Structure-based approaches to improve selectivity: CDK2-GSK3beta binding site analysis

How many kinases are druggable? A review of our current understanding

There are over 500 human kinases ranging from very well-studied to almost completely ignored. Kinases are tractable and implicated in many diseases, making them ideal targets for medicinal chemistry campaigns, but is it possible to discover a drug for each individual kinase? For every human kinase, we gathered data on their citation count, availability of chemical probes, approved and investigational drugs, PDB structures, and biochemical and cellular assays. Analysis of these factors highlights which kinase groups have a wealth of information available, and which groups still have room for progress. The data suggest a disproportionate focus on the more well characterized kinases while much of the kinome remains comparatively understudied. It is noteworthy that tool compounds for understudied kinases have already been developed, and there is still untapped potential for further development in this chemical space. Finally, this review discusses many of the different strategies employed to generate selectivity between kinases. Given the large volume of information available and the progress made over the past 20 years when it comes to drugging kinases, we believe it is possible to develop a tool compound for every human kinase. We hope this review will prove to be both a useful resource as well as inspire the discovery of a tool for every kinase.

GSK3β as a novel promising target to overcome chemoresistance in pancreatic cancer

Pancreatic cancer is an aggressive malignancy with increasing incidence and poor prognosis due to its late diagnosis and intrinsic chemoresistance. Most pancreatic cancer patients present with locally advanced or metastatic disease characterized by inherent resistance to chemotherapy. These features pose a series of therapeutic challenges and new targets are urgently needed. Glycogen synthase kinase 3 beta (GSK3β) is a conserved serine/threonine kinase, which regulates key cellular processes including cell proliferation, DNA repair, cell cycle progression, signaling and metabolic pathways. GSK3β is implicated in non-malignant and malignant diseases including inflammation, neurodegenerative diseases, diabetes and cancer. GSK3β recently emerged among the key factors involved in the onset and progression of pancreatic cancer, as well as in the acquisition of chemoresistance. Intensive research has been conducted on key oncogenic functions of GSK3β and its potential as a druggable target; currently developed GSK3β inhibitors display promising results in preclinical models of distinct tumor types, including pancreatic cancer. Here, we review the latest findings about GSK-3β biology and its role in the development and progression of pancreatic cancer. Moreover, we discuss therapeutic agents targeting GSK3β that could be administered as monotherapy or in combination with other drugs to surmount chemoresistance. Several studies are also defining potential gene signatures to identify patients who might benefit from GSK3β-based therapeutic intervention. This detailed overview emphasizes the urgent need of additional molecular studies on the impact of GSK3β inhibition as well as structural analysis of novel compounds and omics studies of predictive biomarkers.

How to design potent and selective DYRK1B inhibitors? Molecular modeling study

DYRK1B protein kinase is an emerging anticancer target due to its overexpression in a variety of cancers and its role in cancer chemoresistance through maintaining cancer cells in the G0 (quiescent) state. Consequently, there is a growing interest in the development of potent and selective DYRK1B inhibitors for anticancer therapy. One of the major off-targets is another protein kinase, GSK3β, which phosphorylates an important regulator of cell cycle progression on the same residue as DYRK1B and is involved in multiple signaling pathways. In the current work, we performed a detailed comparative structural analysis of DYRK1B and GSK3β ATP-binding sites and identified key regions responsible for selectivity. As the crystal structure of DYRK1B has never been reported, we built and optimized a homology model by comparative modeling and metadynamics simulations. Calculation of interaction energies between docked ligands in the ATP-binding sites of both kinases allowed us to pinpoint key residues responsible for potency and selectivity. Specifically, the role of the gatekeeper residues in DYRK1B and GSK3β is discussed in detail, and two other residues are identified as key to selectivity of DYRK1B inhibition versus GSK3β. The analysis presented in this work was used to support the design of potent and selective azaindole-quinoline-based DYRK1B inhibitors and can facilitate development of more selective inhibitors for DYRK kinases.

Design and synthesis of (aza)indolyl maleimide-based covalent inhibitors of glycogen synthase kinase 3β

The optimization of both non-covalent interactions and reactive groups led to azaindolyl maleimide compound 38b as a selective and covalent inhibitor against GSK3β.

Theoretical studies on the selective mechanisms of GSK3β and CDK2 by molecular dynamics simulations and free energy calculations

Glycogen synthase kinase 3 (GSK3) is a serine/threonine protein kinase which is widely involved in cell signaling and controls a broad number of cellular functions. GSK3 contains α and β isoforms, and GSK3β has received more attention and becomes an attractive drug target for the treatment of several diseases. The binding pocket of cyclin-dependent kinase 2 (CDK2) shares high sequence identity to that of GSK3β, and therefore, the design of highly selective inhibitors toward GSK3β remains a big challenge. In this study, a computational strategy, which combines molecular docking, molecular dynamics simulations, free energy calculations, and umbrella sampling simulations, was employed to explore the binding mechanisms of two selective inhibitors to GSK3β and CDK2. The simulation results highlighted the key residues critical for GSK3β selectivity. It was observed that although GSK3β and CDK2 share the conserved ATP-binding pockets, some different residues have significant contributions to protein selectivity. This study provides valuable information for understanding the GSK3β-selective binding mechanisms and the rational design of selective GSK3β inhibitors.

Application of Fragment-Based de Novo Design to the Discovery of Selective Picomolar Inhibitors of Glycogen Synthase Kinase-3 Beta

A systematic fragment-based de novo design procedure was developed and applied to discover new potent and selective inhibitors of glycogen synthase kinase-3 beta (GSK3β). Candidate inhibitors were generated to simultaneously maximize the biochemical potency and the specificity for GSK3β through three design steps: identification of the optimal molecular fragments for the three sub-binding regions, design of proper linking moieties to connect the fragmental building blocks, and final scoring of the generated molecules. By virtue of modifying the ligand hydration free energy term in the scoring function using hybrid scaled particle theory and the extended solvent-contact model, we identified several GSK3β inhibitors with biochemical potencies ranging from low nanomolar to picomolar levels. Among them, the two most potent inhibitors (12 and 27) are anticipated to serve as promising starting points of drug discovery for various diseases caused by GSK3β because of the high specificity for the inhibition of GSK3β.

Glycogen Synthase Kinase 3β Promotes the Endocytosis of Transferrin in the African Trypanosome

Human parasite Trypanosoma brucei proliferates in the blood of its host, where it takes up iron via receptor-mediated endocytosis of transferrin (Tf). Mechanisms of Tf endocytosis in the trypanosome are not fully understood. Small molecule lapatinib inhibits Tf endocytosis in T. brucei and associates with protein kinase GSK3β (TbGSK3β). Therefore, we hypothesized that Tf endocytosis may be regulated by TbGSK3β, and we used three approaches (both genetic and small molecule) to test this possibility. First, the RNAi knock-down of TbGSK3β reduced Tf endocytosis selectively, without affecting the uptake of haptaglobin-hemoglobin (Hp-Hb) or bovine serum albumin (BSA). Second, the overexpression of TbGSK3β increased the Tf uptake. Third, small-molecule inhibitors of TbGSK3β, TWS119 (IC50 = 600 nM), and GW8510 (IC50 = 8 nM) reduced Tf endocytosis. Furthermore, TWS119, but not GW8510, selectively blocked Tf uptake. Thus, TWS119 phenocopies the selective endocytosis effects of a TbGSK3β knockdown. Two new inhibitors of TbGSK3β, LY2784544 (IC50 = 0.6 μM) and sorafenib (IC50 = 1.7 μM), were discovered in a focused screen: at low micromolar concentrations, they prevented Tf endocytosis as well as trypanosome proliferation (GI50's were 1.0 and 3.1 μM, respectively). These studies show that (a) TbGSK3β regulates Tf endocytosis, (b) TWS119 is a small-molecule tool for investigating the endocytosis of Tf,

Pharmacophore Modeling, Ensemble Docking, Virtual Screening, and Biological Evaluation on Glycogen Synthase Kinase-3β

Glycogen synthase kinase-3 (GSK-3) is a multifunctional serine/threonine protein kinase which is engaged in a variety of signaling pathways, regulating a wide range of cellular processes. GSK-3β, also known as tau protein kinase I (TPK-I), is one of the most important kinases implicated in the hyperphosphorylation of tau that leads to neurodegenerative diseases. Hence, GSK-3β has emerged as an important therapeutic target. To identify compounds that are structurally novel and diverse compared to previously reported ATP-competitive GSK-3β inhibitors, we performed virtual screening by implementing a mixed ligand/structure-based approach, which included pharmacophore modeling, diversity analysis, and ensemble docking. The sensitivities of different docking protocols to induced-fit effects were explored. An enrichment study was employed to verify the robustness of ensemble docking, using 13 X-ray structures of GSK-3β, compared to individual docking in terms of retrieving active compounds from a decoy dataset. A total of 24 structurally diverse compounds obtained from the virtual screening underwent biological validation. The bioassay results showed that 15 out of the 24 hit compounds are indeed GSK-3β inhibitors, and among them, one compound exhibiting sub-micromolar inhibitory activity is a reasonable starting point for further optimization.

Chemogenomics in drug discovery: computational methods based on the comparison of binding sites

Novel computational methods for understanding relationships between ligands and all possible biological targets have emerged in recent years. Proteins are connected to each other based on the similarity of their ligands or based on the similarity of their binding sites. The assumption is that compounds sharing chemical similarity should share targets and that targets with a similar binding site should also share ligands. A large number of computational techniques have been developed to assess ligand and binding site similarity, which can be used to make quantitative predictions of the most probable biological target of a given compound. This review covers the recent advances in new computational methods for relating biological targets based on the similarity of their binding sites. Binding site comparisons are used for the prediction of their most likely ligands, their possible cross reactivity and selectivity. These comparisons can also be used to infer the function of novel uncharacterized proteins.

QSAR study and molecular docking on indirubin inhibitors of Glycogen Synthase Kinase-3

The current study describes the development of in silico models based on a novel alternative of the MTD-PLS methodology (Partial-Least-Squares variant of Minimal Topologic Difference) developed by our group to predict the inhibition of GSK-3β by indirubin derivatives. The new MTD-PLS methodology involves selection rules for the PLS equation coefficients based on physico-chemical considerations aimed at reducing the bias in the output information. These QSAR models have been derived using calculated fragmental descriptors relevant to binding including polarizability, hydrophobicity, hydrogen bond donor, hydrogen bond acceptor, volume and electronic effects. The MTD-PLS methodology afforded moderate but robust statistical characteristics (R2 Y(CUM) = 0.707, Q2(CUM) = 0.664). The MTD-PLS model obtained has been validated in terms of predictive ability by joined internal-external cross-validation applying Golbraikh-Tropsha criteria and Y-randomization test. The information supplied by the MTD-PLS model has been evaluated against Fujita-Ban outcomes that afforded a statistically reliable model (R2=0.923). Furthermore, the results originated from QSAR models were laterally validated with docking insights that suggested the substitution pattern for the design of new indirubins with improved pharmacological potential against GSK-3β. The new restriction rules introduced in this paper are applicable and provide reliable results in accordance with physico-chemical reality.

3D-QSAR and molecular docking studies on 3-anilino-4-arylmaleimide derivatives as glycogen synthase kinase-3β inhibitors

Glycogen synthase kinase-3, a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders, and Alzheimer's disease. It is important to inhibit glycogen synthase kinase-3 selectively, and the net effect of the glycogen synthase kinase-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure-activity relationship studies on novel class of 3-anilino-4-arylmaleimide derivatives to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for the alignment of the molecules. Statistically significant CoMSIA (r2(cv)=0.614, r2(ncv)=0.948) and comparative molecular field analysis (r2(cv) =0.652, r2(ncv)=0.958) models were generated using 57 molecules in training set. The predictive ability of CoMSIA and comparative molecular field analysis models was determined using a test set of 17 molecules, which gave predictive correlation coefficients (r2(pred)) of 0.87 and 0.82, respectively, indicating good predictive power. Based on the information derived from CoMSIA and comparative molecular field analysis contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new anilinoarylmaleimide derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems, hence suggesting that newly designed molecules can be more potent and selective toward glycogen synthase kinase-3β inhibition.

Binding studies and quantitative structure-activity relationship of 3-amino-1H-indazoles as inhibitors of GSK3β

Docking of 3-amino-1H-indazoles complexed with glycogen synthase kinase 3 beta (GSK3β) was performed to gain insight into the structural requirements and preferred conformations of these inhibitors. The study was conducted on a selected set of 57 compounds with variation in structure and activity. We found that the most active compounds established three hydrogen bonds with the residues of the hinge region of GSK3β, but some of the less active compounds have other binding modes. In addition, models able to predict GSK3β inhibitory activities (IC(50) ) of the studied compounds were obtained by 3D-QSAR methods CoMFA and CoMSIA. Ligand-based and receptor-guided alignment methods were utilized. Adequate R(2) and Q(2) values were obtained by each method, although some striking differences existed between the obtained contour maps. Each of the predictive models exhibited a similar ability to predict the activity of a test set. The application of docking and quantitative structure-activity relationship together allowed conclusions to be drawn for the choice of suitable GSK3β inhibitors.

Self-organizing fuzzy graphs for structure-based comparison of protein pockets

Patterns of receptor-ligand interaction can be conserved in functionally equivalent proteins even in the absence of sequence homology. Therefore, structural comparison of ligand-binding pockets and their pharmacophoric features allow for the characterization of so-called "orphan" proteins with known three-dimensional structure but unknown function, and predict ligand promiscuity of binding pockets. We present an algorithm for rapid pocket comparison (PoLiMorph), in which protein pockets are represented by self-organizing graphs that fill the volume of the cavity. Vertices in these three-dimensional frameworks contain information about the local ligand-receptor interaction potential coded by fuzzy property labels. For framework matching, we developed a fast heuristic based on the maximum dispersion problem, as an alternative to techniques utilizing clique detection or geometric hashing algorithms. A sophisticated scoring function was applied that incorporates knowledge about property distributions and ligand-receptor interaction patterns. In an all-against-all virtual screening experiment with 207 pocket frameworks extracted from a subset of PDBbind, PoLiMorph correctly assigned 81% of 69 distinct structural classes and demonstrated sustained ability to group pockets accommodating the same ligand chemotype. We determined a score threshold that indicates "true" pocket similarity with high reliability, which not only supports structure-based drug design but also allows for sequence-independent studies of the proteome.

Cell division cycle 7 kinase inhibitors: 1H-pyrrolo[2,3-b]pyridines, synthesis and structure-activity relationships

Cdc7 kinase has recently emerged as an attractive target for cancer therapy and low-molecular-weight inhibitors of Cdc7 kinase have been found to be effective in the inhibition of tumor growth in animal models. In this paper, we describe synthesis and structure-activity relationships of new 1H-pyrrolo[2,3-b]pyridine derivatives identified as inhibitors of Cdc7 kinase. Progress from (Z)-2-phenyl-5-(1H-pyrrolo[2,3-b]pyridin-3-ylmethylene)-3,5-dihydro-4H-imidazol-4-one (1) to [(Z)-2-(benzylamino)-5-(1H-pyrrolo[2,3-b]pyridin-3-ylmethylene)-1,3-thiazol-4(5H)-one] (42), a potent ATP mimetic inhibitor of Cdc7 kinase with IC(50) value of 7 nM, is also reported.

From a natural product lead to the identification of potent and selective benzofuran-3-yl-(indol-3-yl)maleimides as glycogen synthase kinase 3beta inhibitors that suppress proliferation and survival of pancreatic cancer cells

Recent studies have demonstrated that glycogen synthase kinase 3beta (GSK-3beta) is overexpressed in human colon and pancreatic carcinomas, contributing to cancer cell proliferation and survival. Here, we report the design, synthesis, and biological evaluation of benzofuran-3-yl-(indol-3-yl)maleimides, potent GSK-3beta inhibitors. Some of these compounds show picomolar inhibitory activity toward GSK-3beta and an enhanced selectivity against cyclin-dependent kinase 2 (CDK-2). Selected GSK-3beta inhibitors were tested in the pancreatic cancer cell lines MiaPaCa-2, BXPC-3, and HupT3. We determined that some of these compounds, namely compounds 5, 6, 11, 20, and 26, demonstrate antiproliferative activity against some or all of the pancreatic cancer cells at low micromolar to nanomolar concentrations. We found that the treatment of pancreatic cancer cells with GSK-3beta inhibitors 5 and 26 resulted in suppression of GSK-3beta activity and a distinct decrease of the X-linked inhibitor of apoptosis (XIAP) expression, leading to significant apoptosis. The present data suggest a possible role for GSK-3beta inhibitors in cancer therapy, in addition to their more prominent applications in CNS disorders.

Structure-based chemogenomics: analysis of protein family landscapes

Analysis of the three-dimensional structures of protein ligand complexes provides valuable insight into both the common interaction patterns within a target family and the discriminating features between the different members of a target family. Knowledge of the common interaction patterns helps to design target family focused chemical libraries for hit finding, while the discriminating features can be exploited to optimize the selectivity profile of a lead compound against particular member of a target family. Herein, we review the computational tools which have been developed to analyze crystal structures of members of a target family.

3D-QSAR and molecular docking study on bisarylmaleimide series as glycogen synthase kinase 3, cyclin dependent kinase 2 and cyclin dependent kinase 4 inhibitors: An insight into the criteria for selectivity

Selective glycogen synthase kinase 3 (GSK3) inhibition over cyclin dependent kinases such as cyclin dependent kinase 2 (CDK2) and cyclin dependent kinase 4 (CDK4) is an important requirement for improved therapeutic profile of GSK3 inhibitors. The concepts of selectivity and additivity fields have been employed in developing selective CoMFA models for these related kinases. Initially, sets of three individual CoMFA models were developed, using 36 compounds of bisarylmaleimide series to correlate with the GSK3, CDK2 and CDK4 inhibitory potencies. These models showed a satisfactory statistical significance: CoMFA-GSK3 (r(2)(con), r(2)(cv): 0.931, 0.519), CoMFA-CDK2 (0.937, 0.563), and CoMFA-CDK4 (0.892, 0.725). Three different selective CoMFA models were then developed using differences in pIC(50) values. These three models showed a superior statistical significance: (i) CoMFA-Selective1 (r(2)(con), r(2)(cv): 0.969, 0.768), (ii) CoMFA-Selective 2 (0.974, 0.835) and (iii) CoMFA-Selective3 (0.963, 0.776). The selective models were found to outperform the individual models in terms of the quality of correlation and were found to be more informative in pinpointing the structural basis for the observed quantitative differences of kinase inhibition. An in-depth comparative investigation was carried out between the individual and selective models to gain an insight into the selectivity criterion. To further validate this approach, a set of new compounds were designed which show selectivity and were docked into the active site of GSK3, using FlexX based incremental construction algorithm.

6-Substituted Pyrrolo[3,4-c]pyrazoles: An Improved Class of CDK2 Inhibitors

We have recently reported a new class of CDK2/cyclin A inhibitors based on a bicyclic tetrahydropyrrolo[3,4-c]pyrazole scaffold. The introduction of small alkyl or cycloalkyl groups in position 6 of this scaffold allowed variation at the other two diversity points. Conventional and polymer-assisted solution phase chemistry provided a way of generating compounds with improved biochemical and cellular activity. Optimization of the physical properties and pharmacokinetic profile led to a compound which exhibited good efficacy in vivo on A2780 human ovarian carcinoma.

A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application

A fast new algorithm (Fingerprints for Ligands And Proteins or FLAP) able to describe small molecules and protein structures using a common reference framework of four-point pharmacophore fingerprints and a molecular-cavity shape is described in detail. The procedure starts by using the GRID force field to calculate molecular interaction fields, which are then used to identify particular target locations where an energetic interaction with small molecular features would be very favorable. The target points thus calculated are then used by FLAP to build all possible four-point pharmacophores present in the given target site. A related approach can be applied to small molecules, using directly the GRID atom types to identify pharmacophoric features, and this complementary description of the target and ligand then leads to several novel applications. FLAP can be used for selectivity studies or similarity analyses in order to compare macromolecules without superposing them. Protein families can be compared and clustered into target classes, without bias from previous knowledge and without requiring protein superposition, alignment, or knowledge-based comparison. FLAP can be used effectively for ligand-based virtual screening and structure-based virtual screening, with the pharmacophore molecular recognition. Finally, the new method can calculate descriptors for chemometric analysis and can initiate a docking procedure. This paper presents the background to the new procedure and includes case studies illustrating several relevant applications of the new approach.

Structure–activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors.

Comparative analysis of the surface interaction properties of the binding sites of CDK2, CDK4, and ERK2

Recently developed hydrogen-bonding and hydrophobic analysis algorithms were used to investigate the interaction properties of the ATP binding sites of CDK2, CDK4, and ERK2. We were able to prioritise those hydrogen-bonding groups that are observed to bind the native ATP ligand, as well as to identify other important groups found to bind inhibitors of these enzymes. However, as the hydrogen-bonding groups in the ATP binding sites of these enzymes are fairly well-conserved, we have confirmed that inhibitor selectivity may be predominantly due to differences in either the hydrophobic or steric properties of their binding sites. In particular, the hydrophobic properties of regions outside the specificity surface were observed to provide a rationale for the differences in specificity between various inhibitors to these enzymes. Our method was thus able to identify variations in hydrophobicity. The greater hydrophobicity of certain regions of CDK4 over analogous regions in CDK2 was detectable; likewise, it was possible to distinguish variations in hydrophobicity for regions of CDK2 against those in ERK2, despite the fact that these regions are largely composed of similar residue types.

3D-QSAR and molecular docking studies on pyrazolopyrimidine derivatives as glycogen synthase kinase-3beta inhibitors

Glycogen synthase kinase-3 (GSK-3), a serine/threonine kinase, is a fascinating enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as diabetes mellitus, cancer, chronic inflammation, bipolar disorders and Alzheimer's disease. It is important to inhibit GSK-3 selectively and the net effect of the GSK-3 inhibitors thus should be target specific, over other phylogenetically related kinases such as CDK-2. In the present work, we have carried out three-dimensional quantitative structure activity relationship (3D-QSAR) studies on novel class of pyrazolopyrimidine derivatives as GSK-3 inhibitors reported to have improved cellular activity. Docked conformation of the most active molecule in the series, which shows desirable interactions in the receptor, was taken as template for alignment of the molecules. Statistically significant CoMFA and CoMSIA models were generated using 49 molecules in training set. By applying leave-one-out (LOO) cross-validation study, r(cv)2 values of 0.53 and 0.48 for CoMFA and CoMSIA, respectively and non-cross-validated (r(ncv)2) values of 0.98 and 0.92 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of CoMFA and CoMSIA models was determined using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)2) of 0.47 and 0.48, respectively, indicating good predictive power. Based upon the information derived from CoMFA and CoMSIA contour maps, we have identified some key features that explain the observed variance in the activity and have been used to design new pyrazolopyrimidine derivatives. The designed molecules showed better binding affinity in terms of estimated docking scores with respect to the already reported systems; hence suggesting that newly designed molecules can be more potent and selective towards GSK-3beta inhibition.

New leads for selective GSK-3 inhibition: pharmacophore mapping and virtual screening studies

Glycogen Synthase Kinase-3 is a regulatory serine/threonine kinase, which is being targeted for the treatment of a number of human diseases including type-2 diabetes mellitus, neurodegenerative diseases, cancer and chronic inflammation. Selective GSK-3 inhibition is an important requirement owing to the possibility of side effects arising from other kinases. A pharmacophore mapping strategy is employed in this work to identify new leads for selective GSK-3 inhibition. Ligands known to show selective GSK-3 inhibition were employed in generating a pharmacophore map using distance comparison method (DISCO). The derived pharmacophore map was validated using (i) important interactions involved in selective GSK-3 inhibitions, and (ii) an in-house database containing different classes of GSK-3 selective, non-selective and inactive molecules. New Lead identification was carried out by performing virtual screening using validated pharmacophoric query and three chemical databases namely NCI, Maybridge and Leadquest. Further data reduction was carried out by employing virtual filters based on (i) Lipinski's rule of 5 (ii) van der Waals bumps and (iii) restricting the number of rotatable bonds to seven. Final screening was carried out using FlexX based molecular docking study.