Implications of fast-time scale dynamics of human DNA/RNA cytosine methyltransferases (DNMTs) for protein function

Discovery and development of DNA methyltransferase inhibitors using in silico approaches

Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries. Computational methods also synergize with natural-product-based drug discovery and drug repurposing. Herein, we survey the latest developments of in silico approaches to advance epigenetic drug and probe discovery targeting DNMTs.

Molecular modeling of inhibitors of human DNA methyltransferase with a crystal structure: discovery of a novel DNMT1 inhibitor

DNA methyltransferases (DNMTs) are promising epigenetic targets for the development of novel anticancer drugs and other diseases. Molecular modeling and experimental approaches are being used to identify and develop inhibitors of human DNMTs. Most of the computational efforts conducted so far with DNMT1 employ homology models of the enzyme. Recently, a crystallographic structure of the methyltransferase domain of human DNMT1 bound to unmethylated DNA was published. Following on our previous computational and experimental studies with DNMTs, we herein present molecular dynamics of the crystal structure of human DNMT1. Docking studies of established DNMT1 inhibitors with the crystal structure gave rise to a structure-based pharmacophore model that suggests key interactions of the inhibitors with the catalytic binding site. Results had a good agreement with the docking and pharmacophore models previously developed using a homology model of the catalytic domain of DNMT1. The docking protocol was able to distinguish active DNMT1 inhibitors from, for example, experimentally known inactive DNMT1 inhibitors. As part of our efforts to identify novel inhibitors of DNMT1, we conducted the experimental characterization of aurintricarboxylic acid (ATA) that in preliminary docking studies showed promising activity. ATA had a submicromolar inhibition (IC₅₀ = 0.68 μM) against DNMT1. ATA was also evaluated for Dnmt3a inhibition showing an IC₅₀ = 1.4 μM. This chapter illustrates the synergy from integrating molecular modeling and experimental methods to further advance the discovery of novel candidates for epigenetic therapies.

Will molecular dynamics simulations of proteins ever reach equilibrium?

We show that conformational entropies calculated for five proteins and protein-ligand complexes with dihedral-distribution histogramming, the von Mises approach, or quasi-harmonic analysis do not converge to any useful precision even if molecular dynamics (MD) simulations of 380-500 ns length are employed (the uncertainty is 12-89 kJ mol(-1)). To explain this, we suggest a simple protein model involving dihedrals with effective barriers forming a uniform distribution and show that for such a model, the entropy increases logarithmically with time until all significantly populated dihedral states have been sampled, in agreement with the simulations (during the simulations, 52-70% of the available dihedral phase space has been visited). This is also confirmed by the analysis of the trajectories of a 1 ms simulation of bovine pancreatic trypsin inhibitor (31 kJ mol(-1) difference in the entropy between the first and second part of the simulation). Strictly speaking, this means that it is practically impossible to equilibrate MD simulations of proteins. We discuss the implications of such a lack of strict equilibration of protein MD simulations and show that ligand-binding free energies estimated with the MM/GBSA method (molecular mechanics with generalised Born and surface-area solvation) vary by 3-15 kJ mol(-1) during a 500 ns simulation (the higher estimate is caused by rare conformational changes), although they involve a questionable but well-converged normal-mode entropy estimate, whereas free energies estimated by free-energy perturbation vary by less than 0.6 kJ mol(-1) for the same simulation.

Molecular dynamics simulations of human DNA methyltransferase 3B with selective inhibitor nanaomycin A

DNA methyltransferases (DNMTs) are involved in epigenetic regulation of the genome and are promising targets for therapeutic intervention in cancer and other diseases. Until now, very limited information is available concerning the molecular dynamics of DNMTs. The natural product nanaomycin A is the first selective inhibitor of DNMT3B that induce genomic demethylation. Herein we report long (>100ns) molecular dynamics simulations for human DNMT3B bound to nanaomycin A with and without the presence of the cofactor S-adenosyl-L-methionine (SAM). We concluded that SAM favors the binding of nanaomycin A to DNMT3B. Key interactions of nanaomycin A with DNMT3B involve long lasting interactions with Arg731, Arg733, Arg832, and the catalytic Cys651. Results further support the previous hypothesis that nanaomycin A has key interactions with amino acid residues involved in the mechanism of methylation. This work represents one of the first molecular dynamics studies of DNMT3B. Results of this work shed light on the structure and binding recognition process of a key epigenetic enzyme with a small molecule inhibitor.

A reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 protease with 22 ligands

In this study, we introduce a fast and reliable rescoring scheme for docked complexes based on a semiempirical quantum mechanical PM6-DH2 method. The method utilizes a PM6-based Hamiltonian with corrections for dispersion energy and hydrogen bonds. The total score is constructed as the sum of the PM6-DH2 interaction enthalpy, the empirical force field (AMBER) interaction entropy, and the sum of the deformation (PM6-DH2, SMD) and the desolvation (SMD) energies of the ligand. The main advantage of the procedure is the fact that we do not add any empirical parameter for either an individual component of the total score or an individual protein-ligand complex. This rescoring method is applied to a very challenging system, namely, the HIV-1 protease with a set of ligands. As opposed to the conventional DOCK procedure, the PM6-DH2 rescoring based on all of the terms distinguishes between binders and nonbinders and provides a reliable correlation of the theoretical and experimental binding free energies. Such a dramatic improvement, resulting from the PM6-DH2 rescoring of all the complexes, provides a valuable yet inexpensive tool for rational drug discovery and de novo ligand design.