Ligand-, structure- and pharmacophore-based molecular fingerprints: a case study on adenosine A1, A2A, A2B, and A3 receptor antagonists

Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection

Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.

Inhibitors of ABCG2-mediated multidrug resistance: Lead generation through computer-aided drug design

Human breast cancer resistance protein (BCRP), known also as ABCG2, plays a major role in multiple drug resistance (MDR) in tumor cells. Through this ABC transporter, cancer cells acquire the ability of resistance to structurally and functionally unrelated anticancer drugs. Nowadays, the design of ABCG2 inhibitors as potential agents to enhance the chemotherapy efficacy is an interesting strategy. In this context, we have used computer-aided drug design (CADD) based on available data of a large series of potent inhibitors from our groups as an approach in guiding the design of effective ABCG2 inhibitors. We report therein the results on the use of the FLAPpharm method to elucidate the pharmacophoric features of one of the ABCG2 binding sites involved in the regulation of the basal ATPase activity of the transporter. The predictivity of the model was evaluated by testing three predicted compounds which were found to induce high inhibitory activity of BCRP, in the nanomolar range for the best of them.

Pharmacophore-Based Discovery of Substrates of a Novel Drug/Proton-Antiporter in the Human Brain Endothelial hCMEC/D3 Cell Line

A drug/proton-antiporter, whose the molecular structure is still unknown, was previously evidenced at the blood-brain barrier (BBB) by functional experiments. The computational method could help in the identification of substrates of this solute carrier (SLC) transporter. Two pharmacophore models for substrates of this transporter using the FLAPpharm approach were developed. The trans-stimulation potency of 40 selected compounds for already known specific substrates ([3H]-clonidine) were determined and compared in the human brain endothelial cell line hCMEC/D3. Results. The two pharmacophore models obtained were used as templates to screen xenobiotic and endogenous compounds from four databases (e.g., Specs), and 45 hypothetical new candidates were tested to determine their substrate capacity. Psychoactive drugs such as antidepressants (e.g., imipramine, desipramine), antipsychotics/neuroleptics such as phenothiazine derivatives (chlorpromazine), sedatives anti-histamine-H1 drugs (promazine, promethazine, triprolidine, pheniramine), opiates/opioids (e.g., hydrocodone), trihexyphenidyl and sibutramine were correctly predicted as proton-antiporter substrates. The best performing pharmacophore model for the proton-antiporter substrates appeared as a good predictor of known substrates and allowed the identification of new substrate compounds. This model marks a new step in the characterization of this drug/proton-antiporter and will be of great use in uncovering its substrates and designing chemical entities with an improved influx capability to cross the BBB.

Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks

Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficients. In most classical similarity approaches, it is assumed that the molecular features in both biological and non-biologically-related activity carry the same weight. However, based on the chemical structure, it has been found that some distinguishable features are more important than others. Hence, this difference should be taken consideration by placing more weight on each important fragment. The main aim of this research is to enhance the performance of similarity searching by using multiple descriptors. In this paper, a deep learning method known as deep belief networks (DBN) has been used to reweight the molecule features. Several descriptors have been used for the MDL Drug Data Report (MDDR) dataset each of which represents different important features. The proposed method has been implemented with each descriptor individually to select the important features based on a new weight, with a lower error rate, and merging together all new features from all descriptors to produce a new descriptor for similarity searching. Based on the extensive experiments conducted, the results show that the proposed method outperformed several existing benchmark similarity methods, including Bayesian inference networks (BIN), the Tanimoto similarity method (TAN), adapted similarity measure of text processing (ASMTP) and the quantum-based similarity method (SQB). The results of this proposed multi-descriptor-based on Stack of deep belief networks method (SDBN) demonstrated a higher accuracy compared to existing methods on structurally heterogeneous datasets.

Medicinal Chemistry and Therapeutic Potential of Agonists, Antagonists and Allosteric Modulators of A1 Adenosine Receptor: Current Status and Perspectives

Adenosine is a purine nucleoside, responsible for the regulation of a wide range of physiological and pathophysiological conditions by binding with four G-protein-coupled receptors (GPCRs), namely A1, A2A, A2B and A3 adenosine receptors (ARs). In particular, A1 AR is ubiquitously present, mediating a variety of physiological processes throughout the body, thus represents a promising drug target for the management of various pathological conditions. Agonists of A1 AR are found to be useful for the treatment of atrial arrhythmia, angina, type-2 diabetes, glaucoma, neuropathic pain, epilepsy, depression and Huntington's disease, whereas antagonists are being investigated for the treatment of diuresis, congestive heart failure, asthma, COPD, anxiety and dementia. However, treatment with full A1 AR agonists has been associated with numerous challenges like cardiovascular side effects, off-target activation as well as desensitization of A1 AR leading to tachyphylaxis. In this regard, partial agonists of A1 AR have been found to be beneficial in enhancing insulin sensitivity and subsequently reducing blood glucose level, while avoiding severe CVS side effects and tachyphylaxis. Allosteric enhancer of A1 AR is found to be potent for the treatment of neuropathic pain, culminating the side effects related to off-target tissue activation of A1 AR. This review provides an overview of the medicinal chemistry and therapeutic potential of various agonists/partial agonists, antagonists and allosteric modulators of A1 AR, with a particular emphasis on their current status and future perspectives in clinical settings.

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment

Background:

Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors.

Methods:

The three dimensional quantitative structure–activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models.

Results:

The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities.

Conclusion :

The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

The use of antagonists for each adenosine receptor (AR) subtype as potent clinical candidates is of growing interest due to their involvement in the treatment of various diseases. The recent resolution of several A₁ and A_2A ARs X-ray structures provides opportunities for structure-based drug design. In this study, we describe the discovery of novel A₁AR antagonists by applying a multistage virtual screening approach, which is based on random forest (RF), e-pharmacophore modeling and docking methods. A multistage virtual screening approach was applied to screen the ChemDiv library (1,492,362 compounds). Among the final hits, 22 compounds were selected for further radioligand binding assay analysis against human A₁AR, and 18 compounds (81.82% success) exhibited nanomolar or low micromolar binding potency (K_i). Then, we selected six compounds (pK_i > 6) to further evaluate their antagonist profile in a cAMP functional assay, and we found that they had low micromolar antagonistic activity (pIC₅₀ = 5.51-6.38) for the A₁AR. Particularly, four of six compounds (pK_i > 6) showed very good affinity (pK_i = 6.11-7.13) and selectively (>100-fold) for A₁AR over A_2AAR. Moreover, the novelty analysis suggested that four of six compounds (pK_i > 6) were dissimilar to existing A₁AR antagonists and hence represented novel A₁AR antagonists. Further molecular docking and molecular dynamics (MD) studies showed that the three selective compounds 15, 20 and 22 were stabilized (RMS_lig value ≤ 2 Å) inside the binding pocket of A₁AR with similar orientations to the docking pose in 100-ns MD simulations, whereas they escaped from the binding area of A_2AAR with larger values of RMS_lig (RMS_lig ≥ 2 Å). We hope that these findings provide new insights into the discovery of drugs targeting A₁AR and facilitate research on new drugs and treatments for A₁AR-related human pathologies.

Comparative Study of Carborane- and Phenyl-Modified Adenosine Derivatives as Ligands for the A2A and A3 Adenosine Receptors Based on a Rigid in Silico Docking and Radioligand Replacement Assay

Adenosine receptors are involved in many physiological processes and pathological conditions and are therefore attractive therapeutic targets. To identify new types of effective ligands for these receptors, a library of adenosine derivatives bearing a boron cluster or phenyl group in the same position was designed. The ligands were screened in silico to determine their calculated affinities for the A2A and A3 adenosine receptors. An virtual screening protocol based on the PatchDock web server was developed. In the first screening phase, the effects of the functional group (organic or inorganic modulator) on the adenosine ligand affinity for the receptors were determined. Then, the lead compounds were identified for each receptor in the second virtual screening phase. Two pairs of the most promising ligands, compounds 3 and 4, and two ligands with lower affinity scores (compounds 11 and 12, one with a boron cluster and one with a phenyl group) were synthesized and tested in a radioligand replacement assay for affinity to the A2A and A3 receptors. A reasonable correlation of in silico and biological assay results was observed. In addition, the effects of a phenyl group and boron cluster, which is new adenosine modifiers, on the adenosine ligand binding were compared.

Mapping ligand binding pockets in chloride ClC-1 channels through an integrated in silico and experimental approach using anthracene-9-carboxylic acid and niflumic acid

BACKGROUND AND PURPOSE

Although chloride channels are involved in several physiological processes and acquired diseases, the availability of compounds selectively targeting CLC proteins is limited. ClC-1 channels are responsible for sarcolemma repolarization after an action potential in skeletal muscle and have been associated with myotonia congenita and myotonic dystrophy as well as with other muscular physiopathological conditions. To date only a few ClC-1 blockers have been discovered, such as anthracene-9-carboxylic acid (9-AC) and niflumic acid (NFA), whereas no activator exists. The absence of a ClC-1 structure and the limited information regarding the binding pockets in CLC channels hamper the identification of improved modulators.

EXPERIMENTAL APPROACH

Here we provide an in-depth characterization of drug binding pockets in ClC-1 through an integrated in silico and experimental approach. We first searched putative cavities in a homology model of ClC-1 built upon an eukaryotic CLC crystal structure, and then validated in silico data by measuring the blocking ability of 9-AC and NFA on mutant ClC-1 channels expressed in HEK 293 cells.

KEY RESULTS

We identified four putative binding cavities in ClC-1. 9-AC appears to interact with residues K231, R421 and F484 within the channel pore. We also identified one preferential binding cavity for NFA and propose R421 and F484 as critical residues.

CONCLUSIONS AND IMPLICATIONS

This study represents the first effort to delineate the binding sites of ClC-1. This information is fundamental to discover compounds useful in the treatment of ClC-1-associated dysfunctions and might represent a starting point for specifically targeting other CLC proteins.

Structure-Based Design of Potent and Selective Ligands at the Four Adenosine Receptors

The four receptors that signal for adenosine, A₁, A_2A, A_2B and A₃ ARs, belong to the superfamily of G protein-coupled receptors (GPCRs). They mediate a number of (patho)physiological functions and have attracted the interest of the biopharmaceutical sector for decades as potential drug targets. The many crystal structures of the A_2A, and lately the A₁ ARs, allow for the use of advanced computational, structure-based ligand design methodologies. Over the last decade, we have assessed the efficient synthesis of novel ligands specifically addressed to each of the four ARs. We herein review and update the results of this program with particular focus on molecular dynamics (MD) and free energy perturbation (FEP) protocols. The first in silico mutagenesis on the A₁AR here reported allows understanding the specificity and high affinity of the xanthine-antagonist 8-Cyclopentyl-1,3-dipropylxanthine (DPCPX). On the A_2AAR, we demonstrate how FEP simulations can distinguish the conformational selectivity of a recent series of partial agonists. These novel results are complemented with the revision of the first series of enantiospecific antagonists on the A_2BAR, and the use of FEP as a tool for bioisosteric design on the A₃AR.

Discovery, design and synthesis of 6H-anthra[1,9-cd]isoxazol-6-one scaffold as G9a inhibitor through a combination of shape-based virtual screening and structure-based molecular modification

Protein lysine methyltransferase G9a is widely considered as an appealing antineoplastic target. Herein we present an integrated workflow combining shape-based virtual screening and structure-based molecular modification for the identification of novel G9a inhibitors. The shape-based similarity screening through ROCS overlay on the basis of the structure of UNC0638 was performed to identify CPUY074001 contained a 6H-anthra[1,9-cd]isoxazol-6-one scaffold as a hit. Analysis of the binding mode of CPUY074001 with G9a and 3D-QSAR results, two series compounds were designed and synthesized. The derivatives were confirmed to be active by in vitro assay and the SAR was explored by docking stimulations. Besides, several analogues showed acceptable anti-proliferative effects against several cancer cell lines. Among them, CPUY074020 displayed potent dual G9a inhibitory activity and anti-proliferative activity. Furthermore, CPUY074020 induced cell apoptosis in a dose-dependent manner and displayed a significant decrease in dimethylation of H3K9. Simultaneously, CPUY074020 showed reasonable in vivo PK properties. Altogether, our workflow supplied a high efficient strategy in the identification of novel G9a inhibitors. Compounds reported here can serve as promising leads for further study.

Molecular interaction fingerprint approaches for GPCR drug discovery

Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

Human Aquaporin-4 and Molecular Modeling: Historical Perspective and View to the Future

Among the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges.

A Rational Approach for the Identification of Non-Hydroxamate HDAC6-Selective Inhibitors

The human histone deacetylase isoform 6 (HDAC6) has been demonstrated to play a major role in cell motility and aggresome formation, being interesting for the treatment of multiple tumour types and neurodegenerative conditions. Currently, most HDAC inhibitors in preclinical or clinical evaluations are non-selective inhibitors, characterised by a hydroxamate zinc-binding group (ZBG) showing off-target effects and mutagenicity. The identification of selective HDAC6 inhibitors with novel chemical properties has not been successful yet, also because of the absence of crystallographic information that makes the rational design of HDAC6 selective inhibitors difficult. Using HDAC inhibitory data retrieved from the ChEMBL database and ligand-based computational strategies, we identified 8 original new non-hydroxamate HDAC6 inhibitors from the SPECS database, with activity in the low μM range. The most potent and selective compound, bearing a hydrazide ZBG, was shown to increase tubulin acetylation in human cells. No effects on histone H4 acetylation were observed. To the best of our knowledge, this is the first report of an HDAC6 selective inhibitor bearing a hydrazide ZBG. Its capability to passively cross the blood-brain barrier (BBB), as observed through PAMPA assays, and its low cytotoxicity in vitro, suggested its potential for drug development.

Structurally Enabled Discovery of Adenosine A2A Receptor Antagonists

Over the past decade there has been a revolution in the field of G protein-coupled receptor (GPCR) structural biology. Many years of innovative research from different areas have come together to fuel this significant change in the fortunes of this field, which for many years was characterized by the paucity of high-resolution structures. The determination to succeed has been in part due to the recognized importance of these proteins as drug targets, and although the pharmaceutical industry has been focusing on these receptors, it can be justifiably argued and demonstrated that many of the approved and commercially successful GPCR drugs can be significantly improved to increase efficacy and/or reduce undesired side effects. In addition, many validated targets in this class remain to be drugged. It is widely recognized that application of structure-based drug design approaches can help medicinal chemists a long way toward discovering better drugs. The achievement of structural biologists in providing high-resolution insight is beginning to transform drug discovery efforts, and there are a number of GPCR drugs that have been discovered by use of structural information that are in clinical development. This review aims to highlight the key developments that have brought success to GPCR structure resolution efforts and exemplify the practical application of structural information for the discovery of adenosine A_2A receptor antagonists that have potential to treat multiple conditions.

A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I1-type imidazoline receptors (I1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from α2-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine-derived compounds with anticancer potential and devoid of α2-adrenoceptor effects by means of ligand- and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to α2-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers.

Polymerase Acidic Protein-Basic Protein 1 (PA-PB1) Protein-Protein Interaction as a Target for Next-Generation Anti-influenza Therapeutics

The limited therapeutic options against the influenza virus (flu) and increasing challenges in drug resistance make the search for next-generation agents imperative. In this context, heterotrimeric viral PA/PB1/PB2 RNA-dependent RNA polymerase is an attractive target for a challenging but strategic protein-protein interaction (PPI) inhibition approach. Since 2012, the inhibition of the polymerase PA-PB1 subunit interface has become an active field of research following the publication of PA-PB1 crystal structures. In this Perspective, we briefly discuss the validity of flu polymerase as a drug target and its inhibition through a PPI inhibition strategy, including a comprehensive analysis of available PA-PB1 structures. An overview of all of the reported PA-PB1 complex formation inhibitors is provided, and approaches used for identification of the inhibitors, the hit-to-lead studies, and the emerged structure-activity relationship are described. In addition to highlighting the strengths and weaknesses of all of the PA-PB1 heterodimerization inhibitors, we analyze their hypothesized binding modes and alignment with a pharmacophore model that we have developed.

Pharmacophore-based discovery of inhibitors of a novel drug/proton antiporter in human brain endothelial hCMEC/D3 cell line

BACKGROUND AND PURPOSE

An influx drug/proton antiporter of unknown structure has been functionally demonstrated at the blood-brain barrier. This transporter, which handles some psychoactive drugs like diphenhydramine, clonidine, oxycodone, nicotine and cocaine, could represent a new pharmacological target in drug addiction therapy. However, at present there are no known drugs/inhibitors that effectively inhibit/modulate this transporter in vivo.

EXPERIMENTAL APPROACH

The FLAPpharm approach was used to establish a pharmacophore model for inhibitors of this transporter. The inhibitory potency of 44 selected compounds was determined against the specific substrate, [(3)H]-clonidine, in the human cerebral endothelial cell line hCMEC/D3 and ranked as good, medium, weak or non-inhibitor.

KEY RESULTS

The pharmacophore model obtained was used as a template to screen xenobiotic and endogenous compounds from databases [Specs, Recon2, Human Metabolome Database (HMDB), human intestinal transporter database], and hypothetical candidates were tested in vitro to determine their inhibitory capacity with [(3)H]-clonidine. According to the transporter database, 80% of the proton antiporter inhibitor candidates could inhibit P-glycoprotein/MDR1/ABCB1 and specificity is improved by reducing inhibitor size/shape and increasing water solubility. Virtual screening results using HMDB and Recon2 for endogenous compounds appropriately scored tryptamine as an inhibitor.

CONCLUSIONS AND IMPLICATIONS

The pharmacophore model for the proton-antiporter inhibitors was a good predictor of known inhibitors and allowed us to identify new good inhibitors. This model marks a new step towards the discovery of this drug/proton antiporter and will be of great use for the discovery and design of potent inhibitors that could potentially help to assess and validate its pharmacological role in drug addiction in vivo.

Challenging AQP4 druggability for NMO-IgG antibody binding using molecular dynamics and molecular interaction fields

Neuromyelitis optica (NMO) is a multiple sclerosis-like immunopathology disease affecting optic nerves and the spinal cord. Its pathological hallmark is the deposition of a typical immunoglobulin, called NMO-IgG, against the water channel Aquaporin-4 (AQP4). Preventing NMO-IgG binding would represent a valuable molecular strategy for a focused NMO therapy. The recent observation that aspartate in position 69 (D69) is determinant for the formation of NMO-IgG epitopes prompted us to carry out intensive Molecular Dynamics (MD) studies on a number of single-point AQP4 mutants. Here, we report a domino effect originating from the point mutation at position 69: we find that the side chain of T62 is reoriented far from its expected position leaning on the lumen of the pore. More importantly, the strength of the H-bond interaction between L53 and T56, at the basis of the loop A, is substantially weakened. These events represent important pieces of a clear-cut mechanistic rationale behind the failure of the NMO-IgG binding, while the water channel function as well as the propensity to aggregate into OAPs remains unaltered. The molecular interaction fields (MIF)-based analysis of cavities complemented MD findings indicating a putative binding site comprising the same residues determining epitope reorganization. In this respect, docking studies unveiled an intriguing perspective to address the future design of small drug-like compounds against NMO. In agreement with recent experimental observations, the present study is the first computational attempt to elucidate NMO-IgG binding at the molecular level, as well as a first effort toward a less elusive AQP4 druggability.

Structure-based drug design for G protein-coupled receptors

Our understanding of the structural biology of G protein-coupled receptors has undergone a transformation over the past 5 years. New protein-ligand complexes are described almost monthly in high profile journals. Appreciation of how small molecules and natural ligands bind to their receptors has the potential to impact enormously how medicinal chemists approach this major class of receptor targets. An outline of the key topics in this field and some recent examples of structure- and fragment-based drug design are described. A table is presented with example views of each G protein-coupled receptor for which there is a published X-ray structure, including interactions with small molecule antagonists, partial and full agonists. The possible implications of these new data for drug design are discussed.

Flavin monooxygenase metabolism: why medicinal chemists should matter

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The N-oxide and S-oxide metabolites produced by FMOs are often active metabolites. The FMOs are more active than cytochromes in the brain and work in tandem with CYP3A4 in the liver. FMOs might reduce the risk of phospholipidosis of CAD-like drugs, although some FMOs metabolites seem to be neurotoxic and hepatotoxic. However, in silico methods for FMO metabolism prediction are not yet available. This paper reports, for the first time, a substrate-specificity and catalytic-activity model for FMO3, the most relevant isoform of the FMOs in humans. The application of this model to a series of compounds with unknown FMO metabolism is also reported. The model has also been very useful to design compounds with optimal clearance and in finding erroneous literature data, particularly cases in which substances have been reported to be FMO3 substrates when, in reality, the experimentally validated in silico model correctly predicts that they are not.

Discovery and Bioevaluation of Novel Pyrazolopyrimidine Analogs as Competitive Hsp90 Inhibitors Through Shape-Based Similarity Screening

Hsp90 as a promising therapeutic target for the treatment of cancer has received great attention. Many Hsp90 inhibitors such as BIIB021 and CUDC-305 have been in clinical. In this paper shape-based similarity screening through ROCS overlays on the basis of CUDC-305, BIIB021, PU-H71 and PU-3 were performed to discover HSP90 inhibitors. A set of 19 novel pyrazolopyrimidine analogues was identified and evaluated on enzyme level and cell-based level as Hsp90 inhibitors. The compound HDI4-04 with IC50 0.35 µM in the Hsp90 ATP hydrolysis assay exhibited potent cytotoxicity against five human cancer cell lines. Western blot analysis and Hsp70 luciferase reporter assay further confirmed that HDI4-04 targeted the Hsp90 protein folding machinery. And according to the biological assay, the SAR was discussed and summarized, which will guide us for further optimization of these compounds.

Targeting cystalysin, a virulence factor of treponema denticola-supported periodontitis

Cystalysin from Treponema denticola is a pyridoxal 5'-phosphate dependent lyase that catalyzes the formation of pyruvate, ammonia, and sulfide from cysteine. It is a virulence factor in adult periodontitis because its reaction contributes to hemolysis, which sustains the pathogen. Therefore, it was proposed as a potential antimicrobial target. To identify specific inhibitors by structure-based in silico methods, we first validated the crystal structure of cystalysin as a reliable starting point for the design of ligands. By using single-crystal absorption microspectrophotometry, we found that the enzyme in the crystalline state, with respect to that in solution, exhibits: 1) the same absorption spectra for the catalytic intermediates, 2) a close pKa value for the residue controlling the keto enamine ionization, and 3) similar reactivity with glycine, L-serine, L-methionine, and the nonspecific irreversible inhibitor aminoethoxyvinylglycine. Next, we screened in silico a library of 9357 compounds with the Fingerprints for Ligands and Proteins (FLAP) software, by using the three-dimensional structure of cystalysin as a template. From the library, 17 compounds were selected and experimentally evaluated by enzyme assays and spectroscopic methods. Two compounds were found to competitively inhibit recombinant T. denticola cystalysin, with inhibition constant (Ki ) values of 25 and 37 μM. One of them exhibited a minimum inhibitory concentration (MIC) value of 64 μg mL(-1) on Moraxella catarrhalis ATCC 23246, which proves its ability to cross bacterial membranes.

Isozyme-specific ligands for O-acetylserine sulfhydrylase, a novel antibiotic target

The last step of cysteine biosynthesis in bacteria and plants is catalyzed by O-acetylserine sulfhydrylase. In bacteria, two isozymes, O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, have been identified that share similar binding sites, although the respective specific functions are still debated. O-acetylserine sulfhydrylase plays a key role in the adaptation of bacteria to the host environment, in the defense mechanisms to oxidative stress and in antibiotic resistance. Because mammals synthesize cysteine from methionine and lack O-acetylserine sulfhydrylase, the enzyme is a potential target for antimicrobials. With this aim, we first identified potential inhibitors of the two isozymes via a ligand- and structure-based in silico screening of a subset of the ZINC library using FLAP. The binding affinities of the most promising candidates were measured in vitro on purified O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B from Salmonella typhimurium by a direct method that exploits the change in the cofactor fluorescence. Two molecules were identified with dissociation constants of 3.7 and 33 µM for O-acetylserine sulfhydrylase-A and O-acetylserine sulfhydrylase-B, respectively. Because GRID analysis of the two isoenzymes indicates the presence of a few common pharmacophoric features, cross binding titrations were carried out. It was found that the best binder for O-acetylserine sulfhydrylase-B exhibits a dissociation constant of 29 µM for O-acetylserine sulfhydrylase-A, thus displaying a limited selectivity, whereas the best binder for O-acetylserine sulfhydrylase-A exhibits a dissociation constant of 50 µM for O-acetylserine sulfhydrylase-B and is thus 8-fold selective towards the former isozyme. Therefore, isoform-specific and isoform-independent ligands allow to either selectively target the isozyme that predominantly supports bacteria during infection and long-term survival or to completely block bacterial cysteine biosynthesis.

Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification

Protein kinase CK2 (CK2), a ubiquitous serine/threonine protein kinase for hundreds of endogenous substrates, serves as an attractive anticancer target. One of its most potent inhibitors, CX-4945, has entered a phase I clinical trial. Herein we present an integrated workflow combining shape-based virtual screening for the identification of novel CK2 inhibitors. A shape-based model derived from CX-4945 was built, and the subsequent virtual screening led to the identification of several novel scaffolds with high shape similarity to that of CX-4945. Among them two tricyclic scaffolds named [1,2,4]triazolo[4,3-c]quinazolin and [1,2,4]triazolo[4,3-a]quinoxalin attracted us the most. Combining strictly chemical similarity analysis, a second-round shape-based screening was performed based on the two tricyclic scaffolds, leading to 28 derivatives. These compounds not only targeted CK2 with potent and dose-dependent activities but also showed acceptable antiproliferative effects against a series of cancer cell lines. Our workflow supplies a high efficient strategy in the identification of novel CK2 inhibitors. Compounds reported here can serve as ideal leads for further modifications.

Structure-based approaches to ligands for G-protein-coupled adenosine and P2Y receptors, from small molecules to nanoconjugates

Adenosine receptor (ARs) and P2Y receptors (P2YRs) that respond to extracellular nucleosides/nucleotides are associated with new directions for therapeutics. The X-ray structures of the A2AAR complexes with agonists and antagonists are examined in relationship to the G-protein-coupled receptor (GPCR) superfamily and applied to drug discovery. Much of the data on AR ligand structure from early SAR studies now are explainable from the A2AAR X-ray crystallography. The ligand-receptor interactions in related GPCR complexes can be identified by means of modeling approaches, e.g., molecular docking. Thus, molecular recognition in binding and activation processes has been studied effectively using homology modeling and applied to ligand design. Virtual screening has yielded new nonnucleoside AR antagonists, and existing ligands have been improved with knowledge of the receptor interactions. New agonists are being explored for central nervous system and peripheral therapeutics based on in vivo activity, such as chronic neuropathic pain. Ligands for receptors more distantly related to the X-ray template, i.e., P2YRs, have been introduced and are mainly used as pharmacological tools for elucidating the physiological role of extracellular nucleotides. Other ligand tools for drug discovery include fluorescent probes, radioactive probes, multivalent probes, and functionalized nanoparticles.