STRUCTURAL AND FUNCTIONAL ANALYSIS OF AF9-MLL ONCOGENIC FUSION PROTEIN USING HOMOLOGY MODELING AND SIMULATION BASED APPROACH

Authors

  • Medha Dave Department of Bioinformatics, Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India
  • Aditi Daga Department of Microbiology, MVM Science Collage, Saurashtra University, Rajkot, Gujarat, India
  • Rakesh Rawal Department of Cancer Biology, The Gujarat Cancer and Research institute, Ahmedabad, Gujarat, India.

Keywords:

MLL, Fusion Protein, Molecular modeling, Simulation, Structure Prediction

Abstract

Objective: AF9-MLL has been implicated in the pathogenesis of AML, New Therapeutic regimens are prerequisite for this category of hematological malignancy due to the poor prognosis. The experimental 3D structure of AF9-MLL is not available. Therefore, present study aims in developing the homology model and evaluating the best model through Energy Minimization and MD simulation. The structure further analyzed for functional Annotation.

Methods: To the best of our knowledge, our study is novel in terms of predicting homology based 3D model of AF9-MLL leukemogenic fusion protein, facilitated by I-TASSER. The 3D modeled structure was subsequently optimized with MD simulation for 2 ns. Further stereo-chemical analysis and verification of the best structure so obtained were undertaken by different computational programs including PROCHECK, PROVE, Verify3D and ERRAT.

Results: Homology model predicted from I-TASSER and refined by YASARA showed results with 86.5% residues in the most favorable region, 14.7% in the allowed region, 0.8% in the generously allowed region and 0.3% in the disallowed region. The RMSD between the modeled and the refined structure was found to be 2.37 Ã…. The results of ERRAT, Verify_3D, Prove and ProSA confirmed that the simulated model and energy minimized model is very good then the predicted raw model. The final structure was successfully submitted in Protein Model Database (PMDB) under ID: PM0080061.

Conclusion: In this study, homology model was developed and Validated for MLL-AF9 using bio-informatics tools. These analyses validated that the simulated model is best, robust as well as reliable enough to be used for future study and the functional analysis shows the presence of CXXC domain. Eventually, these molecular and structural studies result in advancement of newer therapies.

 

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References

TH Rabbitts. Chromosomal translocations in human cancer. Nature 1994;372:143-9.

Bernard O, Berger R. Molecular basis of 11q23 rearrangements in hematopoietic malignant proliferations. Genes Chromo-somes Cancer 1995;13:75-85.

Djabali M, Selleri L, Parry P, Bowe M, Young BD, Evans GA. A trithorax-like gene is interrupted by chromosome 11q23 translocations in acute leukaemias. Nat Genet 1992;2:113–8.

Gu Y, Nakamura T, Alder H, Prasad R, Canaani O, Cimino G. The t(4,11) chromosome translocation of human acute leukemias fuses the ALL-1 gene related to Drosophila trithorax to the AF-4gene: Cell 1992;71:701–8.

Tkachuk DC, Kohler S, Cleary ML. Involvement of a homolog of Drosophila trithorax by 11q23 chromosomal translocations in acute leukemias. Cell 1992;1:691-700.

Domer PH, Fakharzadeh SS, Chen CS, Jockel J, Jo hansen L, Silverman GA, Kersey JH, et al. Acute mixed-lineage t(4,11)(q21,q23) generates an MLL-AF4 fusion product. Proc Natl Acad Sci USA 1993;90:884–8.

Corral J, Forster A, Thompson S, Lampert F, Kaneko Y. Acute leukemias of different lineages have similar MLL1 gene fusions encoding related chimeric proteins resulting from chromosomal translocations. Proc Natl Acad Sci USA 1993;90:8538–42.

Lo-Coco F, Mandelli F, Breccia M, Annino L, Guglielmi C, Petti MC, et al. Southern blot analysis of ALL-1 rearrangements at chromosome 11q23 in acute leukemia. Cancer Res 1993;3:3800–3.

Iida S, Seto M, Yamamoto K, Komatsu H. MLLT3 gene on 9p22 in t(9,11) leukemia encodes a serine/proline rich protein homologous to MLLT1 on 19p13. Oncogene 1993;8:3085–92.

Nakamura T, Alder H, Gu Y, Prasad R, Canaani O, Kamada N, et al. Genes on chromosome 4,9 and 19 involved in 11q23 abnormalities in acute leukemia share homology and/or common motifs. Proc Natl Acad Sci USA 1993;90:4631–35.

Yamamoto K, Seto M, Iida S, Komatsu H, Kamada N, Kojima S, et al. A reverse transcriptase-polymerase chain reaction detects heterogeneous chimeric mRNAs in leukemias with 11q23 abnormalities. Blood 1994;83:2912–21.

Corral J, Lavenir I, Impey H, Warren AJ, Forster A, Larson TA, et al. An MLL-AF9 fusion gene made by homologous recombination causes acute leukemia in chimeric mice: a method to create fusion oncogenes. Cell 1996;85:853–61.

Joh T, Hosokawa Y, Suzuki R, Takahashi T, Seto M. Establishment of an inducible expression system of chimeric MLL-LTG9 protein and inhibition ofHoxa7Hoxb7and Hoxc9expressionbyMLL-LTG9in 32Dcl3 cells. Oncogene 1999;8:1125–30.

Dobson CL, Warren AJ, Pannell R, Forster A, Lavenir I, Corral J, et al. The Mll-AF9 gene fusion in mice controls myeloproliferation and specifies acute myeloid leukaemogenesis. EMBO J 1999;8:3564–74.

Pina C, May G, Soneji S, Hong D, Enver T. MLLT3 regulates early human erythroid and megakaryocytic cell fate Cell. Stem Cell 2008;2:264-73.

Gan T, Jude CD, Zaffuto K, Ernst P. Developmentally induced Mll1 loss reveals defects in postnatal haematopoiesis. Leukemia 2010;24:1732-41.

Collins EC, Appert A, Ariza-McNaughton L, Pannell R, Yamada Y, Rabbitts TH. Mouse Af9 is a controller of embryo patterning like Mll whose human homologue fuses with Af9 after chromosomal translocation in leukemia. Mol Cell Biol 2002;22:7313-24.

Slany RK. The molecular biology of mixed lineage leukemia. Haematologica 2009;4:984-93.

Mohan M, Lin C, Guest E, Shilatifard. A Licensed to elongate: a molecular mechanism for MLL-based leukaemogenesis. Nat Rev Cancer 2010;0:721-8.

Whitmarsh RJ, Saginario C, Zhuo Y, Hilgenfeld E, Rappaport EF, Megonigal MD, et al. Reciprocal DNA topoisomerase II cleavage events at 5'-TATTA-3' sequences in MLL and AF-9 create homologous single-stranded overhangs that anneal to form der and der genomic breakpoint junctions in treatment-related AML without further processing. Oncogene 2003;2:8448-59.

Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinf 2008;9:40.

Zhao F, Peng J, Debartolo J. A probabilistic and continuous model of protein conformational space for template-free modeling. J Comput Biol 2010;7:783-98.

Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, et al. Improving physical realism stereochemistry and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins 2009;7:114-22.

Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G. Making optimal use of empirical energy functions: force-field parameterization in crystal space. Proteins 2004;57:678-83.

Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;6:283-91.

Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles methods. Enzymology 1997;77:396-404.

Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 1993;9:1511-9.

Pontius J, Richelle J, Wodak SJ. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J Mol Biol 1996:264:121-36.

Madej T, Addess KJ, Fong JH, Geer LY, Geer RC, Lanczycki CJ, et al. MMDB: 3D structures and macromolecular interactions. Nucleic Acids Res 2012;0:D461-4.

Pal D, Eisenberg D. Inference of protein function from protein structure. Structure 2005;3:121-30.

Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, et al. Inter Pro Scan: protein domains identifier. Nucleic Acids Res 2005;3:W116-20.

Barcellos GB, Pauli I, Caceres RA, Timmers LF, Dias R, de Azevedo WF. Molecular modeling as a tool for drug discovery. Curr Drug Targets 2008;9:1084-91.

Takeda SM, Takaya D, Chiba C, Tanaka H, Umeyama H. Protein structure prediction in structure based drug design. Curr Med Chem 2004;1:551-8.

Cavasotto CN, Phatak SS. Homology modeling in drug discovery: current trends and applications. Drug Discovery Today 2009;4:676-83.

Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery. Curr Perspectives Indian J Pharm Sci 2012;4:1–17.

Abagyan R, Batalov S, Cardozo T, Totrov M, Webber J, Zhou Y. Homology modeling with internal coordinate mechanics: deformation zone mapping and improvements of models via conformational search. Proteins 1997;1:29-37.

Baker D, Sali A. Protein structure prediction and structural genomics. Science 2001;294:93-6.

Bradley P, Misura KM, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science 2005;309:1868-71.

Joo K, Lee J, Lee S, Seo JH, Lee SJ. High accuracy template based modeling by global optimization proteins. Proteins: Struct Funct Bioinf 2007;69:83-9.

Misura KMS, Chivian D, Rohl CA, Kim DE, Baker D. Physically realistic homology models built with ROSETTA can be more accurate than their templates. Proc Natl Acad Sci India 2006;103:5361–6.

Wang Q, Canutescu AA, Dunbrack RL. SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc 2008;3:1832–47.

Krivov GG, Shapovalov MV, Dunbrack RL. Improved prediction of protein side-chain conformations with SCWRL4 proteins. Proteins: Struct Funct Bioinf 2009;77:778–51.

Levitt M. Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 1992;226:507–33.

Tomasz C, Laurie E, Jolanta G, Stephen ML, Relja P, Monika O, et al. Structure of the MLL CXXC domain–DNA complex and its functional role in MLL-AF9 leukemia. Nat Struct Mol Biol 2010;17:62–8.

Hiroshi O, Marie K, Akinori K, Hirotaka M, Takeshi K, Toshiya I, et al. MLL fusion proteins link transcriptional coactivators to previously active CpG-rich promoters. Nucleic Acids Res 2014;42:4241–56.

Sánchez R, Pieper U, Melo F, Eswar N, Martí-Renom MA, Madhusudhan MS, et al. Protein structure modeling for structural genomics. Nat Struct Biol 2000;7:986-90.

Qiu Y, Liu L, Zhao C, Han C, Li F, Zhang J, et al. Combinatorial readout of unmodified H3R2 and acetylated H3K14 by the tandem PHD finger of MOZ reveals a regulatory mechanism for HOXA9 transcription. Genes Dev 2012;26:1376–91.

Cross SH, Meehan RR, Nan X, Bird A. A component of the transcriptional repressor MeCP1 shares a motif with DNA methyltransferase and HRX proteins. Nat Genet 1997;16:256-9.

Published

01-12-2015

How to Cite

Dave, M., A. Daga, and R. Rawal. “STRUCTURAL AND FUNCTIONAL ANALYSIS OF AF9-MLL ONCOGENIC FUSION PROTEIN USING HOMOLOGY MODELING AND SIMULATION BASED APPROACH”. International Journal of Pharmacy and Pharmaceutical Sciences, vol. 7, no. 12, Dec. 2015, pp. 155-61, https://mail.innovareacademics.in/journals/index.php/ijpps/article/view/8721.

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