Pharmacophore identification and validation for human nAChR α7 agonists

Human nAChR α7 is the potential target for schizophrenia cognitive disorders, and it is meaningful to develop selective human nAChR α7 agonists for the clinical treatment of the disease. Because the crystal structure of α7 receptor has not been resolved, ligand-based drug design strategy was took in this work. A 3D QSAR pharmacophore model was built by HypoGen method, and its quality was evaluated by cost function. Furthermore, the pharmacophore model was validated with activity prediction of test set and was cross-validated based on Fisher’s Randomization Method. By Enrichment Factor and AU-ROC analysis, the final pharmacophore, which is consisted of one HBA, two Hydrophobic and one PosIonizable, was selected and it fitted well with the docking result of α7 homology model and the ligand. The pharmacophore is expected for the following virtual screening and lead optimization of human nAChR α7 agonists, which is important for the development and discovery of novel antipsychotics.     

Fragment‐ and negative image‐based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment‐ and negative image‐based (F‐NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A‐specific small‐molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F‐NiB combines features from both fragment‐based drug discovery and negative image‐based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein‐bound ligand(s) are seamlessly combined with the negative image of the target's ligand‐binding cavity. This cavity‐ and fragment‐based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure–activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F‐NiB provides a flexible way to incorporate small‐molecule fragments into the drug discovery.     

Identification of Novel HCV RNA‐dependent RNA polymerase Inhibitors Using Pharmacophore‐Guided Virtual Screening

We performed pharmacophore‐guided virtual screening experiments using FlexX‐Pharm to identify novel inhibitors of hepatitis C virus RNA‐dependent RNA polymerase. Pharmacophore model generated from our previous analysis of the binding modes as well as structure‐based three‐dimensional quantitative structure–activity relationship studies of aryl diketoacid analogues was used. In pharmacophore‐guided virtual screening study, among 37 447 compounds in LeadQuest chemical library, 40 compounds were selected as novel candidates of hepatitis C virus RNA‐dependent RNA polymerase inhibitors, and their biological activities were evaluated. Especially, T29 was chosen for further development.     

采用药效团模型和分子对接方法筛选新型的组蛋白甲基转移酶G9a抑制剂

目的利用药效团模型和分子对接方法对商业化合物库ChemDiv中的G9afocused-libraries进行筛选,希望发现新骨架结构的G9a抑制剂。方法首先,使用Discovery studio 3.1软件分别构建基于配体的药效团模型和基于配体-受体复合物的药效团模型,并根据构建的2个模型再重新定义2个新的药效团模型。然后,构建测试集并测试药效团模型的预测能力。最后,选取最优药效团模型对G9afocused-libraries进行筛选,对筛选出的化合物使用CDOCKER分子对接进行分析与评价。结果测试结果显示,所构建的药效团模型具有一定的预测能力,通过该药效团筛选得到了2个结构新颖的潜在的G9a抑制剂。结论所构建的药效团模型具有一定的可靠性,虚拟筛选发现的G9a抑制剂还需进一步的实验证明。     

Pharmacophore‐Based Virtual Screening to Aid in the Identification of Unknown Protein Function

A pharmacophore‐based virtual screening method was developed and validated for use in predicting the function of a novel protein in terms of small metabolite binding. Five test cases were used for the validation study which spanned two different folds, four superfamilies, and three enzyme classes. Binding sites were predicted using a combination of two methods (CASTp and THEMATICS). The binding site was mapped with chemical probes representing hydrogen‐bond donor, acceptor, negative ionizable, positive ionizable, and hydrophobe. The interaction maps were converted to three or four feature pharmacophore models and used to search a database containing 80 018 tautomers/protomers/conformers of 10 535 metabolites. The pharmacophore‐based virtual screening eliminated >92% of the database as potential substrates and retrieved specific hits, which were ranked using a physics‐based scoring function. The known substrate or product was ranked within the top 0.7% and substrate‐like compounds within the top 1% of the metabolite database for all of the five test cases. The results suggest that using this pharmacophore‐based virtual screening is a time‐efficient strategy that can be applied to screen large databases to help predict the function of small metabolite binding proteins.     

Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening

Peroxisome proliferator-activated receptors (PPARs) are important targets for drugs used in the treatment of atherosclerosis, dyslipidaemia, obesity, type 2 diabetes, and other diseases caused by abnormal regulation of the glucose and lipid metabolism. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR ligands. From the resulting 10 virtual screening hits, five tested positive in human PPAR ligand-binding domain (hPPAR-LBD) transactivation assays and showed affinities for PPAR in a competitive binding assay. Compounds 5, 7, and 8 were identified as PPAR-alpha agonists, whereas compounds 2 and 9 showed agonistic activity for hPPAR-gamma. Moreover, compound 9 was identified as a PPAR-delta antagonist. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for PPAR ligands that could be useful for drug development in the area of atherosclerosis, dyslipidaemia, and type 2 diabetes.     

Virtual Screening of CB2 Receptor Agonists from Bayesian Network and High‐Throughput Docking: Structural Insights into Agonist‐Modulated GPCR Features

The relevance of CB₂‐mediated therapeutics is well established in the treatment of pain, neurodegenerative and gastrointestinal tract disorders. Recent works such as the crystallization of class‐A G‐protein‐coupled receptors in a range of active states and the identification of specific anchoring sites for CB₂ agonists challenged us to design a reliable agonist‐bound homology model of CB₂ receptor. Docking‐scoring enrichment tests of a high‐throughput virtual screening of 140 compounds led to 13 hits within the micromolar affinity range. Most of these hits behaved as CB₂ agonists, among which two novel full agonists emerged. Although the main challenge was a high‐throughput docking run targeting an agonist‐bound state of a CB₂ model, a prior 2D ligand‐based Bayesian network was computed to enrich the input commercial library for 3D screening. The exclusive discovery of agonists illustrates the reliability of this agonist‐bound state model for the identification of polar and aromatic amino acids as new agonist‐modulated CB₂ features to be integrated in the wide activation pathway of G‐protein‐coupled receptors.     

Structure‐based pharmacophore models to probe anticancer activity of inhibitors of protein kinase B‐beta (PKB β)

Protein kinase B ‐ beta (PKBβ/Akt2) is a non‐receptor kinase that has attracted a great deal of attention as a promising cancer therapy drug target. In mammalian cells, hyperactivation of Akt2 exclusively facilitates the survival of solid tumors by interfering with cell cycle progression. This definite function of Akt2 in tumor survival/maintenance provides the basis for the development of its antagonists with the aim of desensitizing cell proliferation. In order to find novel and potent Akt2 inhibitors, structure ‐ based pharmacophore models have been developed and validated by the test set prediction. The final pharmacophore model was used for hits identification using public chemical databases. The hits were further prioritized using drug ‐ like filters which revealed 14 potential hit compounds having novel chemical scaffolds. Our results elucidate the importance of three hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H), and one positive ionic charge (P) toward inhibition of the Ak2. One of our selected hits showed 68% cell apoptosis at 8 μg/ml concentration. We proposed various chemical scaffolds including benzamide, carboxamide, and methyl benzimidazole targeting Akt2 and thus may act as potential leads for the further development of new anticancer agents.     

Novel Scaffold Identification of mGlu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach

Metabotropic glutamate receptor 1 (mGluR1) is considered as an attractive drug target for neuropathic pain treatments. The hierarchical virtual screening approach for identifying novel scaffolds of mGluR1 allosteric modulators was performed using a homology model built with the dopamine D3 crystal structure as template. The mGluR1 mutagenesis data, conserved amino acid sequences across class A and class C GPCRs, and previously reported multiple sequence alignments of class C GPCRs to the rhodopsin template, were employed for the sequence alignment to overcome difficulties of model generation with low sequence identity of mGluR1 and dopamine D3. The structures refined by molecular dynamics simulations were employed for docking of Asinex commercial libraries after hierarchical virtual screening with pharmacophore and naïve Bayesian models. Five of 35 compounds experimentally evaluated using a calcium mobilization assay exhibited micromolar activities (IC50) with chemotype novelty that demonstrated the validity of our methods. A hierarchical structure and ligand‐based virtual screening approach with homology model of class C GPCR based on dopamine D3 class A GPCR structure was successfully performed and applied to discover novel negative mGluR1 allosteric modulators.     

Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening

The G-protein-coupled receptor free fatty acid receptor 1 (FFAR1), previously named GPR40, is a possible novel target for the treatment of type 2 diabetes. In an attempt to identify new ligands for this receptor, we performed virtual screening (VS) based on two-dimensional (2D) similarity, three-dimensional (3D) pharmacophore searches, and docking studies by using the structure of known agonists and our model of the ligand binding site, which was validated by mutagenesis. VS of a database of 2.6 million compounds followed by extraction of structural neighbors of functionally confirmed hits resulted in identification of 15 compounds active at FFAR1 either as full agonists, partial agonists, or pure antagonists. Site-directed mutagenesis and docking studies revealed different patterns of ligand-receptor interactions and provided important information on the role of specific amino acids in binding and activation of FFAR1.     

Combination of Pharmacophore Matching, 2D Similarity Search,and In Vitro Biological Assays in the Selection of Potential 5‐HT6 Antagonists from Large Commercial Repositories

Rapid in silico selection of target‐focused libraries from commercial repositories is an attractive and cost‐effective approach. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compound databases, but the generated library requires further focusing. We report here a combination of the 2D approach with pharmacophore matching which was used for selecting 5‐HT₆ antagonists. In the first screening round, 12 compounds showed >85% antagonist efficacy of the 91 screened. For the second‐round (hit validation) screening phase, pharmacophore models were built, applied, and compared with the routine 2D similarity search. Three pharmacophore models were created based on the structure of the reference compounds and the first‐round hit compounds. The pharmacophore search resulted in a high hit rate (40%) and led to novel chemotypes, while 2D similarity search had slightly better hit rate (51%), but lacking the novelty. To demonstrate the power of the virtual screening cascade, ligand efficiency indices were also calculated and their steady improvement was confirmed.     

Ligand‐binding affinity of alternative conformers of human β2‐adrenergic receptor in the presence of intracellular loop 3 (ICL3) and their potential use in virtual screening studies

This study investigates the structural distinctiveness of orthosteric ligand‐binding sites of several human β₂ adrenergic receptor (β₂‐AR) conformations that have been obtained from a set of independent molecular dynamics (MD) simulations in the presence of intracellular loop 3 (ICL3). A docking protocol was established in order to classify each receptor conformation via its binding affinity to selected ligands with known efficacy. This work's main goal was to reveal many subtle features of the ligand‐binding site, presenting alternative conformations, which might be considered as either active‐ or inactive‐like but mostly specific for that ligand. Agonists, inverse agonists, and antagonists were docked to each MD conformer with distinct binding pockets, using different docking tools and scoring functions. Mostly favored receptor conformation persistently observed in all docking/scoring evaluations was classified as active or inactive based on the type of ligand's biological effect. Classified MD conformers were further tested for their ability to discriminate agonists from inverse agonists/antagonists, and several conformers were proposed as important targets to be used in virtual screening experiments that were often limited to a single X‐ray structure.     

Discovery of substituted sulfonamides and thiazolidin-4-one derivatives as agonists of human constitutive androstane receptor

The constitutive androstane receptor (CAR; NR1I3) is a nuclear receptor responsible for the recognition of potentially toxic endo- and exogenous compounds whose elimination from the body is accelerated by the CAR-mediated inducible expression of metabolizing enzymes and transporters. Despite the importance of CAR, few human agonists are known so far. Following a sequential virtual screening procedure using a 3D pharmacophore and molecular docking approach, we identified 17 novel agonists that could activate human CAR in vitro and enhance its association with the nuclear receptor co-activator SRC1. Selected agonists also increased the expression of the human CAR target CYP2B6 mRNA in primary hepatocytes. Composed of substituted sulfonamides and thiazolidin-4-one derivatives, these agonists represent two novel chemotypes capable of human CAR activation, thus broadening the agonist spectrum of CAR.     

Discovery of small molecule agonists targeting neuropeptide Y4 receptor using homology modeling and virtual screening

Neuropeptide Y4 receptor has the most significant effect on body weight and fat mass in its physiological functions, and the activation of Y4 receptor has explicit role on losing weight. The Y4 receptor has been successfully applied in the development of anti‐obesity agent, thus representing a potential therapeutic target for obesity treatment. Here, we reported the first discovery of small molecule agonists targeting Y4 receptor: three Y4 receptor models with active and inactive conformations were built, each model was submitted following structure‐based virtual screening, and finally six hits were identified as Y4 receptor agonists. These results confirm the reliability of the constructed Y4 receptor models and the proposed computational strategy for investigating novel Y4 receptor agonists. These new small molecule Y4 receptor agonists will contribute to the further development of Y4 agonists as potential therapeutics and functional probes.     

Combining Structure‐Based Pharmacophore and In Silico Approaches to Discover Novel Selective Serotonin Reuptake Inhibitors

Inhibition of human serotonin transporter (hSERT) has been reported to be a potent strategy for the treatment for depression. To discover novel selective serotonin reuptake inhibitors (SSRIs), a structure‐based pharmacophore model (SBPM) was developed using the docked conformations of six highly active SSRIs. The best SBPM, consisting of four chemical features: two ring aromatics (RAs), one hydrophobic (HY), and one positive ionizable (PI), was further validated using Gunner‐Henry (GH) scoring and receiver operating characteristic (ROC) curve methods. This well‐validated SBPM was then used as a 3D‐query in virtual screening to identify potential hits from National Cancer Institute (NCI) database. These hits were subsequently filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction and molecular docking, and their binding stabilities were validated by 20‐ns MD simulations. Finally, only two compounds (NSC175176 and NSC705841) were identified as potential leads, which exhibited higher binding affinities in comparison with the paroxetine. Our results also suggest that cation–π interaction plays a crucial role in stabilizing the hSERT‐inhibitor complex. To our knowledge, the present work is the first structure‐based virtual screening study for new SSRI discovery, which should be a useful guide for the rapid identification of novel therapeutic agents from chemical database.     

Pharmacophore modeling and virtual screening for the discovery of new fatty acid amide hydrolase inhibitors

A predictive pharmacophore model has been generated from a series of diverse fatty acid amide hydrolase (FAAH) inhibitors and the optimal pharmacophore model applied in virtual screening. The pharmacophore model was based on a training set of 21 compounds carefully selected from the published literatures. The optimal model Hypo-1 included four features (two hydrogen-bond acceptor units, one aromatic hydrophobic unit and one aromatic ring unit) and two excluded volumes. Cross-validation of the model confirmed that Hypo-1 was not generated by chance correlation. A large test set of 55 compounds showed that Hypo-1 performed well in classifying highly active and less active FAAH inhibitors. Superimposition analysis of the FAAH X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the model. Virtual screening generated a total of 976 hits from the Zinc Natural Products database, a hit rate of 1.04% and enrichment of 83.89. The acceptable hit rate further supports the use of Hypo-1 as a 3D query tool for virtual screening.     

传统中药中抗动脉粥样硬化PPARα激动剂的虚拟筛选

目的基于传统中药寻找抗动脉粥样硬化的先导药物分子。方法过氧化物酶体增殖物激活受体α(PPARα)是抗动脉粥样硬化的研究热点之一,以该受体为靶标,采用计算机辅助药物分子设计方法中的药效团筛选、分子对接和相互作用模式分析对传统中药数据库进行虚拟筛选。结果从含有37 170个化合物的传统中药数据库中优选出20个活性较好的化合物,通过进一步的氢键作用分析确认3个先导化合物。结论该研究为新型抗动脉粥样硬化PPARα激动剂的发现提供了新的方法和理论依据。