Histone Deacetylase Inhibitors: Structure-Based Modeling and Isoform-Selectivity Prediction

Bovine Serum Amine Oxidase and Polyamine Analogues: Chemical Synthesis and Biological Evaluation Integrated with Molecular Docking and 3-D QSAR Studies

Natural polyamines (PAs) are key players in cellular homeostasis by regulating cell growth and proliferation. Several observations highlight that PAs are also implicated in pathways regulating cell death. Indeed, the PA accumulation cytotoxic effect, maximized with the use of bovine serum amine oxidase (BSAO) enzyme, represents a valuable strategy against tumor progression. In the present study, along with the design, synthesis, and biological evaluation of a series of new spermine (Spm) analogues (1-23), a mixed structure-based (SB) and ligand-based (LB) protocol was applied. Binding modes of BSAO-PA modeled complexes led to clarify electrostatic and steric features likely affecting the BSAO-PA biochemical kinetics. LB and SB three-dimensional quantitative structure-activity relationship (Py-CoMFA and Py-ComBinE) models were developed by means of the 3d-qsar.com portal, and their analysis represents a strong basis for future design and synthesis of PA BSAO substrates for potential application in oxidative stress-induced chemotherapy.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

The main protease (M^pro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure–activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized M^pro-inhibitors and validated on available literature data. By means of deep optimization both models’ goodness and robustness reached final statistical values of r²/q² values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEP_PRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure–activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral M^pro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

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.

Computer-Aided Drug Design in Epigenetics

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

Combinations of isoform-targeted histone deacetylase inhibitors and bryostatin analogues display remarkable potency to activate latent HIV without global T-cell activation

Current antiretroviral therapy (ART) for HIV/AIDS slows disease progression by reducing viral loads and increasing CD4 counts. Yet ART is not curative due to the persistence of CD4+ T-cell proviral reservoirs that chronically resupply active virus. Elimination of these reservoirs through the administration of synergistic combinations of latency reversing agents (LRAs), such as histone deacetylase (HDAC) inhibitors and protein kinase C (PKC) modulators, provides a promising strategy to reduce if not eradicate the viral reservoir. Here, we demonstrate that largazole and its analogues are isoform-targeted histone deacetylase inhibitors and potent LRAs. Significantly, these isoform-targeted HDAC inhibitors synergize with PKC modulators, namely bryostatin-1 analogues (bryologs). Implementation of this unprecedented LRA combination induces HIV-1 reactivation to unparalleled levels and avoids global T-cell activation within resting CD4+ T-cells.

The Search for Potent, Small-Molecule HDACIs in Cancer Treatment: A Decade After Vorinostat

Histone deacetylases (HDACs) play a crucial role in the remodeling of chromatin, and are involved in the epigenetic regulation of gene expression. In the last decade, inhibition of HDACs came out as a target for specific epigenetic changes associated with cancer and other diseases. Until now, more than 20 HDAC inhibitors (HDACIs) have entered clinical studies, and some of them (e.g., vorinostat, romidepsin) have been approved for the treatment of cutaneous T-cell lymphoma. This review provides an overview of current knowledge, progress, and molecular mechanisms of HDACIs, covering a period from 2011 until 2015.

Structural basis for the development of SARS 3CL protease inhibitors from a peptide mimic to an aza-decaline scaffold

Design of inhibitors against severe acute respiratory syndrome (SARS) chymotrypsin-like protease (3CL(pro) ) is a potentially important approach to fight against SARS. We have developed several synthetic inhibitors by structure-based drug design. In this report, we reveal two crystal structures of SARS 3CL(pro) complexed with two new inhibitors based on our previous work. These structures combined with six crystal structures complexed with a series of related ligands reported by us are collectively analyzed. To these eight complexes, the structural basis for inhibitor binding was analyzed by the COMBINE method, which is a chemometrical analysis optimized for the protein-ligand complex. The analysis revealed that the first two latent variables gave a cumulative contribution ratio of r(2)  = 0.971. Interestingly, scores using the second latent variables for each complex were strongly correlated with root mean square deviations (RMSDs) of side-chain heavy atoms of Met(49) from those of the intact crystal structure of SARS-3CL(pro) (r = 0.77) enlarging the S2 pocket. The substantial contribution of this side chain (∼10%) for the explanation of pIC50 s was dependent on stereochemistry and the chemical structure of the ligand adapted to the S2 pocket of the protease. Thus, starting from a substrate mimic inhibitor, a design for a central scaffold for a low molecular weight inhibitor was evaluated to develop a further potent inhibitor. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 391-403, 2016.

Inside HDACs with more selective HDAC inhibitors

Inhibitors of histone deacetylases (HDACs) are nowadays part of the therapeutic arsenal mainly against cancers, with four compounds approved by the Food and Drug Administration. During the last five years, several groups have made continuous efforts to improve this class of compounds, designing more selective compounds or compounds with multiple capacities. After a survey of the HDAC biology and structures, this review summarizes the results of the chemists working in this field, and highlights when possible the behavior of the molecules inside their targets.

An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design

Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e., by determining the docking accuracy (DA)) in order to select the most appropriate docking procedure and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different root-mean-square deviation (RMSD) formulas, scoring functions, and format results, it is both difficult and time-consuming to consistently determine and compare their predictive capabilities in order to identify the best protocol to use for the target of interest and to extrapolate the binding poses (i.e., best-docked (BD), best-cluster (BC), and best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures called Clusterizer and DockAccessor have been developed and implemented for use with some common and "free-for-academics" programs such as AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC, and BF poses as well as to perform consistent cluster and DA analyses. Clusterizer and DockAccessor (code available over the Internet) represent two novel tools to collect computationally determined poses and detect the most predictive docking approach. Herein an application to human lysine deacetylase (hKDAC) inhibitors is illustrated.

2-Benzazolyl-4-Piperazin-1-Ylsulfonylbenzenecarbohydroxamic Acids as Novel Selective Histone Deacetylase-6 Inhibitors with Antiproliferative Activity

We have screened our compound collection in an established cell based assay that measures the derepression of an epigenetically silenced transgene, the locus derepression assay. The screen led to the identification of 4-[4-(1-methylbenzimidazol-2-yl)piperazin-1-yl]sulfonylbenzenecarbohydroxamic acid (9b) as an active which was found to inhibit HDAC1. In initial structure activity relationships study, the 1-methylbenzimidazole ring was replaced by the isosteric heterocycles benzimidazole, benzoxazole, and benzothiazole and the position of the hydroxamic acid substituent on the phenyl ring was varied. Whereas compounds bearing a para substituted hydroxamic acid (9a-d) were active HDAC inhibitors, the meta substituted analogues (8a-d) were appreciably inactive. Compounds 9a-d selectively inhibited HDAC6 (IC50 = 0.1-1.0 μM) over HDAC1 (IC50 = 0.9-6 μM) and moreover, also selectively inhibited the growth of lung cancer cells vs. patient matched normal cells. The compounds induce a cell cycle arrest in the S-phase while induction of apoptosis is neglible as compared to controls. Molecular modeling studies uncovered that the MM-GBSA energy for interaction of 9a-d with HDAC6 was higher than for HDAC1 providing structural rationale for the HDAC6 selectivity.

Therapeutic potential of isoform selective HDAC inhibitors for the treatment of schizophrenia

Increasing evidence supports a role for epigenetic involvement in some of the neurobiological alterations observed in neurodegenerative and psychiatric disorders including schizophrenia. In particular, there is mounting evidence implicating dysfunction in acetylation status, a chromatin modification mediated in part by HDACs, as a possible contributing factor to certain facets of this debilitating disease. Additional data support the notion that small molecule inhibition of HDACs may provide therapeutic alternatives to treating many of the symptoms associated with schizophrenia, particularly cognitive deficits. However, the development of highly potent and selective inhibitors of the individual HDAC isoforms will be necessary to delineate the associated biological effects and test the feasibility of such an approach for this complex and chronically treated disease. Here, we summarize current evidence for the role of HDAC isoforms in schizophrenia and highlight the state of the art in developing selective inhibitors of these isoforms as a potential treatment for schizophrenia.

Epigenetic molecular recognition: a biomolecular modeling perspective

The abnormal regulation of epigenetic protein families is associated with the onset and progression of various human diseases. However, epigenetic processes remain relatively obscure at the molecular level, thus preventing the rational design of chemical therapeutics. An array of robust computational and modeling approaches can complement experiments to shed light on the complex mechanisms of epigenetic molecular recognition and can guide medicinal chemists in designing selective and potent drug molecules. Herein we present a review of studies focused on epigenetic molecular recognition from a biomolecular modeling viewpoint. Although the known epigenetic targets are numerous, this review focuses on the more limited protein families on which computational modeling has been successfully applied. Therefore, we review three main topics: 1) histone deacetylases, 2) histone demethylases, and 3) histone tail dynamics. A brief review of the biological background and biomedical relevance is presented for each topic, followed by a detailed discussion of the computational studies and their relevance.

Systematic mining of generally recognized as safe (GRAS) flavor chemicals for bioactive compounds

Bioactive food compounds can be both therapeutically and nutritionally relevant. Screening strategies are widely employed to identify bioactive compounds from edible plants. Flavor additives contained in the so-called FEMA GRAS (generally recognized as safe) list of approved flavoring ingredients is an additional source of potentially bioactive compounds. This work used the principles of molecular similarity to identify compounds with potential mood-modulating properties. The ability of certain GRAS molecules to inhibit histone deacetylase-1 (HDAC1), proposed as an important player in mood modulation, was assayed. Two GRAS chemicals were identified as HDAC1 inhibitors in the micromolar range, results similar to what was observed for the structurally related mood prescription drug valproic acid. Additional studies on bioavailability, toxicity at higher concentrations, and off-target effects are warranted. The methodology described in this work could be employed to identify potentially bioactive flavor chemicals present in the FEMA GRAS list.

Limiting assumptions in molecular modeling: electrostatics

Molecular mechanics attempts to represent intermolecular interactions in terms of classical physics. Initial efforts assumed a point charge located at the atom center and coulombic interactions. It is been recognized over multiple decades that simply representing electrostatics with a charge on each atom failed to reproduce the electrostatic potential surrounding a molecule as estimated by quantum mechanics. Molecular orbitals are not spherically symmetrical, an implicit assumption of monopole electrostatics. This perspective reviews recent evidence that requires use of multipole electrostatics and polarizability in molecular modeling.