Modified N-acyl-L-homoserine lactone compounds abrogate Las-dependent quorum-sensing response in human pathogen Pseudomonas aeruginosa.

Quorum sensing (QS) is a mode of cell-cell communication that bacteria use to sense population density and orchestrate collective behaviors. The common opportunistic human pathogen Pseudomonas aeruginosa employs QS to regulate a large set of genes involved in virulence and host-pathogen interactions. The Las circuit positioned on the top of the QS hierarchy in P. aeruginosa makes use of N-acyl-L-homoserine lactones (AHLs) as signal molecules, like N-3-oxo-dodecanoyl-L-homoserine lactone (3O-C12-HSL). Disabling QS circuits by certain small-molecule compounds, known as quorum-sensing inhibitors (QSIs), has been proposed as a strategy to attenuate bacterial pathogenicity. In this study, four new AHL analogs were designed by incorporating a tert-butoxycarbonyl Boc group in amide and ß-keto (3-oxo) moiety. Compounds were evaluated on a molecular and phenotypic basis as a QSI using the screening strategy linked to the assignment of the Las QS system in P. aeruginosa. Using a LasR-based bioreporter, we found that the compounds decreased LasR-controlled light activity and competed efficiently with natural 3O-C12-HSL. The compounds reduced the production of the cognate 3O-C12-HSL and certain virulence traits, like total protease activity, elastase activity, pyocyanin production, and extracellular DNA release. Furthermore, a quantitative proteomic approach was used to study the effect of the compounds on QS-regulated extracellular proteins. Among the four compounds tested, one of them showed the most significant difference in the appearance of the 3O-C12-HSL-responsive reference proteins related to QS communication and virulence, i.e., a distinct activity as a QSI. Moreover, by combining experimental data with computational chemistry, we addressed the effect of LasR protein flexibility on docking precision and assessed the advantage of using a multi-conformational docking procedure for binding mode prediction of LasR modulators. Thus, the four new AHL compounds were tested for their interaction with the AHL-binding site in LasR to identify the key interferences with the activity of LasR. Our study provides further insight into molecular features that are required for small-molecule modulation of LasR-dependent QS communication in P. aeruginosa. This should facilitate rational design of the next generation of antivirulence tools to study and manipulate QS-controlled fitness in bacteria and, thereby, handle bacterial infections in a new way.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 µs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Identification of histone deacetylase inhibitors with (arylidene)aminoxy scaffold active in uveal melanoma cell lines.

Uveal melanoma (UM) represents an aggressive type of cancer and currently, there is no effective treatment for this metastatic disease. In the last years, histone deacetylase inhibitors (HDACIs) have been studied as a possible therapeutic treatment for UM, alone or in association with other chemotherapeutic agents. Here we synthesised a series of new HDACIs based on the SAHA scaffold bearing an (arylidene)aminoxy moiety. Their HDAC inhibitory activity was evaluated on isolated human HDAC1, 3, 6, and 8 by fluorometric assay and their binding mode in the catalytic site of HDACs was studied by molecular docking. The most promising hit was the quinoline derivative VS13, a nanomolar inhibitor of HDAC6, which exhibited a good antiproliferative effect on UM cell lines at micromolar concentration and a capability to modify the mRNA levels of HDAC target genes similar to that of SAHA.

Docking Finds GPCR Ligands in Dark Chemical Matter.

High-throughput screening has revealed dark chemical matter, a set of drug-like compounds that has never shown bioactivity despite being extensively assayed. If dark molecules are found active at a therapeutic target, their extraordinary selectivity profiles make excellent starting points for drug development. We explored if ligands of therapeutically relevant G-protein-coupled receptors could be discovered by structure-based virtual screening of the dark chemical matter. Molecular docking screens against crystal structures of the A2A adenosine and the D4 dopamine receptors were carried out, and 53 top-ranked molecules were evaluated experimentally. Two ligands of each receptor were discovered, and the most potent had sub-micromolar affinities. Analysis of bioactivity data showed that the ligands lacked activity at hundreds of off-targets, including several that are associated with adverse effects. Our results demonstrate that virtual screening provides an efficient means to mine the dark chemical space, which could contribute to development of drugs with improved safety profiles.

Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection.

Main goal in drug discovery is the identification of drug-like compounds capable to modulate specific biological targets. Thus, the prediction of reliable binding poses of candidate ligands, through molecular docking simulations, represents a key step to be pursued in structure-based drug design (SBDD). Since the increasing number of resolved three-dimensional ligand-protein structures, together with the expansion of computational power and software development, the comprehensive and systematic use of experimental data can be proficiently employed to validate the docking performance. This allows to select and refine the protocol to adopt when predicting the binding pose of trial compounds in a target. Given the availability of multiple docking software, a comparative docking assessment in an early research stage represents a must-use step to minimize fails in molecular modeling. This chapter describes how to perform a docking assessment, using freely available tools, in a semiautomated fashion.

Structural Characterization of Agonist Binding to Protease-Activated Receptor 2 through Mutagenesis and Computational Modeling.

Protease-activated receptor 2 (PAR2) is a G-protein-coupled receptor that is activated by proteolytic cleavage of its N-terminus. The unmasked N-terminal peptide then binds to the transmembrane bundle, leading to activation of intracellular signaling pathways associated with inflammation and cancer. Recently determined crystal structures have revealed binding sites of PAR2 antagonists, but the binding mode of the peptide agonist remains unknown. In order to generate a model of PAR2 in complex with peptide SLIGKV, corresponding to the trypsin-exposed tethered ligand, the orthosteric binding site was probed by iterative combinations of receptor mutagenesis, agonist ligand modifications, and data-driven structural modeling. Flexible-receptor docking identified a conserved binding mode for agonists related to the endogenous ligand that was consistent with the experimental data and allowed synthesis of a novel peptide (1-benzyl-1H[1,2,3]triazole-4-yl-LIGKV) with functional potency higher than that of SLIGKV. The final model may be used to understand the structural basis of PAR2 activation and in virtual screens to identify novel agonists and competitive antagonists. The combined experimental and computational approach to characterize agonist binding to PAR2 can be extended to study the many other G protein-coupled receptors that recognize peptides or proteins.

Limiting Assumptions in the Design of Peptidomimetics.

Preclinical Research Limiting the flexibility of organic compounds to enhance their affinity and selectivity for targeting a macromolecule involved in molecular recognition has become a well-developed paradigm in medicinal chemistry. While the role of reverse-turn motifs as peptidomimetics has received the most attention, ß-sheets and helices are also important motifs for protein/protein interactions. The more complicated problem of mimicking the interacting surface of noncontiguous epitopes will not be considered in this review. This limited overview focuses on efforts to use amino acid synthons as secondary-structure mimetics as well as providing examples of peptidomimetic design focused on nonpeptide synthetic chemistry in contrast. In particular, the rationale of optimal design criteria for mimicry and the many naïve violations of those criteria made in its pursuit are emphasized. Drug Dev Res 78 : 245-267, 2017. © 2017 Wiley Periodicals, Inc.

Structural insights of SmKDAC8 inhibitors: Targeting Schistosoma epigenetics through a combined structure-based 3D QSAR, in vitro and synthesis strategy.

A predictive structure-based 3D QSAR (COMBINEr 2.0) model of the Schistosoma mansoni lysine deacetylase 8 enzyme (SmKDAC8) was developed, validated and used to perform virtual screening (VS) of the NCI Diversity Set V database (1593 compounds). Three external datasets (with congeneric structures to those experimentally resolved in complexes by X-ray and previously reported as SmKDAC8 inhibitors) were employed to compose and validate the most predictive model. Two series characterized by 104 benzodiazepine derivatives (BZDs) and 60 simplified largazole analogs (SLAs), recently reported by our group as human KDAC inhibitors, were tested for their inhibition potency against SmKDAC8 to probe the predictive capability of the quantitative models against compounds with diverse structures. The SmKDAC8 biochemical results confirmed: (1) the benzodiazepine moiety as a valuable scaffold to further investigate when pursuing SmKDAC8 inhibition; (2) the predictive capability of the COMBINEr 2.0 model towards non-congeneric series of compounds, highlighting the most influencing ligand-protein interactions and refining the structure-activity relationships. From the VS investigations, the first 40 top-ranked compounds were obtained and biologically tested for their inhibition potency against SmKDAC8 and hKDACs 1, 3, 6 and 8. Among them, a non-hydroxamic acid benzothiadiazine dioxide derivative (code NSC163639), showed interesting activity and selectivity against SmKDAC8. To further elucidate the structure-activity relationships of NSC163639, two analogs (herein reported as compounds 3 and 4) were synthesized and biologically evaluated. Results suggest the benzothiadiazine dioxide moiety as a promising scaffold to be used in a next step to derive selective SmKDAC8 inhibitors.

Design and synthesis of benzodiazepine analogs as isoform-selective human lysine deacetylase inhibitors.

A comprehensive investigation was performed to identify new benzodiazepine (BZD) derivatives as potent and selective human lysine deacetylase inhibitors (hKDACis). A total of 108 BZD compounds were designed, synthesized and from that 104 compounds were biologically evaluated against human lysine deacetylases (hKDACs) 1, 3 and 8 (class I) and 6 (class IIb). The most active compounds showed mid-nanomolar potencies against hKDACs 1, 3 and 6 and micromolar activity against hKDAC8, while a promising compound (6q) showed selectivity towards hKDAC3 among the different enzyme isoforms. An hKDAC6 homology model, refined by molecular dynamics simulation was generated, and molecular docking studies performed to rationalize the dominant ligand-residue interactions as well as to define structure-activity-relationships. Experimental results confirmed the usefulness of the benzodiazepine moiety as capping group when pursuing hKDAC isoform-selectivity inhibition, suggesting its continued use when designing new hKDACis.

Design and Synthesis of Simplified Largazole Analogues as Isoform-Selective Human Lysine Deacetylase Inhibitors.

Selective inhibition of KDAC isoforms while maintaining potency remains a challenge. Using the largazole macrocyclic depsipeptide structure as a starting point for developing new KDACIs with increased selectivity, a combination of four different simplified largazole analogue (SLA) scaffolds with diverse zinc-binding groups (for a total of 60 compounds) were designed, synthesized, and evaluated against class I KDACs 1, 3, and 8, and class II KDAC6. Experimental evidence as well as molecular docking poses converged to establish the cyclic tetrapeptides (CTPs) as the primary determinant of both potency and selectivity by influencing the correct alignment of the zinc-binding group in the KDAC active site, providing a further basis for developing new KDACIs of higher isoform selectivity and potency.

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.

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches.

Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

Exploring the role of 2-chloro-6-fluoro substitution in 2-alkylthio-6-benzyl-5-alkylpyrimidin-4(3H)-ones: effects in HIV-1-infected cells and in HIV-1 reverse transcriptase enzymes.

A comparison of the effects of the 6-(2-chloro-6-fluorobenzyl)-2-(alkylthio)pyrimidin-4(3H)-ones (2-Cl-6-F-S-DABOs) 7-12 and the related 6-(2,6-difluorobenzyl) counterparts 13-15 in HIV-1 infected cells and in the HIV-1 reverse transcriptase (RT) assays is here described. The new 2-Cl-6-F-S-DABOs showed up to picomolar activity against wt HIV-1. Against clinically relevant HIV-1 mutants and in enzyme assays, the simultaneous C5(methyl)/C6(methyl/ethyl) substitution in the 2-Cl-6-F- and 2,6-F2-benzyl series furnished compounds with the highest, wide-spectrum inhibitory activity against HIV-1. Three representative 2-Cl-6-F-S-DABOs carrying two (9c, 10c) or one (10a) stereogenic centers were resolved into their individual stereoisomers and showed a significant diastereo- and enantioselectivity in HIV-1 inhibition, the highest antiviral activity well correlating with the R absolute configuration to the stereogenic center of the C6-benzylic position in both cellular and enzymatic tests. Application of previously reported COMBINEr protocol on 9c and 10c confirmed the influence of the stereogenic centers on their binding modes in the HIV-1 RT.

Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening.

The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.

Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application.

Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 µM as hits for a subsequent medicinal chemistry optimization procedure.

Pharmacophore assessment through 3-D QSAR: evaluation of the predictive ability on new derivatives by the application on a series of antitubercular agents.

Pharmacophoric mapping is a useful procedure to frame, especially when crystallographic receptor structures are unavailable as in ligand-based studies, the hypothetical site of interaction. In this study, 71 pyrrole derivatives active against M. tuberculosis were used to derive through a recent new 3-D QSAR protocol, 3-D QSAutogrid/R, several predictive 3-D QSAR models on compounds aligned by a previously reported pharmacophoric application. A final multiprobe (MP) 3-D QSAR model was then obtained configuring itself as a tool to derive pharmacophoric quantitative models. To stress the applicability of the described models, an external test set of unrelated and newly synthesized series of R-4-amino-3-isoxazolidinone derivatives found to be active at micromolar level against M. tuberculosis was used, and the predicted bioactivities were in good agreement with the experimental values. The 3-D QSAutogrid/R procedure proved to be able to correlate by a single multi-informative scenario the different activity molecular profiles thus confirming its usefulness in the rational drug design approach.

Design, synthesis and biological evaluation of new classes of thieno[3,2-d]pyrimidinone and thieno[1,2,3]triazine as inhibitor of vascular endothelial growth factor receptor-2 (VEGFR-2).

Driven by a multidisciplinary approach combination (Structure-Based (SB) Three-Dimensional Quantitative Structure-Activity Relationships (3-D QSAR), molecular modeling, organic chemistry and various biological evaluations) here is reported the disclosure of new thienopyrimidines 1-3 as inhibitors of KDR activity and human umbilical vein endothelial cell (HUVEC) proliferation. More specifically, compound 2f represents a new lead compound that inhibits VEGFR-2 and HUVEC at µM concentration. Moreover by the mean of an endothelial cell tube formation in vitro model 2f tartaric acid salt proved to block angiogenesis of HUVEC at µM level.

Comprehensive model of wild-type and mutant HIV-1 reverse transciptases.

An enhanced version of COMBINE that uses both ligand-based and structure-based alignment of ligands has been used to build a comprehensive 3-D QSAR model of wild-type HIV-1 reverse transcriptase and drug-resistant mutants. The COMBINEr model focused on 7 different RT enzymes complexed with just two HIV-RT inhibitors, niverapine (NVP) and efavirenz (EFV); therefore, 14 inhibitor/enzyme complexes comprised the training set. An external test set of chiral 2-(alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones (DABOs) was used to test predictability. The COMBINEr model MC4, although developed using only two inhibitors, predicted the experimental activities of the test set with an acceptable average absolute error of prediction (0.89 pK (i)). Most notably, the model was able to correctly predict the right eudismic ratio for two R/S pairs of DABO derivatives. The enhanced COMBINEr approach was developed using only software freely available to academics.

Histone deacetylase inhibitors: structure-based modeling and isoform-selectivity prediction.

An enhanced version of comparative binding energy (COMBINE) analysis, named COMBINEr, based on both ligand-based and structure-based alignments has been used to build several 3-D QSAR models for the eleven human zinc-based histone deacetylases (HDACs). When faced with an abundance of data from diverse structure-activity sources, choosing the best paradigm for an integrative analysis is difficult. A common example from studies on enzyme-inhibitors is the abundance of crystal structures characterized by diverse ligands complexed with different enzyme isoforms. A novel comprehensive tool for data mining on such inhomogeneous set of structure-activity data was developed based on the original approach of Ortiz, Gago, and Wade, and applied to predict HDAC inhibitors' isoform selectivity. The COMBINEr approach (apart from the AMBER programs) has been developed to use only software freely available to academics.