Virtual screening to successfully identify novel janus kinase 3 inhibitors: a sequential focused screening approach

In an effort to identify novel Janus kinase 3 inhibitors, a sequential focused screening approach was adopted to search our in-house chemical database. By biologically testing only 79 selected compounds, we successfully identified 19 compounds showing IC 50 < 20 microM, with four of them in the nanomolar range. Particularly, a 3,5-disubstituted pyrazolo[4,3- d]pyrimidine scaffold emerged as a promising candidate for further lead optimization. With the advantages of efficiency and flexibility, this approach may be utilized to identify leads for other therapeutic targets.     

盐皮质激素受体拮抗剂的虚拟筛选

目的:采用分子对接和分子动力学方法进行盐皮质激素受体拮抗剂(mineralocorticoid receptor antagonists,MRAs)的虚拟筛选,以期发现全新结构的具有潜在治疗糖尿病肾病的先导化合物。方法:收集已知活性的MRAs及其诱饵分子,构建基于结构的盐皮质激素受体虚拟筛选模型,利用该模型对中药成分数据库(Traditional Chinese Medicines Integrated Database,TCMID)进行虚拟筛选,综合对接得分和结合模式挑选2个与阳性药类似的中药成分进行分子动力学模拟,以期发现潜在MRAs。结果:诱饵分子结果表明构建的虚拟筛选模型可用于MRAs化合物库的虚拟筛选,通过对中药成分数据库的虚拟筛选发现30个候选MRAs化合物,其中分子动力学模拟结果表明,水飞蓟莫林的结合自由能略差于已知阳性化合物,可作为MRAs的先导化合物。结论:分子对接结合分子动力学模拟方法可用于从中药成分中筛选新型盐皮质激素受体拮抗剂,为糖尿病肾病药物开发的相关实验研究提供设计思路。     

Cell-based assays and instrumentation for screening ion-channel targets

Ion channels are an important class of drug targets. They comprise the molecular basis for essential physiological functions including fluid secretion, electrolyte balance, bioenergetics and membrane excitability. High-throughput screening for ion-channel function requires sensitive, simple assays and instrumentation that will report ion channel activity in living cells. This article will review relevant assay technologies for ion channels and describe voltage-sensitive probes and instruments based on fluorescence resonance energy transfer (FRET) that enable ion-channel drug discovery.     

RNAi screening for therapeutic targets in human malignancies

The advent of RNA interference (RNAi) based library screening approaches has sparked a surge in loss-of-function genetic screens. Several recent screens have aimed to identify novel regulators of cancer-related phenotypes. These employ various tumor cell types to model malignant cell functions and use different RNAi effector library approaches to reveal a cache of novel tumor regulators. This review surveys recent RNAi screens conducted in transformed human cells.     

Improved lead-finding for kinase targets using high-throughput docking

Protein kinases represent a major class of drug targets for the pharmaceutical industry, and the identification of kinase inhibitors with novel and diverse chemotypes is therefore a high priority. Virtual screening methods are a primary source for the discovery of lead molecules for drug development, with high-throughput docking algorithms being among the most extensively used of these methods. A number of developments in virtual screening technologies have resulted in more effective computer-based compound screening of potential kinase inhibitors. These developments include the following: (i) conformational search methods for pose generation; (ii) improvements in the prediction of protein-ligand binding energy through the use of scoring functions; (iii) the use of interaction filters for identifying ligand poses with known binding determinants; and (iv) the impact of binding site flexibility upon high-throughput docking success. This review discusses the application of these methods in the context of the discovery of kinase adenosine triphosphate antagonists.     

Virtual screening against obesity

The development of novel drugs against obesity is one of the top priorities of worldwide drug research. In recent years, it has been facilitated by the application of virtual screening methods. In this review, we give a short introduction into obesity-related protein targets and computer-aided drug design techniques. Furthermore, we highlight the most successful virtual screening studies, outline their results, and provide suggestions for future anti-obesity drug development.     

Phenotype-first screening for the identification of novel drug targets

Modern drug discovery is predominantly a target-driven process, where success is intricately linked to the selection of an appropriate molecular target. Ideally, there is conclusive functional evidence that a selected target is disease-relevant and, furthermore, suitable for drug development. Phenotype-first screening is a highly attractive approach for target identification because it offers the unique possibility to analyse entire genomes in an unbiased fashion for disease-related phenotypes. Various studies have demonstrated that phenotype-first screening can be successfully applied to the identification of drug targets, thus establishing this approach as a valuable tool for future target discovery efforts.     

Virtual screening for aryl hydrocarbon receptor binding prediction

The overall goal of this study has been to validate computational models for predicting aryl hydrocarbon receptor (AhR) binding. Due to the unavailability of the AhR X-ray crystal structure we have decided to use QSARs models for the binding prediction virtual screening. We have built up CoMFA, Volsurf, and HQSAR models using as a training set 84 AhR ligands. Additionally, we have built a hybrid model combining two of the final selected models in order to give a single operational system. The results show that CoMFA, VolSurf, HQSAR, and the hybrid models gives good results (R(2) equal to 0.91, 0.79, 0.85, and 0.82 and q(2) 0.62, 0.58, 0.62, and 0.70, respectively). Since the techniques analyzed show a good correlation and good prediction also for an external test set, particularly the HQSAR and the hybrid model, we can conclude that these models can be used for predicting AhR binding in virtual screening.     

RNA interference screening for the discovery of oncology targets

RNA interference (RNAi) mediated loss-of-function screens have the potential to delineate biological functions of genes and the proteins they encode. RNAi has proven to be a promising technology for identification and validation of new targets for the pharmacological treatment of many diseases including cancer. Here we review the use of high-throughput RNAi screens, examine the types of targets pursued for oncology indications, and discuss the integration of diverse datasets in both target discovery and drug discovery programs.     

Kinome-wide siRNA screening identifies molecular targets mediating the sensitivity of pancreatic cancer cells to Aurora kinase inhibitors

Aurora kinases are a family of mitotic kinases that play important roles in the tumorigenesis of a variety of cancers including pancreatic cancer. A number of Aurora kinase inhibitors (AKIs) are currently being tested in preclinical and clinical settings as anti-cancer therapies. However, the antitumor activity of AKIs in clinical trials has been modest. In order to improve the antitumor activity of AKIs in pancreatic cancer, we utilized a kinome focused RNAi screen to identify genes that, when silenced, would sensitize pancreatic cancer cells to AKI treatment. A total of 17 kinase genes were identified and confirmed as positive hits. One of the hits was the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), which has been shown to be overexpressed in pancreatic cancer cells and tumor tissues. Imatinib, a PDGFR inhibitor, significantly enhanced the anti-proliferative effect of ZM447439, an Aurora B specific inhibitor, and PHA-739358, a pan-Aurora kinase inhibitor. Further studies showed that imatinib augmented the induction of G2/M cell cycle arrest and apoptosis by PHA-739358. These findings indicate that PDGFRA is a potential mediator of AKI sensitivity in pancreatic cancer cells.     

Druggability indices for protein targets derived from NMR-based screening data

An analysis of heteronuclear-NMR-based screening data is used to derive relationships between the ability of small molecules to bind to a protein and various parameters that describe the protein binding site. It is found that a simple model including terms for polar and apolar surface area, surface complexity, and pocket dimensions accurately predicts the experimental screening hit rates with an R(2) of 0.72, an adjusted R(2) of 0.65, and a leave-one-out Q(2) of 0.56. Application of the model to predict the druggability of protein targets not used in the training set correctly classified 94% of the proteins for which high-affinity, noncovalent, druglike leads have been reported. In addition to understanding the pocket characteristics that contribute to high-affinity binding, the relationships that have been defined allow for quantitative comparative analyses of protein binding sites for use in target assessment and validation, virtual ligand screening, and structure-based drug design.     

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION: Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways. AREAS COVERED: The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques. EXPERT OPINION: The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.     

Protein kinase C isozymes as potential targets for anticancer therapy

Protein kinase C (PKC) comprises a family of isozymes (alpha, betaI, betaII, gamma, delta, epsilon, theta, eta, lambda/iota [mouse/human], and zeta) which are involved in signal transduction from membrane receptors to the nucleus. Activation of PKC by phorbol esters promotes tumor formation, and from that it was concluded that inhibitors of PKC might prevent carcinogenesis or inhibit tumor proliferation. However, the situation is more complicated because the exact function of the different PKC isozymes is not known at present. They have been shown to be involved in synaptic transmissions, the activation of ion fluxes, secretion, cell cycle control, differentiation, proliferation, tumorigenesis, metastasis and apoptosis. Modulators such as bryostatin-1, phospholipid analogues, PKC-activating adriamycin derivatives, CGP41251, UCN-01, and antisense oligonucleotides directed against PKCalpha, have shown antitumor activity in cancer patients. PKC inhibitors are not specific to PKC, but also interact with other signaling molecules, which may contribute to the antitumor effects. Modulators of PKC have also been shown to influence non-MDR1-mediated and MDR1-mediated antitumor drug resistance. This review is focussed on the role of PKC isozymes in human cell proliferation, apoptosis and antitumor drug resistance, and on the use of PKC modulators as antitumor agents.     

Proteomic screening of molecular targets of crocin

Background

Traditional drug discovery approaches are mainly relied on the observed phenotypic changes following administration of a plant extract, drug candidate or natural product. Recently, target-based approaches are becoming more popular. The present study aimed to identify the cellular targets of crocin, the bioactive dietary carotenoid present in saffron, using an affinity-based method.

Methods

Heart, kidney and brain tissues of BALB/c mice were homogenized and extracted for the experiments. Target deconvolution was carried out by first passing cell lysate through an affinity column prepared by covalently attaching crocin to agarose beads. Isolated proteins were separated on a 2D gel, trypsinized in situ and identified by MALDI-TOF/TOF mass spectrometry. MASCOT search engine was used to analyze Mass Data.

Results

Part of proteome that physically interacts with crocin was found to consist of beta-actin-like protein 2, cytochrome b-c1 complex subunit 1, ATP synthase subunit beta, tubulin beta-3 chain, tubulin beta-6 chain, 14-3-3 protein beta/alpha, V-type proton ATPase catalytic subunitA, 60 kDa heat shock protein, creatine kinase b-type, peroxiredoxin-2, cytochrome b-c1 complex subunit 2, acetyl-coA acetyltransferase, cytochrome c1, proteasome subunit alpha type-6 and proteasome subunit alpha type-4.

Conclusion

The present findings revealed that crocin physically binds to a wide range of cellular proteins such as structural proteins, membrane transporters, and enzymes involved in ATP and redox homeostasis and signal transduction.     

Virtual screening in structure-based drug discovery

Recent advances in structure determination and computational methods have encouraged the development of structure-based virtual screening. Here we survey progress in the field and review the most recent methods, validation experiments and real applications, including an in-house example of hit identification for the oncology target Hsp90. These results provide a basis for discussing the current state of structure-based virtual screening and to outline the developments that are expected to have a major impact in the near future.     

Virtual screening of the estrogen receptor

Background: For > 30 years, the estrogen receptor (ER) has been the most important biomarker in breast cancer, principally because of its role in indicating the potential of patients to benefit from endocrine therapy. The search for modulators of ER (selective estrogen receptor modulators) through the use of computational methods such as virtual screening (VS) has redefined the area. Objective: We demonstrate how this receptor has become a key target in the computational (docking and scoring, pharmacophore) arena for algorithm development and validation. The use of quantitative structure–activity relationship for estimation of binding affinity to ER is also discussed, and finally all examples of lead identification through VS are exemplified using several VS campaigns carried out to identify environmental endocrine disruptors. Method: This review comprehensively details all current applications of virtual screening to the estrogen receptor and demonstrates how its use has pushed the boundaries of VS in general. Conclusion: The widespread application of the estrogen receptor to VS has allowed identification of numerous pitfalls within the process flow of VS such as library generation, correct validation procedures for docking/scoring functions, and inclusion of receptor flexibility.     

Virtual high-throughput screening of molecular databases

Virtual high-throughput screening (vHTS) is an efficient and widely applicable method used to identify initial hit compounds for pharmaceutical research. Despite its widespread use, several aspects of protein structure-based vHTS can still be optimized, particularly its accuracy and speed in generating results. Recent developments that address these issues include machine learning and implicit solvation methods. Various machine learning methods are available to improve vHTS accuracy, for example, target-specific optimization of scoring functions, the integration of essential protein-ligand interactions, and the application of negative training data. Implicit solvation methods are exemplified by the molecular mechanics Poisson-Boltzmann solvent accessible surface area approach. Furthermore, grid computing and intelligent database screening approaches are used to improve the speed of vHTS.     

Advances in functional assays for high-throughput screening of ion channels targets

Importance of the field: Ion channels are important targets for many disease areas but are challenging to screen due to lack of technologies enabling robust high-throughput assays, particularly for state-dependent interactions. Areas covered in this review: Current assay technologies used to measure ion channel function are reviewed and assessed for use in high-throughput screening (HTS). An iterative approach to screening is evaluated as an alternative to full collection screening in order to take advantage of low-throughput, high cost assays that yield high quality data. What the reader will gain: The reader will gain an understanding of the advantages and disadvantages of various assay techniques used to screen ion channels and their suitability for use in HTS. Take home message: Assays that directly measure ion channel function are prone to less artifact and higher hit confirmation in screening than those using an indirect measure but they are usually lower throughput. However, an iterative approach to screening can make the relatively lower throughput techniques amenable for use in interrogating large collections of compounds.     

Negative regulation of cyclin-dependent kinase 5 targets by protein kinase C

Cyclin-dependent kinase 5 (Cdk5) is a proline-directed protein serine/threonine kinase essential for brain development and implicated in synaptic plasticity, dopaminergic neurotransmission, drug addiction, and neurodegenerative disorders. Relatively little is known about the molecular mechanisms that regulate the activity of Cdk5 in vivo. In order to determine whether protein kinase C (PKC) regulates Cdk5 activity in the central nervous system, the phosphorylation levels of two Cdk5 substrates were evaluated under conditions of altered PKC activity in vivo. Treatment of acute striatal slices with a PKC-activating phorbol ester caused a time- and dose-dependent decrease in the levels of phospho-Ser6 inhibitor-1, phospho-Ser67 inhibitor-1, and phospho-Thr75 dopamine- and cAMP-regulated phosphoprotein, Mr 32,000 (DARPP-32). This effect was reversed by the PKC inhibitor, Ro-32-0432. Moreover, phospho-Ser6 inhibitor-1, phospho-Ser67 inhibitor-1, and phospho-Thr75 DARPP-32 levels were elevated in brain tissue from mice lacking the gene for PKC-alpha. PKC did not phosphorylate Cdk5 or its cofactor, p25, in vitro. Striatal levels of the Cdk5 cofactor, p35, did not change in response to phorbol ester treatment. Furthermore, Cdk5 immunoprecipitated from striatal slices treated with phorbol ester had unaltered activity toward a control substrate in vitro. These results suggest that PKC exerts its effects on the phosphorylation state of Cdk5 substrates through an indirect mechanism that may involve the regulatory binding partners of Cdk5 other than its neuronal cofactors.