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.
Similar content being viewed by others
Data availability
This study did not require material.
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
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
Maret W (2010) Metalloproteomics, metalloproteomes, and the annotation of metalloproteins. Metallomics 2:117–125. https://doi.org/10.1039/b915804a
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
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
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
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
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
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
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
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
Wibley JEA, Pegg AE, Moody PCE (2000) Crystal structure of the human O(6)-alkylguanine-DNA alkyltransferase. Nucleic Acids Res 28:393–401
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Zhu S (2011) Computational and experimental studies of protein kinase-inhibitor interactions. Univ Iowa
Laskowski RA (2009) PDBsum new things. Nucleic Acids Res 37:D355–D359. https://doi.org/10.1093/nar/gkn860
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
Author information
Authors and Affiliations
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.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics approval
Because this was a computational biology study, it did not require an ethical verification code.
Consent to participate
This study was not performed on individuals.
Consent for publication
Because this study was not performed on individuals, there is no need for consent to publish.
Code availability
This study did not require material.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supporting Information
File S include Figurers, table to support “RESULTS” sections of this paper.
ESM 1
(DOCX 4732 kb).
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00894-020-04631-x