In silico assessment of human Calprotectin subunits (S100A8/A9) in presence of sodium and calcium ions using Molecular Dynamics simulation approach


Autoři: Nematollah Gheibi aff001;  Mohammad Ghorbani aff002;  Hanifeh Shariatifar aff003;  Alireza Farasat aff001
Působiště autorů: Cellular and Molecular Research Center, Qazvin University of Medical Sciences, Qazvin, Iran aff001;  Department of Nanobiotechnology/ Biophysics, Faculty of Biological Science, Tarbiat Modares University, Tehran, Iran aff002;  Young Researchers and Elite Club, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224095

Souhrn

Calprotectin is a heterodimeric protein complex which consists of two subunits including S100A8 and S100A9. This protein has a major role in different inflammatory disease and various types of cancers. In current study we aimed to evaluate the structural and thermodynamic changes of the subunits and the complex in presence of sodium and calcium ions using molecular dynamics (MD) simulation. Therefore, the residue interaction network (RIN) was visualized in Cytoscape program. In next step, to measure the binding free energy, the potential of mean force (PMF) method was performed. Finally, the molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method was applied as an effective tool to calculate the molecular model affinities. The MD simulation results of the subunits represented their structural changes in presence of Ca2+. Moreover, the RIN and Hydrogen bond analysis demonstrated that cluster interactions between Calprotectin subunits in presence of Ca2+ were greater in comparison with Na+. Our findings indicated that the binding free energy of the subunits in presence of Ca2+ was significantly greater than Na+. The results revealed that Ca2+ has the ability to induce structural changes in subunits in comparison with Na+ which lead to create stronger interactions between. Hence, studying the physical characteristics of the human proteins could be considered as a powerful tool in theranostics and drug design purposes.

Klíčová slova:

Biochemical simulations – Free energy – Interaction networks – Molecular dynamics – Protein interactions – Protein structure – Simulation and modeling – Sodium


Zdroje

1. Leukert N, Sorg C, Roth J. Molecular basis of the complex formation between the two calcium-binding proteins S100A8 (MRP8) and S100A9 (MRP14). Biological chemistry. 2005;386(5):429–34. doi: 10.1515/BC.2005.051 15927886

2. Shirley SH, von Maltzan K, Robbins PO, Kusewitt DF. Melanocyte and melanoma cell activation by calprotectin. Journal of skin cancer. 2014;2014.

3. Vogl T, Gharibyan AL, Morozova-Roche LA. Pro-inflammatory S100A8 and S100A9 proteins: self-assembly into multifunctional native and amyloid complexes. International journal of molecular sciences. 2012;13(3):2893–917. doi: 10.3390/ijms13032893 22489132

4. Reis RAG, Bortot LO, Caliri A. In silico assessment of S100A12 monomer and dimer structural dynamics: implications for the understanding of its metal-induced conformational changes. JBIC Journal of Biological Inorganic Chemistry. 2014;19(7):1113–20. doi: 10.1007/s00775-014-1149-y 24944024

5. Markowitz J, Carson WE III. Review of S100A9 biology and its role in cancer. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 2013;1835(1):100–9.

6. Brunjes Brophy M, Nakashige TG, Gaillard A, Nolan EM. Contributions of the S100A9 C-terminal tail to high-affinity Mn (II) chelation by the host-defense protein human calprotectin. Journal of the American Chemical Society. 2013;135(47):17804–17. doi: 10.1021/ja407147d 24245608

7. Santamaria-Kisiel L, Rintala-Dempsey AC, Shaw GS. Calcium-dependent and-independent interactions of the S100 protein family. Biochemical Journal. 2006;396(2):201–14. doi: 10.1042/BJ20060195 16683912

8. Gheibi N, Asghari H, Chegini K, Sahmani M, Moghadasi M. The role of calcium in the conformational changes of the recombinant S100A8/S100A91. Molecular Biology. 2016;50(1):118–23.

9. Korndörfer IP, Brueckner F, Skerra A. The crystal structure of the human (S100A8/S100A9) 2 heterotetramer, calprotectin, illustrates how conformational changes of interacting α-helices can determine specific association of two EF-hand proteins. Journal of molecular biology. 2007;370(5):887–98. doi: 10.1016/j.jmb.2007.04.065 17553524

10. Ichikawa M, Williams R, Wang L, Vogl T, Srikrishna G. S100A8/A9 activate key genes and pathways in colon tumor progression. Molecular cancer research. 2011;9(2):133–48. doi: 10.1158/1541-7786.MCR-10-0394 21228116

11. Gomes LH, Raftery MJ, Yan WX, Goyette JD, Thomas PS, Geczy CL. S100A8 and S100A9—oxidant scavengers in inflammation. Free Radical Biology and Medicine. 2013;58:170–86. doi: 10.1016/j.freeradbiomed.2012.12.012 23277148

12. Leclerc E, Fritz G, Weibel M, Heizmann CW, Galichet A. S100B and S100A6 differentially modulate cell survival by interacting with distinct RAGE (receptor for advanced glycation end products) immunoglobulin domains. Journal of Biological Chemistry. 2007;282(43):31317–31. doi: 10.1074/jbc.M703951200 17726019

13. Ehrchen JM, Sunderkötter C, Foell D, Vogl T, Roth J. The endogenous Toll–like receptor 4 agonist S100A8/S100A9 (calprotectin) as innate amplifier of infection, autoimmunity, and cancer. Journal of leukocyte biology. 2009;86(3):557–66. doi: 10.1189/jlb.1008647 19451397

14. Hibino T, Sakaguchi M, Miyamoto S, Yamamoto M, Motoyama A, Hosoi J, et al. S100A9 is a novel ligand of EMMPRIN that promotes melanoma metastasis. Cancer research. 2013;73(1):172–83. doi: 10.1158/0008-5472.CAN-11-3843 23135911

15. Jung S-Y, Park Y-B, Ha Y-J, Lee K-H, Lee S-K. Serum calprotectin as a marker for disease activity and severity in adult-onset Still’s disease. The Journal of rheumatology. 2010;37(5):1029–34. doi: 10.3899/jrheum.091120 20231196

16. Shabani F, Farasat A, Mahdavi M, Gheibi N. Calprotectin (S100A8/S100A9): a key protein between inflammation and cancer. Inflammation Research. 2018;67(10):801–12. doi: 10.1007/s00011-018-1173-4 30083975

17. Chang C-C, Khan I, Tsai K-L, Li H, Yang L-W, Chou R-H, et al. Blocking the interaction between S100A9 and RAGE V domain using CHAPS molecule: A novel route to drug development against cell proliferation. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics. 2016;1864(11):1558–69.

18. Lin H, Andersen GR, Yatime L. Crystal structure of human S100A8 in complex with zinc and calcium. BMC structural biology. 2016;16(1):8. doi: 10.1186/s12900-016-0058-4 27251136

19. Batoulis H, Schmidt TH, Weber P, Schloetel J-G, Kandt C, Lang T. Concentration dependent ion-protein interaction patterns underlying protein oligomerization behaviours. Scientific reports. 2016;6:24131. doi: 10.1038/srep24131 27052788

20. Akya A, Farasat A, Ghadiri K, Rostamian M. Identification of HLA-I restricted epitopes in six vaccine candidates of Leishmania tropica using immunoinformatics and molecular dynamics simulation approaches. Infection, Genetics and Evolution. 2019;75:103953. doi: 10.1016/j.meegid.2019.103953 31284043

21. Farasat A, Rahbarizadeh F, Hosseinzadeh G, Sajjadi S, Kamali M, Keihan AH. Affinity enhancement of nanobody binding to EGFR: in silico site-directed mutagenesis and molecular dynamics simulation approaches. Journal of biomolecular structure and dynamics. 2017;35(8):1710–28. doi: 10.1080/07391102.2016.1192065 27691399

22. Ochoa R, Soler MA, Laio A, Cossio P. Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids. Physical Chemistry Chemical Physics. 2018;20(40):25901–9. doi: 10.1039/c8cp03826k 30289133

23. Piovesan D, Minervini G, Tosatto SC. The RING 2.0 web server for high quality residue interaction networks. Nucleic acids research. 2016;44(W1):W367–W74. doi: 10.1093/nar/gkw315 27198219

24. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498–504. doi: 10.1101/gr.1239303 14597658

25. Naughton FB, Kalli AC, Sansom MS. Modes of interaction of pleckstrin homology domains with membranes: toward a computational biochemistry of membrane recognition. Journal of molecular biology. 2018;430(3):372–88. doi: 10.1016/j.jmb.2017.12.011 29273202

26. Lemkul JA, Bevan DR. Assessing the stability of Alzheimer’s amyloid protofibrils using molecular dynamics. The Journal of Physical Chemistry B. 2010;114(4):1652–60. doi: 10.1021/jp9110794 20055378

27. Zeng S, Zhou G, Guo J, Zhou F, Chen J. Molecular simulations of conformation change and aggregation of HIV-1 Vpr13-33 on graphene oxide. Scientific reports. 2016;6:24906. doi: 10.1038/srep24906 27097898

28. Sulaiman KO, Kolapo TU, Onawole AT, Islam MA, Adegoke RO, Badmus SO. Molecular dynamics and combined docking studies for the identification of Zaire ebola virus inhibitors. Journal of Biomolecular Structure and Dynamics. 2019;37(12):3029–40. doi: 10.1080/07391102.2018.1506362 30058446

29. Wang C, Nguyen PH, Pham K, Huynh D, Le TBN, Wang H, et al. Calculating protein–ligand binding affinities with MMPBSA: Method and error analysis. Journal of computational chemistry. 2016;37(27):2436–46. doi: 10.1002/jcc.24467 27510546

30. Kumari R, Kumar R, Consortium OSDD, Lynn A. g_mmpbsa∙ A GROMACS tool for high-throughput MM-PBSA calculations. Journal of chemical information and modeling. 2014;54(7):1951–62. doi: 10.1021/ci500020m 24850022

31. Wang Z, Zhang Q, Meng F, Li S, Xu Q, Qi Z. Characterization of the ligand binding of PGRP-L in half-smooth tongue sole (Cynoglossus semilaevis) by molecular dynamics and free energy calculation. Electronic Journal of Biotechnology. 2018;31:93–9.

32. DeLano WL. The PyMOL molecular graphics system. http://wwwpymolorg. 2002.

33. Shen M, Guan J, Xu L, Yu Y, He J, Jones GW, et al. Steered molecular dynamics simulations on the binding of the appendant structure and helix-β2 in domain-swapped human cystatin C dimer. Journal of Biomolecular Structure and Dynamics. 2012;30(6):652–61. doi: 10.1080/07391102.2012.689698 22731964

34. Karain WI. Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method. BMC bioinformatics. 2017;18(1):525. doi: 10.1186/s12859-017-1943-y 29179670

35. Kaur G, Pandey B, Kumar A, Garewal N, Grover A, Kaur J. Drug targeted virtual screening and molecular dynamics of LipU protein of Mycobacterium tuberculosis and Mycobacterium leprae. Journal of Biomolecular Structure and Dynamics. 2019;37(5):1254–69. doi: 10.1080/07391102.2018.1454852 29557724

36. Lobanov MY, Bogatyreva N, Galzitskaya O. Radius of gyration as an indicator of protein structure compactness. Molecular Biology. 2008;42(4):623–8.

37. Anantram A, Kundaikar H, Degani M, Prabhu A. Molecular dynamic simulations on an inhibitor of anti-apoptotic Bcl-2 proteins for insights into its interaction mechanism for anti-cancer activity. Journal of Biomolecular Structure and Dynamics. 2019;37(12):3109–21. doi: 10.1080/07391102.2018.1508371 30526410

38. Chen J, Wang J, Zhu W. Molecular Mechanism and Energy Basis of Conformational Diversity of Antibody SPE7 Revealed by Molecular Dynamics Simulation and Principal Component Analysis. Scientific reports. 2016;6:36900. doi: 10.1038/srep36900 27830740

39. Mahapatra MK, Bera K, Singh DV, Kumar R, Kumar M. In silico modelling and molecular dynamics simulation studies of thiazolidine based PTP1B inhibitors. Journal of Biomolecular Structure and Dynamics. 2018;36(5):1195–211. doi: 10.1080/07391102.2017.1317026 28393626

40. Mitternacht S. FreeSASA: An open source C library for solvent accessible surface area calculations. F1000Research. 2016;5.

41. Chen H, Panagiotopoulos AZ. Molecular Modeling of Surfactant Micellization Using Solvent Accessible Surface Area. Langmuir. 2019.

42. Shukla H, Shukla R, Sonkar A, Tripathi T. Alterations in conformational topology and interaction dynamics caused by L418A mutation leads to activity loss of Mycobacterium tuberculosis isocitrate lyase. Biochemical and biophysical research communications. 2017;490(2):276–82. doi: 10.1016/j.bbrc.2017.06.036 28610921

43. Griffin JW, Bradshaw PC. In silico prediction of novel residues involved in amyloid primary nucleation of human I56T and D67H lysozyme. BMC structural biology. 2018;18(1):9. doi: 10.1186/s12900-018-0088-1 30029603

44. You W, Tang Z, Chang C-eA. Potential mean force from umbrella sampling simulations: what can we learn and what is missed? Journal of chemical theory and computation. 2019.

45. Bowman JD, Lindert S. Molecular Dynamics and Umbrella Sampling Simulations Elucidate Differences in Troponin C Isoform and Mutant Hydrophobic Patch Exposure. The Journal of Physical Chemistry B. 2018;122(32):7874–83. doi: 10.1021/acs.jpcb.8b05435 30070845

46. Liu Z, Zhang Y. Molecular dynamics simulations and MM–PBSA calculations of the lectin from snowdrop (Galanthus nivalis). Journal of molecular modeling. 2009;15(12):1501. doi: 10.1007/s00894-009-0502-5 19449157

47. Kumar A, Srivastava G, Negi AS, Sharma A. Docking, molecular dynamics, binding energy-MM-PBSA studies of naphthofuran derivatives to identify potential dual inhibitors against BACE-1 and GSK-3β. Journal of Biomolecular Structure and Dynamics. 2019;37(2):275–90. doi: 10.1080/07391102.2018.1426043 29310523

48. Mallamace D, Fazio E, Mallamace F, Corsaro C. The Role of Hydrogen Bonding in the Folding/Unfolding Process of Hydrated Lysozyme: A Review of Recent NMR and FTIR Results. International journal of molecular sciences. 2018;19(12):3825.

49. Leukert N, Vogl T, Strupat K, Reichelt R, Sorg C, Roth J. Calcium-dependent tetramer formation of S100A8 and S100A9 is essential for biological activity. Journal of molecular biology. 2006;359(4):961–72. doi: 10.1016/j.jmb.2006.04.009 16690079

50. Streicher WW, Lopez MM, Makhatadze GI. Modulation of quaternary structure of S100 proteins by calcium ions. Biophysical chemistry. 2010;151(3):181–6. doi: 10.1016/j.bpc.2010.06.003 20621410


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