An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

Expanding the Structural Diversity of DNA Methyltransferase Inhibitors.

Inhibitors of DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug discovery. They are also chemical tools to understand the biochemistry of epigenetic processes. Herein, we report five distinct inhibitors of DNMT1 characterized in enzymatic inhibition assays that did not show activity with DNMT3B. It was concluded that the dietary component theaflavin is an inhibitor of DNMT1. Two additional novel inhibitors of DNMT1 are the approved drugs glyburide and panobinostat. The DNMT1 enzymatic inhibitory activity of panobinostat, a known pan inhibitor of histone deacetylases, agrees with experimental reports of its ability to reduce DNMT1 activity in liver cancer cell lines. Molecular docking of the active compounds with DNMT1, and re-scoring with the recently developed extended connectivity interaction features approach, led to an excellent agreement between the experimental IC50 values and docking scores.

Recent progress on cheminformatics approaches to epigenetic drug discovery.

The ability of epigenetic markers to affect genome function has enabled transformative changes in drug discovery, especially in cancer and other emerging therapeutic areas. Concordant with the introduction of the term 'epi-informatics', the size of the epigenetically relevant chemical space has grown substantially and so did the number of applications of cheminformatic methods to epigenetics. Recent progress in epi-informatics has improved our understanding of the structure-epigenetic activity relationships and boosted the development of models predicting novel epigenetic agents. Herein, we review the advances in computational approaches to drug discovery of small molecules with epigenetic modulation profiles, summarize the current chemogenomics data available for epigenetic targets, and provide a perspective on the greater utility of biomedical knowledge mining as a means to advance the epigenetic drug discovery.

Current advances on the development of BET inhibitors: insights from computational methods.

Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.

In silico tools to study molecular targets of neglected diseases: inhibition of TcSir2rp3, an epigenetic enzyme of Trypanosoma cruzi.

There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 µM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.

Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1.

In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine methyltransferase (G9a) and DNA methyltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity differences. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.

Flavonoids as Putative Epi-Modulators: Insight into Their Binding Mode with BRD4 Bromodomains Using Molecular Docking and Dynamics.

Flavonoids are widely recognized as natural polydrugs, given their anti-inflammatory, antioxidant, sedative, and antineoplastic activities. Recently, different studies showed that flavonoids have the potential to inhibit bromodomain and extraterminal (BET) bromodomains. Previous reports suggested that flavonoids bind between the Z and A loops of the bromodomain (ZA channel) due to their orientation and interactions with P86, V87, L92, L94, and N140. Herein, a comprehensive characterization of the binding modes of fisetin and the biflavonoid, amentoflavone, is discussed. To this end, both compounds were docked with BET bromodomain 4 (BRD4) using four docking programs. The results were post-processed with protein⁻ligand interaction fingerprints. To gain further insight into the binding mode of the two natural products, the docking results were further analyzed with molecular dynamics simulations. The results showed that amentoflavone makes numerous contacts in the ZA channel, as previously described for flavonoids and kinase inhibitors. It was also found that amentoflavone can potentially make contacts with non-canonical residues for BET inhibition. Most of these contacts were not observed with fisetin. Based on these results, amentoflavone was experimentally tested for BRD4 inhibition, showing activity in the micromolar range. This work may serve as the basis for scaffold optimization and the further characterization of flavonoids as BET inhibitors.

Chemoinformatic expedition of the chemical space of fungal products.

AIM: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. RESULTS: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. CONCLUSION: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.