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In silico profiling and structural insights of zinc metal ion on O6-methylguanine methyl transferase and its interactions using molecular dynamics approach

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Abstract

O6-methylguanine DNA methyl transferase (MGMT) is a metalloenzyme participating in the repair of alkylated DNA. In this research, we performed a comparative study for evaluating the impact of zinc metal ion on the behavior and interactions of MGMT in the both enzymatic forms of apo MGMT and holo MGMT. DNA and proliferating cell nuclear antigen (PCNA), as partners of MGMT, were utilized to evaluate molecular interactions by virtual microscopy of molecular dynamics simulation. The stability and conformational alterations of each forms (apo and holo) MGMT-PCNA, and (apo and holo) MGMT-DNA complexes were calculated by MM/PBSA method. A total of seven systems including apo MGMT, holo MGMT, free PCNA, apo MGMT-PCNA, holo MGMT-PCNA, apo MGMT–DNA, and holo MGMT-DNA complexes were simulated. In this study, we found that holo MGMT was more stable and had better folding and functional properties than that of apo MGMT. Simulation analysis of (apo and holo) MGMT-PCNA complexes displayed that the sequences of the amino acids involved in the interactions were different in the two forms of MGMT. The important amino acids of holo MGMT involved in its interaction with PCNA included E92, K101, A119, G122, N123, P124, and K125, whereas the important amino acids of apo MGMT included R128, R135, S152, N157, Y158, and L162. Virtual microscopy of molecular dynamics simulation showed that the R128 and its surrounding residues were important amino acids involved in the interaction of holo MGMT with DNA that was exactly consistent with X-ray crystallography structure. In the apo form of the protein, the N157 and its surrounding residues were important amino acids involved in the interaction with DNA. The binding free energies of − 387.976, − 396.226, − 622.227, and − 617.333 kcal/mol were obtained for holo MGMT-PCNA, apo MGMT-PCNA, holo MGMT-DNA, and apo MGMT-DNA complexes, respectively. The principle result of this research was that the area of molecular interactions differed between the two states of MGMT. Therefore, in investigations of metalloproteins, the metal ion must be preserved in their structures. Finally, it is recommended to use the holo form of metalloproteins in in vitro and in silico researches.

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Abbreviations

Apo:

Without Zn

DSSP:

Determine the secondary structure of protein

ED:

Essential dynamics

FEL:

Free energy landscape

Holo:

With Zn

HTH:

Helix Turn Helix

IDCL:

Interdomain connector loop

MD:

Molecular dynamics

MDS:

Molecular dynamics simulation

MGMT:

O6-methylguanine DNA methyl transferase

MM/PBSA:

Molecular mechanics/Poisson Boltzmann surface area

NPT:

Fixed number of particles, pressure, and temperature

NVT:

Fixed number of particles, volume, and temperature

PCA:

Principle component analysis

PCHR:

Proline, Cysteine, Histidine, Arginine

PCNA:

Proliferating cell nuclear antigen

PDB:

Protein Data Bank

PIP:

PCNA-interacting protein

PME:

Particle mesh Ewald

RESP:

Restrained electrostatic potential

Rg:

Radius of gyration

RMSD:

Root mean square deviation

RMSF:

Root mean square fluctuation

SASA:

Solvent accessible surface area

SN1:

The SN1 reactions happen in two steps: 1. The leaving group leaves, and the substrate forms a carbocation intermediate. 2. The nucleophile attacks the carbocation, forming the product

SN2:

The SN2 reaction is a type of nucleophilic substitution reaction mechanism that one bond is broken and one bond is formed synchronously, i.e., in one step

Zn:

Zinc

References

  1. Andreini C, Banci L, Bertini I, Rosato A (2006) Zinc through the three domains of life. J Proteome Res 5:3173–3178. https://doi.org/10.1021/pr0603699

    Article  CAS  PubMed  Google Scholar 

  2. Maret W (2010) Metalloproteomics, metalloproteomes, and the annotation of metalloproteins. Metallomics 2:117–125. https://doi.org/10.1039/b915804a

    Article  CAS  PubMed  Google Scholar 

  3. Andreini C, Banci L, Bertini I, Rosato A (2006) Counting the zinc-proteins encoded in the human genome. J Proteome Res 5:196–201. https://doi.org/10.1021/pr050361j

    Article  CAS  PubMed  Google Scholar 

  4. Daniels DS, Mol CD, Arvai AS et al (2000) Active and alkylated human AGT structures: a novel zinc site, inhibitor and extrahelical base binding. EMBO J 19:1719–1730. https://doi.org/10.1093/emboj/19.7.1719

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sharma S, Salehi F, Scheithauer BW et al (2009) Role of MGMT in tumor development, progression, diagnosis, treatment and prognosis. Anticancer Res 29:3759–3768

    CAS  PubMed  Google Scholar 

  6. Lamb KL, Liu Y, Ishiguro K et al (2014) Tumor-associated mutations in O(6) -methylguanine DNA-methyltransferase (MGMT) reduce DNA repair functionality. Mol Carcinog 53:201–210. https://doi.org/10.1002/mc.21964

    Article  CAS  PubMed  Google Scholar 

  7. Duguid E, Rice P, He C The structure of the human AGT protein bound to DNA and its implications for damage detection. J Mol Biol 350:657–666. https://doi.org/10.1016/j.jmb.2005.05.028

  8. Tubbs JL, Pegg AE, Tainer JA (2007) DNA binding, nucleotide flipping, and the helix-turn-helix motif in base repair by O6-alkylguanine-DNA alkyltransferase and its implications for cancer chemotherapy. DNA Repair (Amst) 6:1100–1115. https://doi.org/10.1016/j.dnarep.2007.03.011

    Article  CAS  Google Scholar 

  9. Srivenugopal KS, Yuan XH, Friedman HS, Ali-Osman F (1996) Ubiquitination-dependent proteolysis of O6-methylguanine-DNA methyltransferase in human and murine tumor cells following inactivation with O6-benzylguanine or 1,3-bis(2-chloroethyl)-1-nitrosourea. Biochemistry 35:1328–1334. https://doi.org/10.1021/bi9518205

    Article  CAS  PubMed  Google Scholar 

  10. Sekiguchi M, Nakabeppu Y, Sakumi K, Tuzuki T (1996) DNA-repair methyltransferase as a molecular device for preventing mutation and cancer. J Cancer Res Clin Oncol 122:199–206. https://doi.org/10.1007/bf01209646

    Article  CAS  PubMed  Google Scholar 

  11. Wibley JEA, Pegg AE, Moody PCE (2000) Crystal structure of the human O(6)-alkylguanine-DNA alkyltransferase. Nucleic Acids Res 28:393–401

    Article  CAS  Google Scholar 

  12. Kaina B, Christmann M, Naumann S, Roos WP (2007) MGMT: key node in the battle against genotoxicity, carcinogenicity and apoptosis induced by alkylating agents. DNA Repair 6:1079–1099. https://doi.org/10.1016/j.dnarep.2007.03.008

    Article  CAS  PubMed  Google Scholar 

  13. Gerson SL (2002) Clinical relevance of MGMT in the treatment of cancer. J Clin Oncol 20:2388–2399. https://doi.org/10.1200/JCO.2002.06.110

    Article  CAS  PubMed  Google Scholar 

  14. Fong LYY, Cheung T, Ho YS (1988) Effect of nutritional zinc-deficiency on O6-alkylguanine-DNA-methyl-transferase activities in rat tissues. Cancer Lett 42:217–223. https://doi.org/10.1016/0304-3835(88)90308-4

    Article  CAS  PubMed  Google Scholar 

  15. Pegg AE, Wiest L, Foote RS et al (1983) Purification and properties of O6-methylguanine-DNA transmethylase from rat liver. J Biol Chem 258:2327–2333

    Article  CAS  Google Scholar 

  16. Pegg AE (1990) Properties of mammalian O6-alkylguanine-DNA transferases. Mutat Res Mol Mech Mutagen 233:165–175. https://doi.org/10.1016/0027-5107(90)90160-6

    Article  CAS  Google Scholar 

  17. Rasimas JJ, Kanugula S, Dalessio PM, Ropson IJ, Fried MG, Pegg AE (2003) Effects of zinc occupancy on human O 6-alkylguanine- DNA alkyltransferase. Biochemistry 42:980–990

    Article  CAS  Google Scholar 

  18. Forge V, Wijesinha RT, Balbach J et al (1999) Rapid collapse and slow structural reorganisation during the refolding of bovine α-lactalbumin11Edited by P. E Wright J Mol Biol 288:673–688. https://doi.org/10.1006/jmbi.1999.2687

    Article  CAS  Google Scholar 

  19. IKEGUCHI M, KUWAJIMA K, SUGAI S (1986) Ca2+ alteration in the unfolding behavior of α-lactalbumin1. J Biochem 99:1191–1201. https://doi.org/10.1093/oxfordjournals.jbchem.a135582

    Article  CAS  PubMed  Google Scholar 

  20. Bushmarina NA, Blanchet CE, Vernier G, Forge V (2006) Cofactor effects on the protein folding reaction: acceleration of alpha-lactalbumin refolding by metal ions. Protein Sci 15:659–671. https://doi.org/10.1110/ps.051904206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Banci L, Bertini I, Del Conte R et al (2004) Solution structure and backbone dynamics of the cu(I) and apo forms of the second metal-binding domain of the Menkes protein ATP7A. Biochemistry 43:3396–3403. https://doi.org/10.1021/bi036042s

    Article  CAS  PubMed  Google Scholar 

  22. Invernizzi G, Papaleo E, Grandori R et al (2009) Relevance of metal ions for lipase stability: structural rearrangements induced in the Burkholderia glumae lipase by calcium depletion. J Struct Biol 168:562–570. https://doi.org/10.1016/j.jsb.2009.07.021

    Article  CAS  PubMed  Google Scholar 

  23. Mostofa A, Punganuru SR, Madala HR, Srivenugopal KS (2018) S-phase specific downregulation of human O(6)-methylguanine DNA methyltransferase (MGMT) and its serendipitous interactions with PCNA and p21(cip1) proteins in glioma cells. Neoplasia 20:305–323. https://doi.org/10.1016/j.neo.2018.01.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Niture SK, Doneanu CE, Velu CS et al (2005) Proteomic analysis of human O6-methylguanine-DNA methyltransferase by affinity chromatography and tandem mass spectrometry. Biochem Biophys Res Commun 337:1176–1184. https://doi.org/10.1016/j.bbrc.2005.09.177

    Article  CAS  PubMed  Google Scholar 

  25. Gulbis JM, Kelman Z, Hurwitz J et al (1996) Structure of the C-terminal region of p21(WAF1/CIP1) complexed with human PCNA. Cell 87:297–306

    Article  CAS  Google Scholar 

  26. Hays FA, Teegarden A, Jones ZJR et al (2005) How sequence defines structure: a crystallographic map of DNA structure and conformation. Proc Natl Acad Sci U S A 102:7157–7162. https://doi.org/10.1073/pnas.0409455102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2

  28. Aier I, Varadwaj PK, Raj U (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep 6:34984. https://doi.org/10.1038/srep34984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hornak V, Abel R, Okur A et al (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65:712–725. https://doi.org/10.1002/prot.21123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713. https://doi.org/10.1021/acs.jctc.5b00255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cieplak P, Cornell WD, Bayly C, Kollman PA (1995) Application of the multimolecule and multiconformational RESP methodology to biopolymers: charge derivation for DNA, RNA, and proteins. J Comput Chem 16:1357–1377. https://doi.org/10.1002/jcc.540161106

    Article  CAS  Google Scholar 

  32. Az’hari S, Mosaddeghi H, Ghayeb Y (2019) Molecular dynamics study of the interaction between RNA-binding domain of NS1 influenza A virus and various types of carbon nanotubes. Curr Sci 116:398. https://doi.org/10.18520/cs/v116/i3/398-404

    Article  Google Scholar 

  33. Anbarasu K, Jayanthi S (2018) Identification of curcumin derivatives as human LMTK3 inhibitors for breast cancer: a docking, dynamics, and MM/PBSA approach. 3 Biotech 8:228. https://doi.org/10.1007/s13205-018-1239-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pandey B, Grover A, Sharma P (2018) Molecular dynamics simulations revealed structural differences among WRKY domain-DNA interaction in barley (Hordeum vulgare). BMC Genomics 19:132. https://doi.org/10.1186/s12864-018-4506-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhu S (2011) Computational and experimental studies of protein kinase-inhibitor interactions. Univ Iowa

  36. Laskowski RA (2009) PDBsum new things. Nucleic Acids Res 37:D355–D359. https://doi.org/10.1093/nar/gkn860

    Article  CAS  PubMed  Google Scholar 

  37. Laskowski RA, Jabłońska J, Pravda L et al (2018) PDBsum: structural summaries of PDB entries. Protein Sci 27:129–134. https://doi.org/10.1002/pro.3289

    Article  CAS  PubMed  Google Scholar 

  38. Rodziewicz-Motowidło S, Wahlbom M, Wang X et al (2006) Checking the conformational stability of cystatin C and its L68Q variant by molecular dynamics studies: why is the L68Q variant amyloidogenic? J Struct Biol 154:68–78. https://doi.org/10.1016/j.jsb.2005.11.015

    Article  CAS  PubMed  Google Scholar 

  39. Sarma H, Kumar Mattaparthi VS (2018) Unveiling the transient protein-protein interactions that regulate the activity of human lemur tyrosine kinase-3 (LMTK3) domain by cyclin dependent kinase 5 (CDK5) in breast cancer: an in silico study. Curr Proteomics 15:62–70. https://doi.org/10.2174/1570164614666170726160314

    Article  CAS  Google Scholar 

  40. Smith AA, Caruso A (2013) In silico characterization and homology modeling of a cyanobacterial phosphoenolpyruvate carboxykinase enzyme. Struct Biol 2013:10. https://doi.org/10.1155/2013/370820

    Article  Google Scholar 

  41. Sagendorf JM, Markarian N, Berman HM, Rohs R (2019) DNAproDB: an expanded database and web-based tool for structural analysis of DNA–protein complexes. Nucleic Acids Res 48:D277–D287. https://doi.org/10.1093/nar/gkz889

    Article  CAS  PubMed Central  Google Scholar 

  42. Baker NA, Sept D, Joseph S et al (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98:10037–10041. https://doi.org/10.1073/pnas.181342398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kumari R, Kumar R, Lynn A (2014) g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model 54:1951–1962. https://doi.org/10.1021/ci500020m

    Article  CAS  PubMed  Google Scholar 

  44. Wang C, Greene D, Xiao L et al (2018) Recent developments and applications of the MMPBSA method. Front Mol Biosci 4. https://doi.org/10.3389/fmolb.2017.00087

  45. Ren X, Zeng R, Wang C et al (2017) Structural insight into inhibition of REV7 protein interaction revealed by docking{,} molecular dynamics and MM/PBSA studies. RSC Adv 7:27780–27786. https://doi.org/10.1039/C7RA03716C

    Article  CAS  Google Scholar 

  46. Genheden S, Ryde U (2010) How to obtain statistically converged MM/GBSA results. J Comput Chem 31:837–846. https://doi.org/10.1002/jcc.21366

    Article  CAS  PubMed  Google Scholar 

  47. Balasubramanian PK, Balupuri A, Kang H-Y, Cho SJ (2017) Receptor-guided 3D-QSAR studies, molecular dynamics simulation and free energy calculations of Btk kinase inhibitors. BMC Syst Biol 11:6. https://doi.org/10.1186/s12918-017-0385-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sindhikara DJ, Roitberg AE, Merz KMJ (2009) Apo and nickel-bound forms of the Pyrococcus horikoshii species of the metalloregulatory protein: NikR characterized by molecular dynamics simulations. Biochemistry 48:12024–12033. https://doi.org/10.1021/bi9013352

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sala D, Giachetti A, Rosato A (2018) Molecular dynamics simulations of metalloproteins: a folding study of rubredoxin from Pyrococcus furiosus. Biophysics (Oxf) 5:77–96. https://doi.org/10.3934/biophy.2018.1.77

    Article  CAS  Google Scholar 

  50. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637. https://doi.org/10.1002/bip.360221211

    Article  CAS  PubMed  Google Scholar 

  51. Anwar MA, Choi S (2017) Structure-activity relationship in TLR4 mutations: atomistic molecular dynamics simulations and residue interaction network analysis. Sci Rep 7:43807. https://doi.org/10.1038/srep43807

    Article  PubMed  PubMed Central  Google Scholar 

  52. George Priya Doss C, Rajith B, Chakraboty C et al (2014) In silico profiling and structural insights of missense mutations in RET protein kinase domain by molecular dynamics and docking approach. Mol BioSyst 10:421–436. https://doi.org/10.1039/c3mb70427k

    Article  CAS  PubMed  Google Scholar 

  53. Pearson K (1901) LIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos Mag J Sci 2:559–572. https://doi.org/10.1080/14786440109462720

    Article  Google Scholar 

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Acknowledgments

We are grateful to Dr. Fatemeh Ravari for providing computational chemistry laboratory. We acknowledge the help of Dr. Mohammad Fathabadi for advice in some analysis.

Funding

This is part of a doctoral dissertation and is funded by the researchers themselves.

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Authors

Contributions

The project was designed and directed by Jamshid Mehrzad. Marzieh Gharouni developed the theory and performed the computations. Hamid Mosaddeghi provided guidance in performing computer calculations and data analysis. Ali Es-haghic and Alireza Motavalizadehkakhky were consultants for the entire project. All authors contributed to the writing of the paper.

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Correspondence to Hamid Mosaddeghi or Jamshid Mehrzad.

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Gharouni, M., Mosaddeghi, H., Mehrzad, J. et al. In silico profiling and structural insights of zinc metal ion on O6-methylguanine methyl transferase and its interactions using molecular dynamics approach. J Mol Model 27, 40 (2021). https://doi.org/10.1007/s00894-020-04631-x

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