Evaluación in silico de las interacciones entre el receptor FMS-tirosina cinasa 3 con la mutación ITD (FLT3-ITD) y los inhibidores de FLT3 usados en el tratamiento de la Leucemia Mieloide Aguda.
Abstract
Acute myeloid leukemia (AML) is a hematopoietic neoplasm, which represents 80% of leukemia cases worldwide. Its main feature is the hyperproliferation of immature myeloid cells. In leukemic cells, the ITD (internal tandem duplication) mutation in the FMS-tyrosine kinase 3 (FLT3) receptor is associated with proliferative and survival advantages, as well as increased cellular malignancy, therefore, FLT3 is considered a therapeutic target. The use of FLT3 inhibitors such as Midostaurin, Gilteritinib, and Quizartinib has been approved for monotherapy, and other is still under study (Sorafenib). However, little is known on the type of molecular interactions between these inhibitors and the wild type (FLT3-WT) or mutated (FLT3-ITD) FLT3 receptors. In this work we evaluated in silico the interactions between FLT3-WT, FLT3-ITD and their inhibitors. The models constructions were performed by Homologous Protein Modeling (SWISS MODEL and Modeller). The structures were then refined and their quality was validated with ERRAT, VERITY3D, QMEAN and ProSA. Quality values were: ERRAT:95.6% and Z-score:-7.35 (FLT3-WT) and ERRAT:83.2% and Z-score:-7.6 (FLT3-ITD). Finally, molecular dockings between the FLT3 structures and their inhibitors were performed (Autodock-Vina). The best affinities were between WT-FLT3 and Quizartinib (-10.3Kcal/mol), and WT-FLT3 and Gilteritinib (-9.5Kcal/mol), compared to Sorafenib (-8.3Kcal/mol), and Midostaurin (-7.6Kcal/mol). Compared to FLT3-WT, the affinities of FLT3-ITD with Quizartinib (-8.6Kcal/mol) and Gilteritinib (-7.4Kcal/mol) decreased, but increased with Midostaurin (-8.2Kcal/mol), while no apparent changes were observed for Sorafenib (-9.3Kcal/mol). Therefore, the ITD mutation in FLT3 modified the affinities and interactions within the inhibitor-receptor complexes.
References
Antar A., Otrock Z., Cheikh J., Kharfan-Dabaja M., Battipaglia G., Mahfouz R., Mohty M. y Bazarbachi A. (2017). Inhibition of FLT3 in AML: a focus on sorafenib. “Bone Marrow Transplantation”, 52, 344-351.
Antar A., Otrock Z., Jabbour E., Mohty M. y Bazarbachi A. (2019). FLT3 inhibitors in acute myeloid leukemia: ten frequently asked questions. “Leukemia”, 34(3), 682-696.
Arber D., Orazi A., Hasserjia R., Thiele J., Borowitz M., Le Beau M., Bloomfield C., Cazzola M. y Vardiman J. (2016). The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. “Blood”, 127, 2391-405.
Benkert P., Biasini M. y Schwede T. (2011). Toward the estimation of the absolute quality of individual protein structure models. “Bioinformatics”, 27, 343-350.
Berman H., Westbrook J., Feng Z., Gilliland G., Bhat T, Weissig H., Shindyalov I. y Bourne P.E. (2000). The Protein Data Bank. “Nucleic Acids Research”, 28, 235-242.
Bhattacharya D., Nowotny J., Cao R. y Cheng J. (2016). 3Drefine: an interactive web server for efficient protein structure refinement. “Nucleic Acids Research”, 8(44), W406-9.
Bohl S., Bullinger L. y Rücker F. (2019). New Targeted Agents in Acute Myeloid Leukemia: New Hope on the Rise. “International Journal of Molecular Science”, 20, 1983, 1-19.
Colvos C. y Yeates T. (1993). Verification of protein structures: patterns of nonbonded atomic interactions. “Protein Science”, 2, 1511-1519.
Contreras-Moreira B., Fitzjohn P. y Bates P. (2002). Comparative modelling: an essential methodology for protein structure prediction in the post-genomic era. “Applied Bioinformatics”, 1(4), 177-190.
Daver N., Schlenk R., Russell N. y Levis M. (2019). Targeting FLT3 mutations in AML: review of current knowledge and evidence. “Leukemia”, 33, 299-312.
Ding L., Ley T., Larson D., Miller C. Koboldt D., Welch J., Ritchey J., Young M., Lamprecht T., McLellan M., McMichael J., Wallis J., Lu C., Shen D., Harris C., Dooling D., Fulton R., Fulton L., Chen K., Schmidt H., y DiPersio, J. (2012). Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. “Nature”, 481(7382), 506–510.
Duarte D., Hawkins E. y Lo Celso C. (2018). The interplay of leukemia cells and the bone marrow microenvironment. “Blood Spothlight”, 131(14), 1507-1511.
Egbuna C., Patrick-Iwuanyanwu K., Onyeike E., Khan J. y Alshehri B. (2021). FMS-like tyrosine kinase-3 (FLT3) inhibitors with better binding affinity and ADMET properties than sorafenib and gilteritinib against acute myeloid leukemia: in silico studies. “Journal of Biomolecular Structure and Dynamics”, 6, 1-12
Eisenberg D., Luthy R. y Bowie J. (1997). VERIFY3D: Assessment of protein models with three-dimensional profiles. “Methods Enzymology”, 277, 396-404.
Fernández S., Desplat V., Villacreces A., Guitart A., Milpied N., Rigneux A., Vigo I., Pasquet J. y Dumas P. (2019). Targeting tyrosine kinases in acute myeloid leukemia: Why, who and how? “International Journal of Molecular Sciences”, 20(3429), 1-17.
Gallivan J. P. y Dougherty D. A. (1999). Cation-pi interactions in structural biology. “Proceedings of the National Academy of Sciences of the United States of America”, 96(17), 9459–9464.
Gokhale P., Chauhan A., Arora A., Khandekar N., Nayarisseri A. y Singh S. (2019). FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. “Bioinformation”. 15(2) 104-115.
Griffith J., Black J., Faerman C., Swenson L., Wynn M., Lu F., Lippke J. y Saxena K. (2004). The Structural Basis for Autoinhibition of FLT3 by the Juxtamembrane Domain. “Molecular Cell”, 13(1), 169-178.
Gurkan U. y Akkus O. (2008). The mechanical environment of bone marrow: A review. “Annals of Biomedical Engineering”, 36(12), 1978-1991.
Irwin J. y Shoichet B. (2005). ZINC- A Free database of commercially available compounds for virtual screening. “Journal of Chemical Information and Modeling”, 45(1), 177-182.
Kazi J. y Rönnstrand L. (2019). FMS-like tyrosine kinase 3/FLT3: from basic science to clinical implications. “Physiological Reviews”, 99, 1433-1466.
Lagunas-Rangel F. (2016). Leucemia mieloide aguda: una perspectiva de los mecanismos moleculares del cáncer. “Gaceta Mexicana de Oncología”, 15(3), 150-157.
Lagunas-Rangel F., Pérez-Contreras V. y Cortés-Penagos C. (2015). FLT3, NPMI y C/EBPa como marcadores de pronóstico en pacientes con Leucemia Mieloide Aguda. “Revista de Hematología”, 16, 152-167.
Larráyoz M., Mañu A., Ariceta B., Vázquez I., Aguilera-Díaz A., Fernández-Mercado M. y Calasanz M. (2019). Diagnóstico molecular de alteraciones en el gel FLT3: impacto clínico, retos y propuestas. “Genética Médica y Genómica”, 3(3), 31-39.
Laskowski R., Moss D., y Thornton J. (1993). Main-chain bond length and bond angles in protein structures. “Journal of Molecular Biology”, 231, 1049-1067.
Leyto-Cruz F. (2018). Acute Myeloid Leukemia. “Revista de Hematología”, 19(1), 24-40.
Loschi M., Sammut R., Chiche E. y Cluzeau T. (2021). FLT3 Tyrosine Kinase Inhibitors for the Treatment of Fit and Unfit Patients with FLT3-Mutated AML: A Systematic Review. “International Journal of Molecular Sciences”, 22(11), 5873.
Mashkani B., Hossein M., Saadatmandzadeh M., Ashman L. y Griffith R. (2016). FMS-like tyrosine kinase 3 (FLT3) inhibitors: Molecular docking and experimental studies. “European Journal of Pharmacology”. 776, 156-166.
Meshinchi S. y Appelbaum F. (2009). Structural and functional alterations of FLT3 in acute myeloid leukemia. “Clinical Cancer Research”, 15, 4263-4269.
Morris G., Huey R., Lindstrom W., Sanner M., Belew R., Goodsell D. y Olson A. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. “Journal of computational chemistry”, 30(16), 2785-2791.
Pettersen E., Goddard T., Huang C., Couch G., Greenblatt D., Meng E. y Ferriny T. (2004). “Journal of Computational Chemistry”, 25(13), 1605-1612
Reiter K., Polzer H., Krupka C., Maiser A., Vick B., Rothenberg-Thurley M., Metzeler K., Dörfel D., Salih H., Jung G., Nößner E., Jeremias I., Hiddemann W., Leonhardt H., Spiekermann K., Subklewe M. y Greif P. (2018). Tyrosine kinase inhibition increases the cell surface localization of FLT3-ITD and enhances FLT3-directed immunotherapy of acute myeloid leukemia. “Leukemia”, 32(2), 313-322.
Sexauer A, y Tasian S. (2017). Targeting FLT3 Signaling in Childhood Acute Myeloid Leukemia. “Frontiers in pediatrics”, 5, 248.
Schrödinger L., y DeLano W. (2020). PyMOL. Disponible en: http://www.pymol.org/pymol
Staudt D., Murray H., McLachlan T., Alvaro F., Enjeti A., Verrills N. y Dun M. (2018). Targeting Oncogenic Signaling in Mutant FLT3 Acute Myeloid Leukemia: The Path to Least Resistance. “International Journal of Molecular Sciences”, 19, 3198.
Thomas C. (2018). Crystal structure of the FLT3 kinase bound to a small molecule inhibitor (PDB ID 6IL3) doi: 10.2210/pdb6il3/pdb.
Trott O. y Olson A. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. “Journal of computational chemistry”, 31(2), 455–461.
Waterhouse A., Bertoni M., Bienert S., Studer G., Tauriello G., Gumienny R., Heer F., de Beer T., Rempfer C., Bordoli L., Lepore R. y Schwede T. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. “Nucleic Acids Research”, 46, W296-W303.
Webb B. y Sali A. (2016). Comparative Protein Structure Modeling Using Modeller. “Current Protocols in Bioinformatics” 54, John Wiley & Sons, Inc., 5.6.1-5.6.37.
Wiederstein M. y Sippl M. (2007). ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. “Nucleic Acids Research”, 35, W407–W410.
Zorn J., Wang Q., Fujimura E., Barros T. y Kuriyan J. (2015). Crystal structure of the FLT3 kinase domain bound to the inhibitor Quizartinib (AC220). “PloS one”, 10(4), e0121177.
Antar A., Otrock Z., Jabbour E., Mohty M. y Bazarbachi A. (2019). FLT3 inhibitors in acute myeloid leukemia: ten frequently asked questions. “Leukemia”, 34(3), 682-696.
Arber D., Orazi A., Hasserjia R., Thiele J., Borowitz M., Le Beau M., Bloomfield C., Cazzola M. y Vardiman J. (2016). The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. “Blood”, 127, 2391-405.
Benkert P., Biasini M. y Schwede T. (2011). Toward the estimation of the absolute quality of individual protein structure models. “Bioinformatics”, 27, 343-350.
Berman H., Westbrook J., Feng Z., Gilliland G., Bhat T, Weissig H., Shindyalov I. y Bourne P.E. (2000). The Protein Data Bank. “Nucleic Acids Research”, 28, 235-242.
Bhattacharya D., Nowotny J., Cao R. y Cheng J. (2016). 3Drefine: an interactive web server for efficient protein structure refinement. “Nucleic Acids Research”, 8(44), W406-9.
Bohl S., Bullinger L. y Rücker F. (2019). New Targeted Agents in Acute Myeloid Leukemia: New Hope on the Rise. “International Journal of Molecular Science”, 20, 1983, 1-19.
Colvos C. y Yeates T. (1993). Verification of protein structures: patterns of nonbonded atomic interactions. “Protein Science”, 2, 1511-1519.
Contreras-Moreira B., Fitzjohn P. y Bates P. (2002). Comparative modelling: an essential methodology for protein structure prediction in the post-genomic era. “Applied Bioinformatics”, 1(4), 177-190.
Daver N., Schlenk R., Russell N. y Levis M. (2019). Targeting FLT3 mutations in AML: review of current knowledge and evidence. “Leukemia”, 33, 299-312.
Ding L., Ley T., Larson D., Miller C. Koboldt D., Welch J., Ritchey J., Young M., Lamprecht T., McLellan M., McMichael J., Wallis J., Lu C., Shen D., Harris C., Dooling D., Fulton R., Fulton L., Chen K., Schmidt H., y DiPersio, J. (2012). Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. “Nature”, 481(7382), 506–510.
Duarte D., Hawkins E. y Lo Celso C. (2018). The interplay of leukemia cells and the bone marrow microenvironment. “Blood Spothlight”, 131(14), 1507-1511.
Egbuna C., Patrick-Iwuanyanwu K., Onyeike E., Khan J. y Alshehri B. (2021). FMS-like tyrosine kinase-3 (FLT3) inhibitors with better binding affinity and ADMET properties than sorafenib and gilteritinib against acute myeloid leukemia: in silico studies. “Journal of Biomolecular Structure and Dynamics”, 6, 1-12
Eisenberg D., Luthy R. y Bowie J. (1997). VERIFY3D: Assessment of protein models with three-dimensional profiles. “Methods Enzymology”, 277, 396-404.
Fernández S., Desplat V., Villacreces A., Guitart A., Milpied N., Rigneux A., Vigo I., Pasquet J. y Dumas P. (2019). Targeting tyrosine kinases in acute myeloid leukemia: Why, who and how? “International Journal of Molecular Sciences”, 20(3429), 1-17.
Gallivan J. P. y Dougherty D. A. (1999). Cation-pi interactions in structural biology. “Proceedings of the National Academy of Sciences of the United States of America”, 96(17), 9459–9464.
Gokhale P., Chauhan A., Arora A., Khandekar N., Nayarisseri A. y Singh S. (2019). FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. “Bioinformation”. 15(2) 104-115.
Griffith J., Black J., Faerman C., Swenson L., Wynn M., Lu F., Lippke J. y Saxena K. (2004). The Structural Basis for Autoinhibition of FLT3 by the Juxtamembrane Domain. “Molecular Cell”, 13(1), 169-178.
Gurkan U. y Akkus O. (2008). The mechanical environment of bone marrow: A review. “Annals of Biomedical Engineering”, 36(12), 1978-1991.
Irwin J. y Shoichet B. (2005). ZINC- A Free database of commercially available compounds for virtual screening. “Journal of Chemical Information and Modeling”, 45(1), 177-182.
Kazi J. y Rönnstrand L. (2019). FMS-like tyrosine kinase 3/FLT3: from basic science to clinical implications. “Physiological Reviews”, 99, 1433-1466.
Lagunas-Rangel F. (2016). Leucemia mieloide aguda: una perspectiva de los mecanismos moleculares del cáncer. “Gaceta Mexicana de Oncología”, 15(3), 150-157.
Lagunas-Rangel F., Pérez-Contreras V. y Cortés-Penagos C. (2015). FLT3, NPMI y C/EBPa como marcadores de pronóstico en pacientes con Leucemia Mieloide Aguda. “Revista de Hematología”, 16, 152-167.
Larráyoz M., Mañu A., Ariceta B., Vázquez I., Aguilera-Díaz A., Fernández-Mercado M. y Calasanz M. (2019). Diagnóstico molecular de alteraciones en el gel FLT3: impacto clínico, retos y propuestas. “Genética Médica y Genómica”, 3(3), 31-39.
Laskowski R., Moss D., y Thornton J. (1993). Main-chain bond length and bond angles in protein structures. “Journal of Molecular Biology”, 231, 1049-1067.
Leyto-Cruz F. (2018). Acute Myeloid Leukemia. “Revista de Hematología”, 19(1), 24-40.
Loschi M., Sammut R., Chiche E. y Cluzeau T. (2021). FLT3 Tyrosine Kinase Inhibitors for the Treatment of Fit and Unfit Patients with FLT3-Mutated AML: A Systematic Review. “International Journal of Molecular Sciences”, 22(11), 5873.
Mashkani B., Hossein M., Saadatmandzadeh M., Ashman L. y Griffith R. (2016). FMS-like tyrosine kinase 3 (FLT3) inhibitors: Molecular docking and experimental studies. “European Journal of Pharmacology”. 776, 156-166.
Meshinchi S. y Appelbaum F. (2009). Structural and functional alterations of FLT3 in acute myeloid leukemia. “Clinical Cancer Research”, 15, 4263-4269.
Morris G., Huey R., Lindstrom W., Sanner M., Belew R., Goodsell D. y Olson A. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. “Journal of computational chemistry”, 30(16), 2785-2791.
Pettersen E., Goddard T., Huang C., Couch G., Greenblatt D., Meng E. y Ferriny T. (2004). “Journal of Computational Chemistry”, 25(13), 1605-1612
Reiter K., Polzer H., Krupka C., Maiser A., Vick B., Rothenberg-Thurley M., Metzeler K., Dörfel D., Salih H., Jung G., Nößner E., Jeremias I., Hiddemann W., Leonhardt H., Spiekermann K., Subklewe M. y Greif P. (2018). Tyrosine kinase inhibition increases the cell surface localization of FLT3-ITD and enhances FLT3-directed immunotherapy of acute myeloid leukemia. “Leukemia”, 32(2), 313-322.
Sexauer A, y Tasian S. (2017). Targeting FLT3 Signaling in Childhood Acute Myeloid Leukemia. “Frontiers in pediatrics”, 5, 248.
Schrödinger L., y DeLano W. (2020). PyMOL. Disponible en: http://www.pymol.org/pymol
Staudt D., Murray H., McLachlan T., Alvaro F., Enjeti A., Verrills N. y Dun M. (2018). Targeting Oncogenic Signaling in Mutant FLT3 Acute Myeloid Leukemia: The Path to Least Resistance. “International Journal of Molecular Sciences”, 19, 3198.
Thomas C. (2018). Crystal structure of the FLT3 kinase bound to a small molecule inhibitor (PDB ID 6IL3) doi: 10.2210/pdb6il3/pdb.
Trott O. y Olson A. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. “Journal of computational chemistry”, 31(2), 455–461.
Waterhouse A., Bertoni M., Bienert S., Studer G., Tauriello G., Gumienny R., Heer F., de Beer T., Rempfer C., Bordoli L., Lepore R. y Schwede T. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. “Nucleic Acids Research”, 46, W296-W303.
Webb B. y Sali A. (2016). Comparative Protein Structure Modeling Using Modeller. “Current Protocols in Bioinformatics” 54, John Wiley & Sons, Inc., 5.6.1-5.6.37.
Wiederstein M. y Sippl M. (2007). ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. “Nucleic Acids Research”, 35, W407–W410.
Zorn J., Wang Q., Fujimura E., Barros T. y Kuriyan J. (2015). Crystal structure of the FLT3 kinase domain bound to the inhibitor Quizartinib (AC220). “PloS one”, 10(4), e0121177.
Published
2022-09-27
Section
Artículos de Investigación
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Avisos de derechos de autor propuestos por Creative Commons
1. Política propuesta para revistas que ofrecen acceso abierto
Aquellos autores/as que tengan publicaciones con esta revista, aceptan los términos siguientes:
- Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
- Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista.
- Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) antes y durante el proceso de envío, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).
