Google Scholar]
  25. Spadaccini R. Ercole C. Gentile M.A. Sanfelice D. Boelens R. Wechselberger R. Batta G. Bernini A. Niccolai N. Picone D. NMR studies on structure and dynamics of the monomeric derivative of BS-RNase: new insights for 3D domain swapping. PLoS One 2012 7 1 e29076 10.1371/journal.pone.0029076 22253705
    [Google Scholar]
  26. Gotte G. Mahmoud Helmy A. Ercole C. Spadaccini R. Laurents D. V. Donadelli M. Picone D. Double domain swapping in bovine seminal RNase: Formation of distinct N- and C-swapped tetramers and multimers with increasing biological activities. PLoS ONE 2012 7 10 e46804 10.1371/journal.pone.0046804
    [Google Scholar]
  27. Kim J.S. Souček J. Matoušek J. Raines R.T. Structural basis for the biological activities of bovine seminal ribonuclease. J. Biol. Chem. 1995 270 18 10525 10530 10.1074/jbc.270.18.10525 7737987
    [Google Scholar]
  28. Gotte G. Laurents D.V. Merlino A. Picone D. Spadaccini R. Structural and functional relationships of natural and artificial dimeric bovine ribonucleases: New scaffolds for potential antitumor drugs. FEBS Lett. 2013 587 22 3601 3608 10.1016/j.febslet.2013.09.038 24113657
    [Google Scholar]
  29. Simons B.L. Kaplan H. Fournier S.M. Cyr T. Hefford M.A. A novel cross‐linked RNase A dimer with enhanced enzymatic properties. Proteins 2007 66 1 183 195 10.1002/prot.21144 17044066
    [Google Scholar]
  30. Lee J.E. Raines R.T. Cytotoxicity of bovine seminal ribonuclease: Monomer versus dimer. Biochemistry 2005 44 48 15760 15767 10.1021/bi051668z 16313179
    [Google Scholar]
  31. Adrio J. Demain A. Microbial enzymes: Tools for biotechnological processes. Biomolecules 2014 4 1 117 139 10.3390/biom4010117 24970208
    [Google Scholar]
  32. Wu H. Lima W.F. Crooke S.T. Properties of cloned and expressed human RNase H1. J. Biol. Chem. 1999 274 40 28270 28278 10.1074/jbc.274.40.28270 10497183
    [Google Scholar]
  33. Francis D. M. Page R. Strategies to optimize protein expression in E. coli. Curr Protoc Protein Sci. 2010 Chapter 5 1 5.24.1 5.24.29
    [Google Scholar]
  34. Hammadi A.A. Al-Mousawi M.R. Cloning of DNA: A review. Sci. J. Med. Res. 2021 5 20 130 134
    [Google Scholar]
  35. Suhaibun S. R. Elengoe A. Poddar R. Technology advance in drug design using computational biology tool. Mal J Med Health Sci 2020 16 (SUPP10) 18 24
    [Google Scholar]
  36. Varfolomeev S.D. Uporov I.V. Fedorov E.V. Bioinformatics and molecular modeling in chemical enzymology. Active sites of hydrolases. Biochemistry 2002 67 10 1099 1108 10.1023/A:1020907122341 12460108
    [Google Scholar]
  37. Yadav K. Rani V. Anand G. Yadava U. Yadav D. Bioinformatic-driven research in microbial enzymes: An overview Wiley 2025 2 739 759
    [Google Scholar]
  38. Massova I. Kollman P.A. Computational alanine scanning to probe protein− protein interactions: A novel approach to evaluate binding free energies. J. Am. Chem. Soc. 1999 121 36 8133 8143 10.1021/ja990935j
    [Google Scholar]
  39. Naqvi A.A.T. Mohammad T. Hasan G.M. Hassan M.I. Advancements in docking and molecular dynamics simulations towards ligand-receptor interactions and structure-function relationships. Curr. Top. Med. Chem. 2018 18 20 1755 1768 10.2174/1568026618666181025114157 30360721
    [Google Scholar]
  40. Edwards Y.J. Cottage A. Prediction of protein structure and function by using bioinformatics. Genomics Protocols 2001 341 375 10.1385/1‑59259‑235‑X:341
    [Google Scholar]
  41. Sánchez R. Šali A. Evaluation of comparative protein structure modeling by MODELLER-3. Proteins 1997 29 S1 Suppl. 1 50 58 10.1002/(SICI)1097‑0134(1997)1+<50::AID‑PROT8>3.0.CO;2‑S 9485495
    [Google Scholar]
  42. Edwards Y.J.K. Cottage A. Bioinformatics methods to predict protein structure and function. A practical approach. Mol. Biotechnol. 2003 23 2 139 166 10.1385/MB:23:2:139 12632698
    [Google Scholar]
  43. Webb B. Sali A. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2006 Chapter 5 Unit-5.6
    [Google Scholar]
  44. Odhar H.A. Hashim A.F. Ahjel S.W. Humadi S.S. Molecular docking and dynamics simulation analysis of the human FXIIa with compounds from the Mcule database. Bioinformation 2023 19 2 160 166 10.6026/97320630019160 37814681
    [Google Scholar]
  45. Altunkulah E. Ensari̇ Y. Protein structure prediction: An in-depth comparison of approaches and tools. Eskişehir Tech. Univ. Sci. Technol. J. C Life Sci. Biotechnol. 2024 13 1 31 51 10.18036/estubtdc.1378676
    [Google Scholar]
  46. Paiva V.A. Gomes I.S. Monteiro C.R. Mendonça M.V. Martins P.M. Santana C.A. Gonçalves-Almeida V. Izidoro S.C. Melo-Minardi R.C. Silveira S.A. Protein structural bioinformatics: An overview. Comput. Biol. Med. 2022 147 105695 10.1016/j.compbiomed.2022.105695 35785665
    [Google Scholar]
  47. Agnihotry S. Pathak R.K. Singh D.B. Tiwari A. Hussain I. Protein structure prediction. Bioinformatics. Elsevier 2022 177 188 10.1016/B978‑0‑323‑89775‑4.00023‑7
    [Google Scholar]
  48. Farhadi T. Advances in protein tertiary structure prediction. Biomed. Biotechnol. Res. J. 2018 2 1 20 25 10.4103/bbrj.bbrj_94_17
    [Google Scholar]
  49. Xu D. Xu Y. Uberbacher E. Computational tools for protein modeling. Curr. Protein Pept. Sci. 2000 1 1 1 21 10.2174/1389203003381469 12369918
    [Google Scholar]
  50. Hornak V. Abel R. Okur A. Strockbine B. Roitberg A. Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006 65 3 712 725 10.1002/prot.21123 16981200
    [Google Scholar]
  51. Ouyang Y. Zhao L. Zhang Z. Characterization of the structural ensembles of p53 TAD2 by molecular dynamics simulations with different force fields. Phys. Chem. Chem. Phys. 2018 20 13 8676 8684 10.1039/C8CP00067K 29537020
    [Google Scholar]
  52. Somavarapu A.K. Kepp K.P. The dependence of amyloid‐β dynamics on protein force fields and water models. ChemPhysChem 2015 16 15 3278 3289 10.1002/cphc.201500415 26256268
    [Google Scholar]
  53. Maier J.A. Martinez C. Kasavajhala K. Wickstrom L. Hauser K.E. Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 2015 11 8 3696 3713 10.1021/acs.jctc.5b00255 26574453
    [Google Scholar]
  54. de Azevedo W.F. Jr Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem. 2011 18 9 1353 1366 10.2174/092986711795029519 21366529
    [Google Scholar]
  55. Lindahl E. R. Molecular dynamics simulations. Methods Mol Biol. 2008 443 3 23
    [Google Scholar]
  56. Gharouni M. Mosaddeghi H. Mehrzad J. Es-haghi A. Motavalizadehkakhky A. Detecting a novel motif of O6-methyl guanine DNA methyltransferase, a DNA repair enzyme, involved in interaction with proliferating cell nuclear antigen through a computer modeling approach. Comput. Theor. Chem. 2021 1206 113471 10.1016/j.comptc.2021.113471
    [Google Scholar]
  57. Lindorff-Larsen K. Piana S. Palmo K. Maragakis P. Klepeis J.L. Dror R.O. Shaw D.E. Improved side‐chain torsion potentials for the Amber ff99SB protein force field. Proteins 2010 78 8 1950 1958 10.1002/prot.22711 20408171
    [Google Scholar]
  58. Hernández-Rodríguez M. Rosales-Hernández M.C. Mendieta-Wejebe J.E. Martínez-Archundia M. Basurto J.C. Current tools and methods in molecular dynamics (MD) simulations for drug design. Curr. Med. Chem. 2016 23 34 3909 3924 10.2174/0929867323666160530144742 27237821
    [Google Scholar]
  59. Astuti A. Mutiara A. Performance analysis on molecular dynamics simulation of protein using GROMACS. arXiv:0912.0893 2009
    [Google Scholar]
  60. Beg M.A. Shivangi Thakur S.C. Meena L.S. Structural prediction and mutational analysis of Rv3906c gene of Mycobacterium tuberculosis H37Rv to determine its essentiality in survival. Adv. Bioinforma. 2018 2018 1 6152014 30186322
    [Google Scholar]
  61. Nemaysh V. Luthra P.M. Computational analysis revealing that K634 and T681 mutations modulate the 3D-structure of PDGFR-β and lead to sunitinib resistance. RSC Advances 2017 7 60 37612 37626 10.1039/C7RA01305A
    [Google Scholar]
  62. Gupta C.L. Akhtar S. Bajpaib P. Kandpal K.N. Desai G.S. Tiwari A.K. Computational modeling and validation studies of 3-D structure of neuraminidase protein of H1N1 influenza A virus and subsequent in silico elucidation of piceid analogues as its potent inhibitors. EXCLI J. 2013 12 215 225 26417228
    [Google Scholar]
  63. Wiederstein M. Sippl M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007 35 Web Server Suppl. 2 W407 W410 10.1093/nar/gkm290 17517781
    [Google Scholar]
  64. Mousavi Z. Sekhavati M.H. Farzaneh M. Javadmanesh A. Investigating of the binding energy of DEP-A and DEP-B enzymes with DON mycotoxin chemotype by molecular docking. Vet. Res. Biol. Prod. 2024 37 1 15 26
    [Google Scholar]
  65. De Laet M. Gilis D. Rooman M. Stability strengths and weaknesses in protein structures detected by statistical potentials: Application to bovine seminal ribonuclease. Proteins 2016 84 1 143 158 10.1002/prot.24962 26573727
    [Google Scholar]
  66. Parés X. Nogués M.V. de Llorens R. Cuchillo C.M. Structure and function of ribonuclease A binding subsites. Essays Biochem. 1991 26 89 103 1778187
    [Google Scholar]
  67. Taghizadegan N. Firozrai M. Nassiri M. Ariannejad H. Use of molecular dynamic tools in engineering of onconase enzyme to increase cellular uptake and evade RI. Int. J. Pept. Res. Ther. 2020 26 2 737 743 10.1007/s10989‑019‑09881‑9
    [Google Scholar]
  68. Nassiri M. Ghovvati S. Gharouni M. Tahmoorespur M. Bahrami A.R. Dehghani H. Engineering human pancreatic RNase 1 as an immunotherapeutic agent for cancer therapy through computational and experimental studies. Protein J. 2024 43 2 316 332 10.1007/s10930‑023‑10171‑z 38145445
    [Google Scholar]
  69. Sánchez I.E. Tejero J. Gómez-Moreno C. Medina M. Serrano L. Point mutations in protein globular domains: Contributions from function, stability and misfolding. J. Mol. Biol. 2006 363 2 422 432 10.1016/j.jmb.2006.08.020 16978645
    [Google Scholar]
  70. Tsou C-L. Active site flexibility in enzyme catalysis a. Ann. N. Y. Acad. Sci. 1998 864 1 1 8 10.1111/j.1749‑6632.1998.tb10282.x
    [Google Scholar]
  71. Shukla R. Tripathi T. Molecular dynamics simulation of protein and protein–ligand complexes. Computer-Aided Drug Design Springer Singapore 2020 133 161
    [Google Scholar]
/content/journals/cp/10.2174/0115701646347460250101115029
Loading
/content/journals/cp/10.2174/0115701646347460250101115029
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test