Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases

Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

DNA methylation and its potential roles in common oral diseases

Oral diseases are among the most common diseases worldwide and are associated with systemic illnesses, and the rising occurrence of oral diseases significantly impacts the quality of life for many individuals. It is crucial to detect and treat these conditions early to prevent them from advancing. DNA methylation is a fundamental epigenetic process that contributes to a variety of diseases including various oral diseases. Taking advantage of its reversibility, DNA methylation becomes a viable therapeutic target by regulating various cellular processes. Understanding the potential role of this DNA alteration in oral diseases can provide significant advances and more opportunities for diagnosis and therapy. This article will review the biology of DNA methylation, and then mainly discuss the key findings on DNA methylation in oral cancer, periodontitis, endodontic disease, oral mucosal disease, and clefts of the lip and/or palate in the background of studies on global DNA methylation and gene-specific DNA methylation.

Natural Products Impacting DNA Methyltransferases and Histone Deacetylases

Epigenetics refers to heritable changes in gene expression and chromatin structure without change in a DNA sequence. Several epigenetic modifications and respective regulators have been reported. These include DNA methylation, chromatin remodeling, histone post-translational modifications, and non-coding RNAs. Emerging evidence has revealed that epigenetic dysregulations are involved in a wide range of diseases including cancers. Therefore, the reversible nature of epigenetic modifications concerning activation or inhibition of enzymes involved could be promising targets and useful tools for the elucidation of cellular and biological phenomena. In this review, emphasis is laid on natural products that inhibit DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) making them promising candidates for the development of lead structures for anticancer-drugs targeting epigenetic modifications. However, most of the natural products targeting HDAC and/or DNMT lack isoform selectivity, which is important for determining their potential use as therapeutic agents. Nevertheless, the structures presented in this review offer the well-founded basis that screening and chemical modifications of natural products will in future provide not only leads to the identification of more specific inhibitors with fewer side effects, but also important features for the elucidation of HDAC and DNMT function with respect to cancer treatment.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

The potential role of in silico approaches to identify novel bioactive molecules from natural resources

In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].

Resveratrol-salicylate derivatives as selective DNMT3 inhibitors and anticancer agents

Resveratrol is a natural polyphenol with plethora of biological activities. Resveratrol has previously shown to decrease DNA-methyltransferase (DNMT) enzymes expression and to reactivate silenced tumor suppressor genes. Currently, it seems that no resveratrol analogs have been developed as DNMT inhibitors. Recently, we reported the synthesis of resveratrol-salicylate derivatives and by examining the chemical structure of these analogs, we proposed that these compounds could exhibit DNMT inhibition especially that they resembled NSC 14778, a compound we previously identified as a DNMT inhibitor by virtual screening. Indeed, using in vitro DNMT inhibition assay, some of the resveratrol-salicylate analogs we screened in this work that showed selective inhibition against DNMT3 enzymes which were greater than resveratrol. A molecular docking study revealed key binding interactions with DNMT3A and DNMT3B enzymes. In addition, the most active analog, 10 showed considerable cytotoxicity against three human cancer cells; HT-29, HepG2 and SK-BR-3, which was greater than resveratrol. Further studies are needed to understand the anticancer mechanisms of these derivatives.

Molecular Modeling and Chemoinformatics to Advance the Development of Modulators of Epigenetic Targets: A Focus on DNA Methyltransferases

In light of the emerging field of Epi-informatics, ie, computational methods applied to epigenetic research, molecular docking, and dynamics, pharmacophore and activity landscape modeling and QSAR play a key role in the development of modulators of DNA methyltransferases (DNMTs), one of the major epigenetic target families. The increased chemical information available for modulators of DNMTs has opened up the avenue to explore the epigenetic relevant chemical space (ERCS). Herein, we discuss recent progress on the identification and development of inhibitors of DNMTs as potential epi-drugs and epi-probes that have been driven by molecular modeling and chemoinformatics methods. We also survey advances on the elucidation of their structure-activity relationships and exploration of ERCS. Finally, it is illustrated how computational approaches can be applied to identify modulators of DNMTs in food chemicals.

Ensemble-Based Virtual Screening and Experimental Validation of Inhibitors Targeting a Novel Site of Human DNMT1

Human DNA methyltransferase1 (hDNMT1) is responsible for preserving DNA methylation patterns that play important regulatory roles in differentiation and development. Misregulation of DNA methylation has thus been linked to many syndromes, life style diseases, and cancers. Developing specific inhibitors of hDNMT1 is an important challenge in the area since the currently targeted cofactor and substrate binding site share structural features with various proteins. In this work, we generated a structural model of the active form of hDNMT1 and identified that the 5-methylcytosine (5-mC) binding site of the hDNMT1 is structurally unique to the protein. This site has been previously demonstrated to be critical for methylation activity. We further performed multiple nanosecond time scale atomistic molecular dynamics simulations of the structural model followed by virtual screening of the Asinex database to identify inhibitors targeting the 5-mC site. Two compounds were discovered that inhibited hDNMT1 in vitro, one of which also showed inhibition in vivo corroborating the screening procedure. This study thus identifies and attempts to validate for the first time a unique site of hDNMT1 that could be harnessed for rationally designing highly selective and potent hypomethylating agents.

Computational fishing of new DNA methyltransferase inhibitors from natural products

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

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.

Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening

The DNA methyltransferases (DNMTs) found in mammals include DNMT1, DNMT3A, and DNMT3B and are attractive targets in cancer chemotherapy. DNMT1 was the first among the DNMTs to be characterized, and it is responsible for maintaining DNA methylation patterns. A number of DNMT inhibitors have been reported, but most of them are nucleoside analogs that can lead to toxic side effects and lack specificity. By combining docking-based virtual screening with biochemical analyses, we identified a novel compound, DC_05. DC_05 is a non-nucleoside DNMT1 inhibitor with low micromolar IC50 values and significant selectivity toward other AdoMet-dependent protein methyltransferases. Through a process of similarity-based analog searching, compounds DC_501 and DC_517 were found to be more potent than DC_05. These three potent compounds significantly inhibited cancer cell proliferation. The structure-activity relationship (SAR) and binding modes of these inhibitors were also analyzed to assist in the future development of more potent and more specific DNMT1 inhibitors.

Design, synthesis and biological evaluation of 4-amino-N- (4-aminophenyl)benzamide analogues of quinoline-based SGI-1027 as inhibitors of DNA methylation

Quinoline derivative SGI-1027 (N-(4-(2-amino-6-methylpyrimidin-4-ylamino)phenyl)-4-(quinolin-4-ylamino)benzamide) was first described in 2009 as a potent inhibitor of DNA methyltransferase (DNMT) 1, 3A and 3B. Based on molecular modeling studies, performed using the crystal structure of Haemophilus haemolyticus cytosine-5 DNA methyltransferase (MHhaI C5 DNMT), which suggested that the quinoline and the aminopyridimine moieties of SGI-1027 are important for interaction with the substrates and protein, we designed and synthesized 25 derivatives. Among them, four compounds—namely the derivatives 12, 16, 31 and 32—exhibited activities comparable to that of the parent compound. Further evaluation revealed that these compounds were more potent against human DNMT3A than against human DNMT1 and induced the re-expression of a reporter gene, controlled by a methylated cytomegalovirus (CMV) promoter, in leukemia KG-1 cells. These compounds possessed cytotoxicity against leukemia KG-1 cells in the micromolar range, comparable with the cytotoxicity of the reference compound, SGI-1027. Structure–activity relationships were elucidated from the results. First, the presence of a methylene or carbonyl group to conjugate the quinoline moiety decreased the activity. Second, the size and nature of the aromatic or heterocycle subsitutents effects inhibition activity: tricyclic moieties, such as acridine, were found to decrease activity, while bicyclic substituents, such as quinoline, were well tolerated. The best combination was found to be a bicyclic substituent on one side of the compound, and a one-ring moiety on the other side. Finally, the orientation of the central amide bond was found to have little effect on the biological activity. This study provides new insights in to the structure-activity relationships of SGI-1027 and its derivative.

Rationalization of activity cliffs of a sulfonamide inhibitor of DNA methyltransferases with induced-fit docking

Inhibitors of human DNA methyltransferases (DNMT) are of increasing interest to develop novel epi-drugs for the treatment of cancer and other diseases. As the number of compounds with reported DNMT inhibition is increasing, molecular docking is shedding light to elucidate their mechanism of action and further interpret structure–activity relationships. Herein, we present a structure-based rationalization of the activity of SW155246, a distinct sulfonamide compound recently reported as an inhibitor of human DNMT1 obtained from high-throughput screening. We used flexible and induce-fit docking to develop a binding model of SW155246 with a crystallographic structure of human DNMT1. Results were in excellent agreement with experimental information providing a three-dimensional structural interpretation of ‘activity cliffs’, e.g., analogues of SW155246 with a high structural similarity to the sulfonamide compound, but with no activity in the enzymatic assay.