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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

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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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  4. Mariantonietta Forgione
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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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