A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on it

In this study, 3D‐pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross‐validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

Identification of novel polo-like kinase 1 inhibitors by a hybrid virtual screening

Polo-like kinase 1 is an important and attractive oncological target that plays a key role in mitosis and cytokinesis. A combined pharmacophore- and docking-based virtual screening was performed to identify novel polo-like kinase 1 inhibitors. A total of 34 hit compounds were selected and tested in vitro, and some compounds showed inhibition of polo-like kinase 1 and human tumor cell growth. The most potent compound (66) inhibited polo-like kinase 1 with an IC(50) value of 6.99 μm. The docked binding models of two hit compounds were discussed in detail. These compounds contained novel chemical scaffolds and may be used as foundations for the development of novel classes of polo-like kinase 1 inhibitors.     

Investigation of potential glycogen synthase kinase 3 inhibitors using pharmacophore mapping and virtual screening

Glycogen synthase kinase-3 is a serine/threonine kinase that has attracted significant drug discovery attention in recent years. To investigate the identification of new potential glycogen synthase kinase-3 inhibitors, a pharmacophore mapping study was carried out using a set of 21 structurally diverse glycogen synthase kinase-3 inhibitors. A hypothesis containing four features: two hydrophobic, one hydrogen bond donor and another hydrogen bond acceptor was found to be the best from the 10 common feature hypotheses produced by HipHop module of Catalyst. The best hypothesis has a high cost of 156.592 and higher best fit values were obtained for the 21 inhibitors using this best hypothesis than the other HipHop hypotheses. The best hypothesis was then used to screen electronically the NCI2000 database. The hits obtained were docked into glycogen synthase kinase-3beta active site. A total of five novel potential leads were proposed after: (i) visual examination of how well they dock into the glycogen synthase kinase-3beta-binding site, (ii) comparative analysis of their FlexX, G-Score, PMF-Score, ChemScore and D-Scores values, (iii) comparison of their best fit value with the known inhibitors and (iv) examination of the how the hits retain interactions with the important amino acid residues of glycogen synthase kinase-3beta-binding site.     

Sequential virtual screening approach to the identification of small organic molecules as potential BACE-1 inhibitors

In this letter, we report on the sequential application of two different in silico screening approaches combined with bioassays aimed at the identification of small organic molecules as potential BACE-1 inhibitors. Two hits endowed of micromolar inhibitory potency were selected, and the binding mode of the most potent compound was further characterized through docking simulations.     

Identification of novel antitubulin agents by using a virtual screening approach based on a 7-point pharmacophore model of the tubulin colchi-site

Tubulin inhibition represents an established target in the field of anticancer research, and over the last 20 years, an intensive search for new antimicrotubule agents has occurred. Indeed, in silico models have been presented that might aid the discovery of novel agents. Among these, a 7-point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand-based virtual screening on the colchicine-binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH-SY5H) and one had an antitubulinic profile.     

Identification of Novel Anaplastic Lymphoma Kinase (ALK) Inhibitors Using a Common Feature Pharmacophore Model Derived from Known Ligands Crystallized with ALK

In this investigation, a common feature pharmacophore model of anaplastic lymphoma kinase inhibitors was developed based on several known anaplastic lymphoma kinase inhibitors that were co‐crystallized with anaplastic lymphoma kinase. The established pharmacophore model Hypo1 was carefully validated and then adopted to screen two in silico chemical databases, Specs (202 408 compounds) and Enamine (1 105 894 compounds), for retrieving novel anaplastic lymphoma kinase inhibitors. The hit compounds were further filtered using a fast bumping‐check tool and molecular docking. Finally, 25 compounds were selected and purchased from market. The bioactivity of these compounds was firstly measured at the cellular level against a typical anaplastic lymphoma kinase mutant‐driven cancer cell line, Karpas299. And six of them showed a good anti‐viability activity. The kinase inhibitory potency against the recombinant human anaplastic lymphoma kinase kinase was tested to the most active compound at the cellular level, T0508‐5181 (from Specs), which gave a half maximal inhibitory concentration (IC₅₀) of 5.3 μm .     

In silico screening strategies for novel inhibitors of parasitic diseases

Introduction: Parasitic diseases are a major global problem causing long-term disability and death, with severe medical and psychological consequences around the world. Despite the prevalence of parasitic disease, the treatment options for many of these illnesses are still inadequate and there is a dire need for new antiparasitic drugs. In silico screening techniques, which are powerful strategies for hit generation, are widely being applied in the design of new ligands for parasitic diseases.

Areas covered: This article analyses the application of ligand- and structure-based virtual screening strategies against a variety of parasitic diseases and discusses the benefits of the integration between computational and experimental approaches toward the discovery of new antiparasitic agents. The analysis is illustrated by recent examples, with emphasis on the strategies reported within the past 2 years.

Expert opinion: Virtual screening techniques are powerful tools commonly used in drug discovery against parasitic diseases, which have provided new opportunities for the identification of several novel compound classes with antiparasitic activity.     

Discovery of novel Bruton's tyrosine kinase inhibitors using a hybrid protocol of virtual screening approaches based on SVM model, pharmacophore and molecular docking

Bruton's tyrosine kinase has emerged as a potential target for the treatment for B-cell malignancies and autoimmune diseases. Discovery of Bruton's tyrosine kinase inhibitors has thus attracted much attention recently. In this investigation, we introduced a hybrid protocol of virtual screening methods including support vector machine model-based virtual screening, pharmacophore model-based virtual screening and docking-based virtual screening for retrieving new Bruton's tyrosine kinase inhibitors from commercially available chemical databases. Performances of the hybrid virtual screening approach were evaluated against a test set, which results showed that the hybrid virtual screening approach significantly shortened the overall screening time, and considerably increased the hit rate and enrichment factor compared with the individual method (SB-VS, PB-VS and DB-VS) or their combinations by twos. This hybrid virtual screening approach was then applied to screen several chemical databases including Specs (202,408 compounds) and Enamine (980,000 compounds) databases. Thirty-nine compounds were selected from the final hits and have been shifted to experimental studies.     

The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies

We have applied pharmacophore generation, database searching and docking methodologies to discover new structures for the design of vascular endothelial growth factor receptors, the tyrosine kinase insert domain-containing receptor kinase inhibitors. The chemical function based pharmacophore models were built for kinase insert domain-containing receptor kinase inhibitors from a set of 10 known inhibitors using the algorithm HipHop, which is implemented in the CATALYST software. The highest scoring HipHop model consists of four features: one hydrophobic, one hydrogen bond acceptor, one hydrogen bond donor and one ring aromatic function. Using the algorithm CatShape within CATALYST, the bound conformation of 4-amino-furo [2, 3-d] pyrimidine binding to kinase insert domain-containing receptor kinase was used to generate a shape query. A merged shape and hypothesis query that is in an appropriate alignment was then built. The combined shape and hypothesis model was used as a query to search Maybridge database for other potential lead compounds. A total of 39 compounds were retrieved as hits. The hits obtained were docked into kinase insert domain-containing receptor kinase active site. One novel potential lead was proposed based on CATALYST fit value, LigandFit docking scores, and examination of how the hit retain key interactions known to be required for kinase binding. This compound inhibited vascular endothelial growth factor stimulated kinase insert domain-containing receptor phosphorylation in human umbilical vein endothelial cells.     

Combining ligand-based and structure-based drug design in the virtual screening arena

The aim of virtual high-throughput screening is the identification of biologically relevant molecules among either tangible or virtual (large) collections of compounds. Likewise, high-throughput screening (HTS) and high-throughput virtual screening (HTVS) methods are becoming very important within the drug discovery process. HTVS methods can be categorised as either ‘ligand-based’ or ‘structure-based’ depending on if a direct knowledge of the three-dimensional target structure is required. A summary of the most promising computational approaches is reviewed. Advantages and shortcomings of the methodology are also discussed.     

Virtual screening of cathepsin k inhibitors using docking and pharmacophore models

Cathepsin K is a lysosomal cysteine protease that is highly and selectively expressed in osteoclasts, the cells which degrade bone during the continuous cycle of bone degradation and formation. Inhibition of cathepsin K represents a potential therapeutic approach for diseases characterized by excessive bone resorption such as osteoporosis. In order to elucidate the essential structural features for cathepsin K, a three-dimensional pharmacophore hypotheses were built on the basis of a set of known cathepsin K inhibitors selected from the literature using catalyst program. Several methods are used in validation of pharmacophore hypothesis were presented, and the fourth hypothesis (Hypo4) was considered to be the best pharmacophore hypothesis which has a correlation coefficient of 0.944 with training set and has high prediction of activity for a set of 30 test molecules with correlation of 0.909. The model (Hypo4) was then employed as 3D search query to screen the Maybridge database containing 59,000 compounds, to discover novel and highly potent ligands. For analyzing intermolecular interactions between protein and ligand, all the molecules were docked using Glide software. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme inhibitor interaction pattern identified from molecular docking.     

应用虚拟筛选方法寻找链阳霉素A乙酰转移酶抑制剂

维吉尼亚霉素乙酰转移酶D(VatD)通过灭活链阳霉素A而在链阳霉素耐药性的产生中起重要作用。本研究采用虚拟筛选技术寻找VatD的抑制剂,此VatD抑制剂可以和链阳霉素联合使用,从而提供新的治疗耐药菌感染的方法。作者首次应用基于结构的虚拟筛选方法(分子对接)从含300 000化合物的商业化数据库中筛选对抗VatD底物结合位点的化合物,从200个评分最高的化合物中选取26个测定对VatD酶活性的抑制作用。将构建的质粒pRSET B/vatD转染宿主细胞E.coli(TrxB)用于过表达,纯化的VatD对维吉尼亚霉素M1表现乙酰转移酶活性。26个化合物中有3个对VatD表现抑制作用,IC₅₀分别为168.6,91.0和55.2 μmol·L^-1。其他化合物在反应体系中不溶解和/或对酶活性的抑制作用很小(IC₅₀>200 μmol·L^-1)。本文首次设计VatD的小分子化合物抑制剂,发现了3个有活性的化合物,希望其可作为先导化合物进一步发展为新的对抗链阳霉素耐药性的药物。     

肿瘤治疗靶向药物Aurora激酶抑制剂

Aurora激酶在肿瘤细胞的有丝分裂进程中起着重要的调控作用,从而影响细胞周期进程,是抗肿瘤药物的新靶点。本文就Aurora激酶在肿瘤细胞生长中所起的作用及现阶段的Aurora激酶抑制剂进行系统介绍。     

Rho激酶及其抑制剂的研究进展

Rho激酶是近十年来发现参与细胞运动的主要激酶之一，对细胞的分裂、收缩、粘附、迁移、分泌等活动具有重要调节作用。Rho激酶的高表达或过度激活与许多心脑血管疾病的发生发展密切相关，Rho激酶现在已经成为新药研发的重要靶点，而Rho激酶抑制剂的不断发现为心血管、神经系统等疾病的治疗提供了新的希望。为此，本文就Rho激酶及其抑制剂的研究做一简要综述。     

Multicomplex-based pharmacophore-guided 3D-QSAR studies of N-substituted 2'-(aminoaryl)benzothiazoles as Aurora-A inhibitors

Aurora-A has been known as one of the most important targets for cancer therapy, and some Aurora-A inhibitors have entered clinical trails. In this study, combination of the ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship of known Aurora-A inhibitors, and multicomplex-based pharmacophore-guided method has been suggested to generate a comprehensive pharmacophore of Aurora-A kinase based on a collection of crystal structures of Aurora-A-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 37 structurally diverse N-substituted 2'-(aminoaryl)benzothiazoles derivatives. The quantitative structure-activity relationship analyses have been performed on these Aurora-A inhibitors based on multicomplex-based pharmacophore-guided alignment. These results may provide important information for further design and virtual screening of novel Aurora-A inhibitors.     

新方法随机筛选血管内皮生长因子受体1激酶的抑制剂

目的:利用重组血管内皮细胞生长因子(VEGF)受体1激酶蛋白质建立大规模随机筛选分子模型.方法:重组人源VEGF受体1激酶蛋白质的催化活性通过酶联免疫法检测96孔板上的底物磷酸化程度得到.用大规模随机筛选寻找抑制剂,并在稳定表达VEGF受体1的细胞系上研究它们的性质.结果:在研究VEGF受体1激酶蛋白质大肠杆菌表达体系的基础上,建立了大规模筛选模型.通过对2800个有机化合物的筛选,找到了两个二取代呋喃类的VEGF受体1激酶抑制剂(A1和A5).其中化合物A1能抑制底物磷酸化,而化合物A5则对激酶的自磷酸化和底物磷酸化都能抑制.同时A1和A5都能影响转染细胞上的受体磷酸化作用.结论:利用重组受体激酶建立的分子模型为寻找抗肿瘤血管生成抑制剂提供了一个简单而有效的方法.     

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations.

Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors.

Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor–ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS’s position in pharmaceutical research.     

Discovery of novel acyl coenzyme a: cholesterol acyltransferase inhibitors: pharmacophore-based virtual screening, synthesis and pharmacology

The present study describes ligand‐based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r = 0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross‐validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds ( 47 and 48 ; predicted IC₅₀ = 1 nm ) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10 μg/mL) with IC₅₀ value of 3.6 and 2.5 nm , respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors.     

Ligand-based virtual screening and molecular docking studies to identify the critical chemical features of potent cathepsin D inhibitors

Cathepsin D is a major component of lysosomes and plays a major role in catabolism and degenerative diseases. The quantitative structure-activity relationship study was used to explore the critical chemical features of cathepsin D inhibitors. Top 10 hypotheses were built based on 36 known cathepsin D inhibitors using HypoGen/Discovery Studio v2.5. The best hypothesis Hypo1 consists of three hydrophobic, one hydrogen bond acceptor lipid, and one hydrogen bond acceptor features. The selected Hypo1 model was cross-validated using Fischer's randomization method to identify the strong correlation between experimental and predicted activity value as well as the test set and decoy sets used to validate its predictability. Moreover, the best hypothesis was used as a 3D query in virtual screening of Scaffold database. Subsequently, the screened hit molecules were filtered by applying Lipinski's rule of five, absorption, distribution, metabolism, and toxicity, and molecular docking studies. Finally, 49 compounds were obtained as potent cathepsin D inhibitors based on the consensus scoring values, critical interactions with protein active site residues, and predicted activity values. Thus, we suggest that the application of Hypo1 could assist in the selection of potent cathepsin D leads from various databases. Hence, this model was used as a valuable tool to design new candidate for cathepsin D inhibitors.