Histone H3 lysine 27 crotonylation mediates gene transcriptional repression in chromatin.

Histone lysine acylation, including acetylation and crotonylation, plays a pivotal role in gene transcription in health and diseases. However, our understanding of histone lysine acylation has been limited to gene transcriptional activation. Here, we report that histone H3 lysine 27 crotonylation (H3K27cr) directs gene transcriptional repression rather than activation. Specifically, H3K27cr in chromatin is selectively recognized by the YEATS domain of GAS41 in complex with SIN3A-HDAC1 co-repressors. Proto-oncogenic transcription factor MYC recruits GAS41/SIN3A-HDAC1 complex to repress genes in chromatin, including cell-cycle inhibitor p21. GAS41 knockout or H3K27cr-binding depletion results in p21 de-repression, cell-cycle arrest, and tumor growth inhibition in mice, explaining a causal relationship between GAS41 and MYC gene amplification and p21 downregulation in colorectal cancer. Our study suggests that H3K27 crotonylation signifies a previously unrecognized, distinct chromatin state for gene transcriptional repression in contrast to H3K27 trimethylation for transcriptional silencing and H3K27 acetylation for transcriptional activation.

HIPK2 directs cell type-specific regulation of STAT3 transcriptional activity in Th17 cell differentiation.

SignificanceSTAT3 (signal transducer and activator of transcription 3) is a master transcription factor that organizes cellular responses to cytokines and growth factors and is implicated in inflammatory disorders. STAT3 is a well-recognized therapeutic target for human cancer and inflammatory disorders, but how its function is regulated in a cell type-specific manner has been a major outstanding question. We discovered that Stat3 imposes self-directed regulation through controlling transcription of its own regulator homeodomain-interacting protein kinase 2 (Hipk2) in a T helper 17 (Th17) cell-specific manner. Our validation of the functional importance of the Stat3-Hipk2 axis in Th17 cell development in the pathogenesis of T cell-induced colitis in mice suggests an approach to therapeutically treat inflammatory bowel diseases that currently lack a safe and effective therapy.

BRADSHAW: a system for automated molecular design.

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Correction to: BRADSHAW: a system for automated molecular design.

The original version of this article unfortunately contained some mistakes in the references.

Discovery and Lead-Optimization of 4,5-Dihydropyrazoles as Mono-Kinase Selective, Orally Bioavailable and Efficacious Inhibitors of Receptor Interacting Protein 1 (RIP1) Kinase.

RIP1 kinase regulates necroptosis and inflammation and may play an important role in contributing to a variety of human pathologies, including inflammatory and neurological diseases. Currently, RIP1 kinase inhibitors have advanced into early clinical trials for evaluation in inflammatory diseases such as psoriasis, rheumatoid arthritis, and ulcerative colitis and neurological diseases such as amyotrophic lateral sclerosis and Alzheimer's disease. In this paper, we report on the design of potent and highly selective dihydropyrazole (DHP) RIP1 kinase inhibitors starting from a high-throughput screen and the lead-optimization of this series from a lead with minimal rat oral exposure to the identification of dihydropyrazole 77 with good pharmacokinetic profiles in multiple species. Additionally, we identified a potent murine RIP1 kinase inhibitor 76 as a valuable in vivo tool molecule suitable for evaluating the role of RIP1 kinase in chronic models of disease. DHP 76 showed efficacy in mouse models of both multiple sclerosis and human retinitis pigmentosa.

Spatially constrained tandem bromodomain inhibition bolsters sustained repression of BRD4 transcriptional activity for TNBC cell growth.

The importance of BET protein BRD4 in gene transcription is well recognized through the study of chemical modulation of its characteristic tandem bromodomain (BrD) binding to lysine-acetylated histones and transcription factors. However, while monovalent inhibition of BRD4 by BET BrD inhibitors such as JQ1 blocks growth of hematopoietic cancers, it is much less effective generally in solid tumors. Here, we report a thienodiazepine-based bivalent BrD inhibitor, MS645, that affords spatially constrained tandem BrD inhibition and consequently sustained repression of BRD4 transcriptional activity in blocking proliferation of solid-tumor cells including a panel of triple-negative breast cancer (TNBC) cells. MS645 blocks BRD4 binding to transcription enhancer/mediator proteins MED1 and YY1 with potency superior to monovalent BET inhibitors, resulting in down-regulation of proinflammatory cytokines and genes for cell-cycle control and DNA damage repair that are largely unaffected by monovalent BrD inhibition. Our study suggests a therapeutic strategy to maximally control BRD4 activity for rapid growth of solid-tumor TNBC cells.

BET N-terminal bromodomain inhibition selectively blocks Th17 cell differentiation and ameliorates colitis in mice.

T-helper 17 (Th17) cells have important functions in adaptor immunity and have also been implicated in inflammatory disorders. The bromodomain and extraterminal domain (BET) family proteins regulate gene transcription during lineage-specific differentiation of naïve CD4+ T cells to produce mature T-helper cells. Inhibition of acetyl-lysine binding of the BET proteins by pan-BET bromodomain (BrD) inhibitors, such as JQ1, broadly affects differentiation of Th17, Th1, and Th2 cells that have distinct immune functions, thus limiting their therapeutic potential. Whether these BET proteins represent viable new epigenetic drug targets for inflammatory disorders has remained an unanswered question. In this study, we report that selective inhibition of the first bromodomain of BET proteins with our newly designed small molecule MS402 inhibits primarily Th17 cell differentiation with a little or almost no effect on Th1 or Th2 and Treg cells. MS402 preferentially renders Brd4 binding to Th17 signature gene loci over those of housekeeping genes and reduces Brd4 recruitment of p-TEFb to phosphorylate and activate RNA polymerase II for transcription elongation. We further show that MS402 prevents and ameliorates T-cell transfer-induced colitis in mice by blocking Th17 cell overdevelopment. Thus, selective pharmacological modulation of individual bromodomains likely represents a strategy for treatment of inflammatory bowel diseases.

Structural features and inhibitors of bromodomains.

Bromodomains are conserved structural modules responsible for recognizing acetylated-lysine residues on histone tails and other transcription-associated proteins, such as transcription factors and co-factors. Owing to their important functions in the regulation of ordered gene transcription in chromatin, bromodomains of the BET family proteins have recently been shown as druggable targets for a wide array of human diseases, including cancer and inflammation. Here we review the structural and functional features of the bromodomains and their small-molecule inhibitors. Additional new insights provided herein highlight the landscape of the ligand binding sites in the bromodomains that will hopefully facilitate further development of new inhibitors with optimal affinity and selectivity.

Structure-Activity Relationship Studies for Enhancer of Zeste Homologue 2 (EZH2) and Enhancer of Zeste Homologue 1 (EZH1) Inhibitors.

EZH2 or EZH1 (enhancer of zeste homologue 2 or 1) is the catalytic subunit of polycomb repressive complex 2 (PRC2) that catalyzes methylation of histone H3 lysine 27 (H3K27). PRC2 hyperactivity and/or hypertrimethylation of H3K27 are associated with numerous human cancers, therefore inhibition of PRC2 complex has emerged as a promising therapeutic approach. Recent studies have shown that EZH2 and EZH1 are not functionally redundant and inhibition of both EZH2 and EZH1 is necessary to block the progression of certain cancers such as mixed-lineage leukemia (MLL)-rearranged leukemias. Despite the significant advances in discovery of EZH2 inhibitors, there has not been a systematic structure-activity relationship (SAR) study to investigate the selectivity between EZH2 and EZH1 inhibition. Here, we report our SAR studies that focus on modifications to various regions of the EZH2/1 inhibitor UNC1999 (5) to investigate the impact of the structural changes on EZH2 and EZH1 inhibition and selectivity.

The bromodomain: from epigenome reader to druggable target.

Lysine acetylation is a fundamental post-translational modification that plays an important role in the control of gene transcription in chromatin in an ordered fashion. The bromodomain, the conserved structural module present in transcription-associated proteins, functions exclusively to recognize acetyl-lysine on histones and non-histone proteins. The structural analyses of bromodomains' recognition of lysine-acetylated peptides derived from histones and cellular proteins provide detailed insights into the differences and unifying features of biological ligand binding selectivity by the bromodomains. Newly developed small-molecule inhibitors targeting bromodomain proteins further highlight the functional importance of bromodomain/acetyl-lysine binding as a key mechanism in orchestrating molecular interactions and regulation in chromatin biology and gene transcription. These new studies argue that modulating bromodomain/acetyl-lysine interactions with small-molecule chemicals offer new opportunities to control gene expression in a wide array of human diseases including cancer and inflammation. This article is part of a Special Issue entitled: Molecular mechanisms of histone modification function.

ChEpiMod: a knowledgebase for chemical modulators of epigenome reader domains.

CONTEXT: Epigenome reader domains are rapidly emerging as a new class of drug targets for a wide array of human diseases. To facilitate study of structure-activity relationship and small-molecule ligand design for these domains, we have created ChEpiMod. ChEpiMod is a free knowledgebase of chemical modulators with documented modulatory activity for epigenome reader domains. METHODS: ChEpiMod organizes information about chemical modulators and their associated binding-affinity data, as well as available structures of epigenome readers from the Protein Data Bank. The data are gathered from the literature and patents. Entries are supplemented by annotation. The current version of ChEpiMod covers six epigenome reader domain families (Bromodomain, PHD finger, Chromodomain, MBT, PWWP and Tudor). The database can be used to browse existing chemical modulators and bioactivity data, as well as, all available structures of readers and their molecular interactions. The database is updated weekly. AVAILABILITY: ChEpiMod is freely available at http://chepimod.org CONTACT: ming-ming.zhou@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Computational profiling of bioactive compounds using a target-dependent composite workflow.

Computational target fishing is a chemoinformatic method aimed at determining main and secondary targets of bioactive compounds in order to explain their mechanism of action, anticipate potential side effects, or repurpose existing drugs for novel therapeutic indications. Many existing successes in this area have been based on a use of a single computational method to estimate potentially new target-ligand associations. We herewith present an automated workflow using several methods to optimally browse target-ligand space according to existing knowledge on either ligand and target space under investigation. The protocol uses four ligand-based (SVM classification, SVR affinity prediction, nearest neighbors interpolation, shape similarity) and two structure-based approaches (docking, protein-ligand pharmacophore match) in series, according to well-defined ligand and target property checks. The workflow was remarkably accurate (72%) in identifying the main target of 189 clinical candidates and proposed two novel off-targets which could be experimentally validated. Rolofylline, an adenosine A1 receptor antagonist, was confirmed to inhibit phosphodiesterase 5 with a moderate affinity (IC50 = 13.8 µM). More interestingly, we describe a strong binding (IC50 = 142 nM) of a claimed selective phosphodiesterase 10 A inhibitor (PF-2545920) with the cysteinyl leukotriene type 1 G protein-coupled receptor.

Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling.

Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets. A database of 68,056 pharmacophores was first derived from 8,166 high-resolution protein-ligand complexes. In order to limit the number of queries, a maximum of 10 pharmacophores was generated for each complex according to their predicted selectivity. Pharmacophore search was compared to ligand-centric (2-D and 3-D similarity searches) and docking methods in profiling a set of 157 diverse ligands against a panel of 2,556 unique targets of known X-ray structure. As expected, ligand-based methods outperformed, in most of the cases, structure-based approaches in ranking the true targets among the top 1% scoring entries. However, we could identify ligands for which only a single method was successful. Receptor-ligand-based pharmacophore search is notably a fast and reliable alternative to docking when few ligand information is available for some targets. Overall, the present study suggests that a workflow using the best profiling method according to the protein-ligand context is the best strategy to follow. We notably present concrete guidelines for selecting the optimal computational method according to simple ligand and binding site properties.

Enhancing the accuracy of chemogenomic models with a three-dimensional binding site kernel.

Computational chemogenomic (or proteochemometric) methods predict target-ligand interactions by training machine learning algorithms on known experimental data in order to distinguish attributes of true from false target-ligand pairs. Many ligand and target descriptors can be used for training and predicting binary associations or even binding affinities. Several chemogenomic studies have not noticed any real benefit in using 3-D structural target descriptors with respect to simpler sequence-based or property-based information. To assess whether this observation results from inaccurate target description or from the fact that 3-D information is simply not required in chemogenomic modeling, we used a target kernel measuring the distance between target-ligand binding sites of known X-ray structures. When used in combination with a standard ligand kernel in a support vector machine (SVM) classifier, the 3-D target kernel significantly outperforms a sequence-based target kernel in discriminating 2882 target-ligand PDB complexes from 9128 false pairs, whatever the modeling procedure (local or global). The best SVM models could be successfully applied to predict, with very high recall (70%), precision (99%), and specificity (99%), target-ligand associations for an external set of 14,117 ligands and 531 targets. In most of the cases, pooling all data in a global model gave better statistics than just discretizing specific target-ligand subspaces in local models. The current study clearly demonstrates that chemogenomic models taking both ligand and target information outperform simpler ligand-based models. It also permits one to design good modeling practices in predicting target-ligand pairing for a large array of targets: (i) ligand-based models are precise enough if sufficient ligand information (>40-50 diverse ligands) is known; (ii) if not, structure-based chemogenomic models (associating a ligand kernel to a structure-based target kernel) are recommended for proteins of known holostructures; (iii) sequence-based chemogenomic models (associating a ligand kernel to a sequence-based target kernel) can still be used with a very good accuracy for the remaining targets.

sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins.

BACKGROUND: The sc-PDB database is an annotated archive of druggable binding sites extracted from the Protein Data Bank. It contains all-atoms coordinates for 8166 protein-ligand complexes, chosen for their geometrical and physico-chemical properties. The sc-PDB provides a functional annotation for proteins, a chemical description for ligands and the detailed intermolecular interactions for complexes. The sc-PDB now includes a hierarchical classification of all the binding sites within a functional class. METHOD: The sc-PDB entries were first clustered according to the protein name indifferent of the species. For each cluster, we identified dissimilar sites (e.g. catalytic and allosteric sites of an enzyme). SCOPE AND APPLICATIONS: The classification of sc-PDB targets by binding site diversity was intended to facilitate chemogenomics approaches to drug design. In ligand-based approaches, it avoids comparing ligands that do not share the same binding site. In structure-based approaches, it permits to quantitatively evaluate the diversity of the binding site definition (variations in size, sequence and/or structure). AVAILABILITY: The sc-PDB database is freely available at: http://bioinfo-pharma.u-strasbg.fr/scPDB.

Assessing the geometric diversity of cytochrome P450 ligand conformers by hierarchical clustering with a stop criterion.

An algorithm is presented, which exhibits a computed number of rigid conformers of an input small molecule, covering the geometric diversity in the conformational space, with minimal structural redundancy. The algorithm calls a conformer generator, then performs an agglomerative hierarchical clustering with the modified clustering gain as the stop criterion. The number of classes is computed without an arbitrary parameter. A representative conformer is selected in each class, and nonrepresentative conformers are discarded. For illustration, the algorithm has been applied on a database containing 70 ligands of the cytochrome CYP 3A4, showing that the structural flexibility of each ligand is indeed handled via a small number of its representative conformers. The method is valid for all small molecules.