HEMD: an integrated tool of human epigenetic enzymes and chemical modulators for therapeutics

Deciphering Depression: Epigenetic Mechanisms and Treatment Strategies

Depression, a significant mental health disorder, is under intense research scrutiny to uncover its molecular foundations. Epigenetics, which focuses on controlling gene expression without altering DNA sequences, offers promising avenues for innovative treatment. This review explores the pivotal role of epigenetics in depression, emphasizing two key aspects: (I) identifying epigenetic targets for new antidepressants and (II) using personalized medicine based on distinct epigenetic profiles, highlighting potential epigenetic focal points such as DNA methylation, histone structure alterations, and non-coding RNA molecules such as miRNAs. Variations in DNA methylation in individuals with depression provide opportunities to target genes that are associated with neuroplasticity and synaptic activity. Aberrant histone acetylation may indicate that antidepressant strategies involve enzyme modifications. Modulating miRNA levels can reshape depression-linked gene expression. The second section discusses personalized medicine based on epigenetic profiles. Analyzing these patterns could identify biomarkers associated with treatment response and susceptibility to depression, facilitating tailored treatments and proactive mental health care. Addressing ethical concerns regarding epigenetic information, such as privacy and stigmatization, is crucial in understanding the biological basis of depression. Therefore, researchers must consider these issues when examining the role of epigenetics in mental health disorders. The importance of epigenetics in depression is a critical aspect of modern medical research. These findings hold great potential for novel antidepressant medications and personalized treatments, which would significantly improve patient outcomes, and transform psychiatry. As research progresses, it is expected to uncover more complex aspects of epigenetic processes associated with depression, enhance our comprehension, and increase the effectiveness of therapies.

Epigenetic target identification strategy based on multi-feature learning

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

A Computational Approach for the Discovery of Novel DNA Methyltransferase Inhibitors

Nowadays, the explosion of knowledge in the field of epigenetics has revealed new pathways toward the treatment of multifactorial diseases, rendering the key players of the epigenetic machinery the focus of today's pharmaceutical landscape. Among epigenetic enzymes, DNA methyltransferases (DNMTs) are first studied as inhibition targets for cancer treatment. The increasing clinical interest in DNMTs has led to advanced experimental and computational strategies in the search for novel DNMT inhibitors. Considering the importance of epigenetic targets as a novel and promising pharmaceutical trend, the present study attempted to discover novel inhibitors of natural origin against DNMTs using a combination of structure and ligand-based computational approaches. Particularly, a pharmacophore-based virtual screening was performed, followed by molecular docking and molecular dynamics simulations in order to establish an accurate and robust selection methodology. Our screening protocol prioritized five natural-derived compounds, derivatives of coumarins, flavones, chalcones, benzoic acids, and phenazine, bearing completely diverse chemical scaffolds from FDA-approved "Epi-drugs". Their total DNMT inhibitory activity was evaluated, revealing promising results for the derived hits with an inhibitory activity ranging within 30-45% at 100 µM of the tested compounds.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Usefulness of Melatonin and Other Compounds as Antioxidants and Epidrugs in the Treatment of Head and Neck Cancer

Along with genetic mutations, aberrant epigenetic alterations are the initiators of head and neck cancer carcinogenesis. Currently, several drugs are being developed to correct these epigenetic alterations, known as epidrugs. Some compounds with an antioxidant effect have been shown to be effective in preventing these malignant lesions and in minimizing the complications derived from cytotoxic treatment. Furthermore, in vitro and in vivo studies show a promising role in the treatment of head and neck squamous cell carcinoma (HNSCC). This is the case of supplements with DNA methylation inhibitory function (DNMTi), such as epigallocatechin gallate, sulforaphane, and folic acid; histone deacetylase inhibitors (HDACi), such as sodium butyrate and melatonin or histone acetyltransferase inhibitors (HATi), such as curcumin. The objective of this review is to describe the role of some antioxidants and their epigenetic mechanism of action, with special emphasis on melatonin and butyric acid given their organic production, in the prevention and treatment of HNSCC.

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

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.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.

Epigenetic Modulators as Potential Multi-targeted Drugs Against Hedgehog Pathway for Treatment of Cancer

The Sonic hedgehog signalling is known to play a crucial role in regulating embryonic development, cancer stem cell maintenance and tissue patterning. Dysregulated hedgehog signalling has been reported to affect tumorigenesis and drug response in various human malignancies. Epigenetic therapy relying on DNA methyltransferase and Histone deacetylase inhibitors are being proposed as potential drug candidates considering their efficiency in preventing development of cancer progenitor cells, killing drug resistant cells and also dictating "on/off" switch of tumor suppressor genes and oncogenes. In this docking approach, epigenetic modulators were virtually screened for their efficiency in inhibiting key regulators of SHH pathway viz., sonic hedgehog, Smoothened and Gli using polypharmacological approach. The control drugs and epigenetic modulators were docked with PDB protein structures using AutoDock vina and further checked for their drug-likeness properties. Further molecular dynamics simulation using VMD and NAMD, and MMP/GBSA energy calculation were employed for verifying the stability and entropy of the ligand-receptor complex. EPZ-6438 and GSK 343 (EZH2 inhibitors), CHR 3996 and Mocetinostat (HDAC inhibitors), GSK 126 (HKMT inhibitor) and UNC 1215 (L3MBTL3 antagonist) exhibited multiple-targeted approach in modulating HH signalling. This is the first study to report these epigenetic drugs as potential multi-targeted hedgehog pathway inhibitors. Thus, epigenetic polypharmacology approach can be explored as a better alternative to challenges of acute long term toxicity and drug resistance occurring due to traditional single targeted chemotherapy in the future.

Epigenetic drug discovery: systematic assessment of chemical space

Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 10⁶ ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 10⁶ compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.

Exploring the epigenetic drug discovery landscape

INTRODUCTION

Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.

Open chemoinformatic resources to explore the structure, properties and chemical space of molecules

Open chemoinformatic servers facilitate analysis of chemical space and structure–activity relationships.

Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators

Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors ( www.tomocomd.com ) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

HEDD: the human epigenetic drug database

Epigenetic drugs are chemical compounds that target disordered post-translational modification of histone proteins and DNA through enzymes, and the recognition of these changes by adaptor proteins. Epigenetic drug-related experimental data such as gene expression probed by high-throughput sequencing, co-crystal structure probed by X-RAY diffraction and binding constants probed by bio-assay have become widely available. The mining and integration of multiple kinds of data can be beneficial to drug discovery and drug repurposing. HEMD and other epigenetic databases store comprehensively epigenetic data where users can acquire segmental information of epigenetic drugs. However, some data types such as high-throughput datasets are not provide by these databases and they do not support flexible queries for epigenetic drug-related experimental data. Therefore, in reference to HEMD and other epigenetic databases, we developed a relatively comprehensive database for human epigenetic drugs. The human epigenetic drug database (HEDD) focuses on the storage and integration of epigenetic drug datasets obtained from laboratory experiments and manually curated information. The latest release of HEDD incorporates five kinds of datasets: (i) drug, (ii) target, (iii) disease, (vi) high-throughput and (v) complex. In order to facilitate data extraction, flexible search options were built in HEDD, which allowed an unlimited condition query for specific kinds of datasets using drug names, diseases and experiment types.

Database URL:http://hedds.org/

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.

EpiDBase: a manually curated database for small molecule modulators of epigenetic landscape

We have developed EpiDBase (www.epidbase.org), an interactive database of small molecule ligands of epigenetic protein families by bringing together experimental, structural and chemoinformatic data in one place. Currently, EpiDBase encompasses 5784 unique ligands (11 422 entries) of various epigenetic markers such as writers, erasers and readers. The EpiDBase includes experimental IC₅₀ values, ligand molecular weight, hydrogen bond donor and acceptor count, XlogP, number of rotatable bonds, number of aromatic rings, InChIKey, two-dimensional and three-dimensional (3D) chemical structures. A catalog of all epidbase ligands based on the molecular weight is also provided. A structure editor is provided for 3D visualization of ligands. EpiDBase is integrated with tools like text search, disease-specific search, advanced search, substructure, and similarity analysis. Advanced analysis can be performed using substructure and OpenBabel-based chemical similarity fingerprints. The EpiDBase is curated to identify unique molecular scaffolds. Initially, molecules were selected by removing peptides, macrocycles and other complex structures and then processed for conformational sampling by generating 3D conformers. Subsequent filtering through Zinc Is Not Commercial (ZINC: a free database of commercially available compounds for virtual screening) and Lilly MedChem regular rules retained many distinctive drug-like molecules. These molecules were then analyzed for physicochemical properties using OpenBabel descriptors and clustered using various methods such as hierarchical clustering, binning partition and multidimensional scaling. EpiDBase provides comprehensive resources for further design, development and refinement of small molecule modulators of epigenetic markers.

Database URL:www.epidbase.org

Epigenetic relevant chemical space: a chemoinformatic characterization of inhibitors of DNA methyltransferases

The first comprehensive exploration of the epigenetic relevant chemical space is reported in this work with a special emphasis on inhibitors of DNA methyltransferases.

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously.

Epigenetic mechanisms in penile carcinoma

Penile carcinoma (PeCa) represents an important public health problem in poor and developing countries. Despite its unpredictable behavior and aggressive treatment, there have only been a few reports regarding its molecular data, especially epigenetic mechanisms. The functional diversity in different cell types is acquired by chromatin modifications, which are established by epigenetic regulatory mechanisms involving DNA methylation, histone acetylation, and miRNAs. Recent evidence indicates that the dysregulation in these processes can result in the development of several diseases, including cancer. Epigenetic alterations, such as the methylation of CpGs islands, may reveal candidates for the development of specific markers for cancer detection, diagnosis and prognosis. There are a few reports on the epigenetic alterations in PeCa, and most of these studies have only focused on alterations in specific genes in a limited number of cases. This review aims to provide an overview of the current knowledge of the epigenetic alterations in PeCa and the promising results in this field. The identification of epigenetically altered genes in PeCa is an important step in understanding the mechanisms involved in this unexplored disease.