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Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

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Abstract

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

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Abbreviations

DOT1L:

Disruptor of telomeric silencing 1-like

SAM:

S-adenosyl methionine

SAH:

S-adenosyl homocysteine

3-D QSAR:

Three-dimentional quantitative structure–activity relationship

Py-3-D_QSAR:

Python version of 3-D QSAutogrid/R procedure

COMBINE:

COMpatative BINding Energy analysis

COMBINEr:

Revisited COMBINE

Py-COMBINEr:

Python version of COMBINEr

PRMT:

Protein arginine methyl transferase

MLL1:

Myeloid/lymphoid or mixed-lineage leukemia 1

LB:

Ligand-based

LBDD:

Ligande-based drug design

SB:

Structure-based

SBDD:

Structure-based drug design

GRID:

Systematic grid spacing variation

VPO:

Variable pre-treatment optimization

CV:

Cross validation

LOO:

Leave one out

L5O:

Leave some out with 5 groups

LHO:

Leave half out, leave some out with 2 groups

ELE:

Per residues electrostatic interaction energies calculated by Autogrid

STE:

Per residues steric interaction energies calculated by Autogrid

DRY:

Per residues hydrophobic interaction energies calculated by Autogrid

HB:

Per residues hydrogen bonding interaction energies calculated by Autogrid

EC:

Experimental conformation

RC:

Randomized conformation

RD:

Re-docking

CD:

Cross-docking

DA:

Docking accuracy

ECRD:

Experimental conformation re-docking

RCRD:

Random conformation re-docking

ECCD:

Experimental conformation cross-docking

RCCD:

Random conformation cross-docking

RMSD:

Root mean square deviation

MTS:

Modeled test set

CTF:

Crystal test set

MPS:

Modeled prediction set

MIF:

Molecular interaction field

PLS:

Partial least square or projection of latent structures

PC:

Principal compontent

SDEC:

Standard deviation error of calculation

SDEP:

Standard deviation error of prediction

r 2 :

Conventional squared correlation coefficient

q 2 :

Cross-validated correlation coefficient

COEFs:

PLS coefficients

HP:

Histogram plot

AC:

Activity contribution

AAC:

Average activity contribution

MRAC:

Molecule–residue activity contribution

MRAAC:

Molecule–residue average activity contribution

MRIs:

Molecule–residues interactions

AMRIs:

Average molecule–residues interactions

References

  1. Copeland RA, Solomon ME, Richon VM (2009) Protein methyltransferases as a target class for drug discovery. Nat Rev Drug Discov 8(9):724–732. https://doi.org/10.1038/nrd2974

    Article  CAS  Google Scholar 

  2. Bannister AJ, Kouzarides T (2005) Reversing histone methylation. Nature 436(7054):1103–1106. https://doi.org/10.1038/nature04048

    Article  CAS  Google Scholar 

  3. Lachner M, O’Sullivan RJ, Jenuwein T (2003) An epigenetic road map for histone lysine methylation. J Cell Sci 116(Pt 11):2117–2124. https://doi.org/10.1242/jcs.00493

    Article  CAS  Google Scholar 

  4. Margueron R, Trojer P, Reinberg D (2005) The key to development: interpreting the histone code? Curr Opin Genet Dev 15(2):163–176

    Article  CAS  Google Scholar 

  5. Allis CD, Berger SL, Cote J, Dent S, Jenuwien T, Kouzarides T, Pillus L, Reinberg D, Shi Y, Shiekhattar R, Shilatifard A, Workman J, Zhang Y (2007) New nomenclature for chromatin-modifying enzymes. Cell 131(4):633–636. https://doi.org/10.1016/j.cell.2007.10.039

    Article  CAS  Google Scholar 

  6. Nguyen AT, Zhang Y (2011) The diverse functions of Dot1 and H3K79 methylation. Genes Dev 25(13):1345–1358. https://doi.org/10.1101/gad.2057811

    Article  CAS  Google Scholar 

  7. Illi B, Scopece A, Nanni S, Farsetti A, Morgante L, Biglioli P, Capogrossi MC, Gaetano C (2005) Epigenetic histone modification and cardiovascular lineage programming in mouse embryonic stem cells exposed to laminar shear stress. Circ Res 96(5):501–508. https://doi.org/10.1161/01.RES.0000159181.06379.63

    Article  CAS  Google Scholar 

  8. Jones B, Su H, Bhat A, Lei H, Bajko J, Hevi S, Baltus GA, Kadam S, Zhai H, Valdez R, Gonzalo S, Zhang Y, Li E, Chen T (2008) The histone H3K79 methyltransferase Dot1L is essential for mammalian development and heterochromatin structure. PLoS Genet 4(9):e1000190. https://doi.org/10.1371/journal.pgen.1000190

    Article  Google Scholar 

  9. Kimura A (2008) Molecular etiology and pathogenesis of hereditary cardiomyopathy. Circ J 72(Suppl A):A38–A48

    Article  Google Scholar 

  10. Nguyen AT, Xiao B, Neppl RL, Kallin EM, Li J, Chen T, Wang DZ, Xiao X, Zhang Y (2011) DOT1L regulates dystrophin expression and is critical for cardiac function. Genes Dev 25(3):263–274. https://doi.org/10.1101/gad.2018511

    Article  CAS  Google Scholar 

  11. Feng Y, Yang Y, Ortega MM, Copeland JN, Zhang M, Jacob JB, Fields TA, Vivian JL, Fields PE (2010) Early mammalian erythropoiesis requires the Dot1L methyltransferase. Blood 116(22):4483–4491. https://doi.org/10.1182/blood-2010-03-276501

    Article  CAS  Google Scholar 

  12. Im H, Park C, Feng Q, Johnson KD, Kiekhaefer CM, Choi K, Zhang Y, Bresnick EH (2003) Dynamic regulation of histone H3 methylated at lysine 79 within a tissue-specific chromatin domain. J Biol Chem 278(20):18346–18352. https://doi.org/10.1074/jbc.M300890200

    Article  CAS  Google Scholar 

  13. Slany RK (2016) The molecular mechanics of mixed lineage leukemia. Oncogene. https://doi.org/10.1038/onc.2016.30

    Google Scholar 

  14. Butler LH, Slany R, Cui X, Cleary ML, Mason DY (1997) The HRX proto-oncogene product is widely expressed in human tissues and localizes to nuclear structures. Blood 89(9):3361–3370

    CAS  Google Scholar 

  15. Gu Y, Nakamura T, Alder H, Prasad R, Canaani O, Cimino G, Croce CM, Canaani E (1992) The t(4;11) chromosome translocation of human acute leukemias fuses the ALL-1 gene, related to Drosophila trithorax, to the AF-4 gene. Cell 71(4):701–708

    Article  CAS  Google Scholar 

  16. Tkachuk DC, Kohler S, Cleary ML (1992) Involvement of a homolog of Drosophila trithorax by 11q23 chromosomal translocations in acute leukemias. Cell 71(4):691–700

    Article  CAS  Google Scholar 

  17. Okada Y, Feng Q, Lin Y, Jiang Q, Li Y, Coffield VM, Su L, Xu G, Zhang Y (2005) hDOT1L links histone methylation to leukemogenesis. Cell 121(2):167–178. https://doi.org/10.1016/j.cell.2005.02.020

    Article  CAS  Google Scholar 

  18. Daigle SR, Olhava EJ, Therkelsen CA, Basavapathruni A, Jin L, Boriack-Sjodin PA, Allain CJ, Klaus CR, Raimondi A, Scott MP, Waters NJ, Chesworth R, Moyer MP, Copeland RA, Richon VM, Pollock RM (2013) Potent inhibition of DOT1L as treatment of MLL-fusion leukemia. Blood 122(6):1017–1025. https://doi.org/10.1182/blood-2013-04-497644

    Article  CAS  Google Scholar 

  19. Bitoun E, Oliver PL, Davies KE (2007) The mixed-lineage leukemia fusion partner AF4 stimulates RNA polymerase II transcriptional elongation and mediates coordinated chromatin remodeling. Hum Mol Genet 16(1):92–106. https://doi.org/10.1093/hmg/ddl444

    Article  CAS  Google Scholar 

  20. Bernt KM, Armstrong SA (2011) Targeting epigenetic programs in MLL-rearranged leukemias. Hematol Am Soc Hematol Educ Program 2011:354–360. https://doi.org/10.1182/asheducation-2011.1.354

    Google Scholar 

  21. Yao Y, Chen P, Diao J, Cheng G, Deng L, Anglin JL, Prasad BV, Song Y (2011) Selective inhibitors of histone methyltransferase DOT1L: design, synthesis, and crystallographic studies. J Am Chem Soc 133(42):16746–16749. https://doi.org/10.1021/ja206312b

    Article  CAS  Google Scholar 

  22. Yu W, Smil D, Li F, Tempel W, Fedorov O, Nguyen KT, Bolshan Y, Al-Awar R, Knapp S, Arrowsmith CH, Vedadi M, Brown PJ, Schapira M (2013) Bromo-deaza-SAH: a potent and selective DOT1L inhibitor. Bioorg Med Chem 21(7):1787–1794. https://doi.org/10.1016/j.bmc.2013.01.049

    Article  CAS  Google Scholar 

  23. Bento AP, Gaulton A, Hersey A, Bellis LJ, Chambers J, Davies M, Kruger FA, Light Y, Mak L, McGlinchey S, Nowotka M, Papadatos G, Santos R, Overington JP (2014) The ChEMBL bioactivity database: an update. Nucleic Acids Res 42(Database issue):D1083–D1090. https://doi.org/10.1093/nar/gkt1031

    Article  CAS  Google Scholar 

  24. Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40(Database issue):D1100–D1107. https://doi.org/10.1093/nar/gkr777

    Article  CAS  Google Scholar 

  25. Song Y, Lisheng DE, Yao Y, Zhang L, Wang C, Redell MS, Shuo DO (2016) Non-ribose containing inhibitors of histone methyltransferase dot1l for cancer treatment. Google Patents

  26. Song Y, Chen P, DIAO J, Cheng G, DENG L, ANGLIN JL, PRASAD VBV, Yao Y (2014) Selective inhibitors of histone methyltransferase dot1l. Google Patents

  27. Bradner J (2015) Dot1l probes. Google Patents

  28. Chen C, Zhu H, Stauffer F, Caravatti G, Vollmer S, Machauer R, Holzer P, Möbitz H, Scheufler C, Klumpp M, Tiedt R, Beyer KS, Calkins K, Guthy D, Kiffe M, Zhang J, Gaul C (2016) Discovery of novel Dot1L Inhibitors through a structure-based fragmentation approach. ACS Med Chem Lett 7(8):735–740. https://doi.org/10.1021/acsmedchemlett.6b00167

    Article  CAS  Google Scholar 

  29. Chen S, Li L, Chen Y, Hu J, Liu J, Liu YC, Liu R, Zhang Y, Meng F, Zhu K, Lu J, Zheng M, Chen K, Zhang J, Jiang H, Yao Z, Luo C (2016) Identification of novel disruptor of telomeric silencing 1-like (DOT1L) inhibitors through structure-based virtual screening and biological assays. J Chem Inf Model 56(3):527–534. https://doi.org/10.1021/acs.jcim.5b00738

    Article  CAS  Google Scholar 

  30. Luo M, Wang H, Zou Y, Zhang S, Xiao J, Jiang G, Zhang Y, Lai Y (2016) Identification of phenoxyacetamide derivatives as novel DOT1L inhibitors via docking screening and molecular dynamics simulation. J Mol Graph Model 68:128–139. https://doi.org/10.1016/j.jmgm.2016.06.011

    Article  CAS  Google Scholar 

  31. Scheufler C, Möbitz H, Gaul C, Ragot C, Be C, Fernández C, Beyer KS, Tiedt R, Stauffer F (2016) Optimization of a fragment-based screening hit toward potent DOT1L inhibitors interacting in an induced binding pocket. ACS Med Chem Lett 7(8):730–734. https://doi.org/10.1021/acsmedchemlett.6b00168

    Article  CAS  Google Scholar 

  32. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  Google Scholar 

  33. Anglin JL, Song Y (2013) A medicinal chemistry perspective for targeting histone H3 lysine-79 methyltransferase DOT1L. J Med Chem 56(22):8972–8983. https://doi.org/10.1021/jm4007752

    Article  CAS  Google Scholar 

  34. Meng F, Cheng S, Ding H, Liu S, Liu Y, Zhu K, Chen S, Lu J, Xie Y, Li L, Liu R, Shi Z, Zhou Y, Liu YC, Zheng M, Jiang H, Lu W, Liu H, Luo C (2015) Discovery and optimization of novel, selective histone methyltransferase SET7 inhibitors by pharmacophore- and docking-based virtual screening. J Med Chem 58(20):8166–8181. https://doi.org/10.1021/acs.jmedchem.5b01154

    Article  CAS  Google Scholar 

  35. Raj U, Kumar H, Gupta S, Varadwaj PK (2015) Novel DOT1L receptornatural inhibitors involved in mixed lineage leukemia: a virtual screening, molecular docking and dynamics simulation study. Asian Pac J Cancer Prev 16(9):3817–3825. https://doi.org/10.7314/APJCP.2015.16.9.3817

    Article  Google Scholar 

  36. Kouranov A, Xie L, de la Cruz J, Chen L, Westbrook J, Bourne PE, Berman HM (2006) The RCSB PDB information portal for structural genomics. Nucleic Acids Res 34(Database issue):D302–D305. https://doi.org/10.1093/nar/gkj120

    Article  CAS  Google Scholar 

  37. Ragno R, Ballante F, Pirolli A, Wickersham RB 3rd, Patsilinakos A, Hesse S, Perspicace E, Kirsch G (2015) Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches. J Comput Aided Mol Des 29(8):757–776. https://doi.org/10.1007/s10822-015-9859-y

    Article  CAS  Google Scholar 

  38. Basavapathruni A, Jin L, Daigle SR, Majer CR, Therkelsen CA, Wigle TJ, Kuntz KW, Chesworth R, Pollock RM, Scott MP, Moyer MP, Richon VM, Copeland RA, Olhava EJ (2012) Conformational adaptation drives potent, selective and durable inhibition of the human protein methyltransferase DOT1L. Chem Biol Drug Des 80(6):971–980. https://doi.org/10.1111/cbdd.12050

    Article  CAS  Google Scholar 

  39. Yao Y, Chen P, Diao J, Cheng G, Deng L, Anglin JL, Prasad BVV, Song Y (2011) Selective inhibitors of histone methyltransferase DOT1L: design, synthesis, and crystallographic studies. J Am Chem Soc 133(42):16746–16749. https://doi.org/10.1021/ja206312b

    Article  CAS  Google Scholar 

  40. Yu W, Chory EJ, Wernimont AK, Tempel W, Scopton A, Federation A, Marineau JJ, Qi J, Barsyte-Lovejoy D, Yi J, Marcellus R, Iacob RE, Engen JR, Griffin C, Aman A, Wienholds E, Li F, Pineda J, Estiu G, Shatseva T, Hajian T, Al-Awar R, Dick JE, Vedadi M, Brown PJ, Arrowsmith CH, Bradner JE, Schapira M (2012) Catalytic site remodelling of the DOT1L methyltransferase by selective inhibitors. Nat Commun 3:1288. https://doi.org/10.1038/ncomms2304

    Article  Google Scholar 

  41. Richon VM, Johnston D, Sneeringer CJ, Jin L, Majer CR, Elliston K, Jerva LF, Scott MP, Copeland RA (2011) Chemogenetic analysis of human protein methyltransferases. Chem Biol Drug Des 78(2):199–210. https://doi.org/10.1111/j.1747-0285.2011.01135.x

    Article  CAS  Google Scholar 

  42. Min J, Feng Q, Li Z, Zhang Y, Xu RM (2003) Structure of the catalytic domain of human DOT1L, a non-SET domain nucleosomal histone methyltransferase. Cell 112(5):711–723

    Article  CAS  Google Scholar 

  43. Daigle SR, Olhava EJ, Therkelsen CA, Basavapathruni A, Jin L, Boriack-Sjodin PA, Allain CJ, Klaus CR, Raimondi A, Scott MP, Waters NJ, Chesworth R, Moyer MP, Copeland RA, Richon VM, Pollock RM (2013) Potent inhibition of DOT1L as treatment of MLL-fusion leukemia. Blood 122(6):1017

    Article  CAS  Google Scholar 

  44. Hu H, Luo C, Zheng YG (2016) Transient kinetics define a complete kinetic model for protein arginine methyltransferase 1. J Biol Chem 291(52):26722–26738. https://doi.org/10.1074/jbc.M116.757625

    Article  CAS  Google Scholar 

  45. Cheng Y, Prusoff WH (1973) Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem Pharmacol 22(23):3099–3108

    Article  CAS  Google Scholar 

  46. Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52(6):1674–1685. https://doi.org/10.1021/ci300123x

    Article  CAS  Google Scholar 

  47. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top Med Chem 10(1):95–115

    Article  CAS  Google Scholar 

  48. Ballante F, Musmuca I, Marshall GR, Ragno R (2012) Comprehensive model of wild-type and mutant HIV-1 reverse transciptases. J Comput Aided Mol Des 26(8):907–919. https://doi.org/10.1007/s10822-012-9586-6

    Article  CAS  Google Scholar 

  49. Melo-Filho CC, Braga RC, Andrade CH (2014) 3D-QSAR approaches in drug design: perspectives to generate reliable CoMFA models. Curr Comput Aided Drug Des 10(2):148–159

    Article  CAS  Google Scholar 

  50. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334

    CAS  Google Scholar 

  51. Cleves AE, Jain AN (2015) Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock. J Comput Aided Mol Des 29(6):485–509. https://doi.org/10.1007/s10822-015-9846-3

    Article  CAS  Google Scholar 

  52. Korb O, Stutzle T, Exner TE (2009) Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 49(1):84–96. https://doi.org/10.1021/ci800298z

    Article  CAS  Google Scholar 

  53. Yuriev E, Ramsland PA (2013) Latest developments in molecular docking: 2010–2011 in review. J Mol Recognit 26(5):215–239. https://doi.org/10.1002/jmr.2266

    Article  CAS  Google Scholar 

  54. Caroli A, Ballante F, Wickersham RB 3rd, Corelli F, Ragno R (2014) Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. J Chem Inf Model 54(3):970–977. https://doi.org/10.1021/ci400760a

    Article  CAS  Google Scholar 

  55. Consalvi S, Alfonso S, Di Capua A, Poce G, Pirolli A, Sabatino M, Ragno R, Anzini M, Sartini S, La Motta C, Di Cesare Mannelli L, Ghelardini C, Biava M (2015) Synthesis, biological evaluation and docking analysis of a new series of methylsulfonyl and sulfamoyl acetamides and ethyl acetates as potent COX-2 inhibitors. Bioorg Med Chem 23(4):810–820. https://doi.org/10.1016/j.bmc.2014.12.041

    Article  CAS  Google Scholar 

  56. Musmuca I, Caroli A, Mai A, Kaushik-Basu N, Arora P, Ragno R (2010) Combining 3-D quantitative structure-activity relationship with ligand based and structure based alignment procedures for in silico screening of new hepatitis C virus NS5B polymerase inhibitors. J Chem Inf Model 50(4):662–676. https://doi.org/10.1021/ci9004749

    Article  CAS  Google Scholar 

  57. Ballante F, Caroli A, Wickersham RB 3rd, Ragno R (2014) Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening. J Chem Inf Model 54(3):956–969. https://doi.org/10.1021/ci400759t

    Article  CAS  Google Scholar 

  58. Wang Y, Li L, Zhang B, Xing J, Chen S, Wan W, Song Y, Jiang H, Jiang H, Luo C, Zheng M (2017) Discovery of novel disruptor of silencing telomeric 1-like (DOT1L) Inhibitors using a target-specific scoring function for the S-adenosyl-L-methionine (SAM)-dependent methyltransferase family. J Med Chem. https://doi.org/10.1021/acs.jmedchem.6b01785

    Google Scholar 

  59. Lozano JJ, Pastor M, Cruciani G, Gaedt K, Centeno NB, Gago F, Sanz F (2000) 3D-QSAR methods on the basis of ligand-receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series of CYP1A2 ligands. J Comput Aided Mol Des 14(4):341–353

    Article  CAS  Google Scholar 

  60. O’Boyle NM, Morley C, Hutchison GR (2008) Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem Cent J 2:5. https://doi.org/10.1186/1752-153X-2-5

    Article  Google Scholar 

  61. O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8(1):36. https://doi.org/10.1186/s13321-016-0148-0

    Article  Google Scholar 

  62. Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57(8):3186–3204. https://doi.org/10.1021/jm401411z

    Article  CAS  Google Scholar 

  63. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3(11):935–949. https://doi.org/10.1038/nrd1549

    Article  CAS  Google Scholar 

  64. Fiser A, Sali A (2003) Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 374:461–491. https://doi.org/10.1016/S0076-6879(03)74020-8

    Article  CAS  Google Scholar 

  65. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. https://doi.org/10.1002/jcc.20084

    Article  CAS  Google Scholar 

  66. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33–33. https://doi.org/10.1186/1758-2946-3-33

    Article  Google Scholar 

  67. Morris GM, Huey R, Olson AJ (2008) Using AutoDock for ligand-receptor docking. Curr Protoc Bioinformatics. https://doi.org/10.1002/0471250953.bi0814s24

    Google Scholar 

  68. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  Google Scholar 

  69. Pedregosa F, Gaël V, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  70. Silvestri L, Ballante F, Mai A, Marshall GR, Ragno R (2012) Histone deacetylase inhibitors: structure-based modeling and isoform-selectivity prediction. J Chem Inf Model 52(8):2215–2235. https://doi.org/10.1021/ci300160y

    Article  CAS  Google Scholar 

  71. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33

    Article  Google Scholar 

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Acknowledgements

M.S. acknowledges FIRB Grant RBFR10ZJQT for a 1 year fellowship and Sapienza University for “Progetto di Avvio alla Ricerca”. This work was supported by grants from PRIN 2016 (prot. 20152TE5PK) (A.M.), AIRC 2016 (n. 19162) (A.M.), AIRC Fondazione Cariplo TRIDEO Id. 17515 (D.R.), NIH (n. R01GM114306) (A.M.), Italian Ministry of Health Grant PE-2013-02355271 (A.M.) and Progetti di Ricerca di Università (Prot. C26A15988X) (R.R.). Many thanks to the reviewers that with their suggestions helped to improve the manuscript quality.

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Authors and Affiliations

  1. Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Università di Roma, P. le A. Moro 5, 00185, Roma, Italy

    Manuela Sabatino, Alexandros Patsilinakos & Rino Ragno

  2. Dipartimento di Chimica e Tecnologie del Farmaco, Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza Università di Roma, P. le A. Moro 5, 00185, Roma, Italy

    Manuela Sabatino, Dante Rotili, Alexandros Patsilinakos, Mariantonietta Forgione, Daniela Tomaselli, Antonello Mai & Rino Ragno

  3. Alchemical Dynamics s.r.l., 00125, Roma, Italy

    Alexandros Patsilinakos & Rino Ragno

  4. Laboratoire Pierre Fabre, 3 Avenue H. Curien, 31100, Toulouse, France

    Fréderic Alby

  5. CNRS FRE3600 ETaC, Bât IBCG, Toulouse, France

    Paola B. Arimondo

  6. Oversea Fellow, Churchill College, Cambridge, CB3 0DS, UK

    Paola B. Arimondo

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  1. Manuela Sabatino
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Correspondence to Antonello Mai or Rino Ragno.

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Sabatino, M., Rotili, D., Patsilinakos, A. et al. Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches. J Comput Aided Mol Des 32, 435–458 (2018). https://doi.org/10.1007/s10822-018-0096-z

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