Virtual Screening Of Chemical Databases Research Articles

Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation

In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model ‘Hypo 1’ showed excellent correlation (R2 = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R2 = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn2+ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.

Insights of structure-based pharmacophore studies and inhibitor design against Gal3 receptor through molecular dynamics simulations

Our present work studies the structure-based pharmacophore modeling and designing inhibitor against Gal3 receptor through molecular dynamics (MD) simulations extensively. Pharmacophore models play a key role in computer-aided drug discovery like in the case of virtual screening of chemical databases, de novo drug design and lead optimization. Structure-based methods for developing pharmacophore models are important, and there have been a number of studies combining such methods with the use of MD simulations to model protein’s flexibility. The two potential antagonists SNAP 37889 and SNAP 398299 were docked and simulated for 250 ns and the results are analyzed and carried for the structure-based pharmacophore studies. This helped in identification of the subtype selectivity of the binding sites of the Gal3 receptor. Our work mainly focuses on identifying these binding site residues and to design more potent inhibitors compared to the previously available inhibitors through pharmacophore models. The study provides crucial insight into the binding site residues Ala2, Asp3, Ala4, Gln5, Phe24, Gln79, Ala80, Ile82, Tyr83, Trp88, His99, Ile102, Tyr103, Met106, Tyr157, Tyr161, Pro174, Trp176, Arg181, Ala183, Leu184, Asp185, Thr188, Trp248, His251, His252, Ile255, Leu256, Phe258, Trp259, Tyr270, Arg273, Leu274 and His277, which plays a significant role in the conformational changes of the receptor and helps to understand the inhibition mechanism. Communicated by Ramaswamy H. Sarma

Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the architecture of the neural network, to form virtual compound library of potential anti-HIV-1 agents for training the neural network, to make molecular docking of all compounds from this library with gp120, to calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder. The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

Ligand and structure based virtual screening of chemical databases to explore potent small molecule inhibitors against breast invasive carcinoma using recent computational technologies

Breast carcinoma is the most common invasive cancer to affect the women in the North America and the world. Cancer of breast is the number one cancer overall with estimated 1.5 lakh new cases during 2016. The success of the current endocrine therapies is often limited due to the development of resistance. Therefore, there is a need to develop new lead compounds for breast cancer treatment. As 70% of breast carcinoma is ER+, and it is well known previously that estrogen receptor alpha (ERα) is overexpressed in ER + cases, so in the current work we attempt to develop some novel potent analogues against ERα. To achieve this, we have adopted an integrative computational approach that involves multiple sequence alignment, virtual screening (ligand and structure based), molecular docking, fingerprint based clustering and molecular dynamics simulation. The approach envisaged vital information about the binding site residues, conserved sequence among different species, ligand and protein conformations, binding energy of compound to bind into the active site of the receptor. Molecular docking analysis revealed that some analogues exhibited significant binding towards ERα. The top docked complexes showing good docking scores, hydrogen bond and hydrophobic interactions were selected for molecular dynamics simulation studies. RMSD revealed that the systems were quite stable with RMSD value below 3 Å. The RMSF analysis calculated residue wise fluctuations and revealed that the residues are flexible enough to interact with the ligand. The residue at C-terminal showed more flexibility as compared to other residues. To confirm binding of these analogues, MMGBSA analysis was performed which revealed binding energy of the ligands. Further, per-residue decomposition energy analysis revealed that Glu353, Leu346, Leu387 and Arg394 contributed towards ligand binding. The results visibly indicated that MMGBSA can act as filter in virtual screening experiments and play a major role in facilitating drug discovery.

Using 3D-QSAR to predict the separation efficiencies of flotation collectors: Implications for rational design of non-polar side chains

Three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were innovatively introduced into the structure-performance study of flotation collectors using topomer comparative molecular field analysis (Topomer CoMFA) and comparative molecular similarity indices analysis (CoMSIA). A total of eighteen cupferron derivatives was used to build up the 3D-QSAR models. The resultant Topomer CoMFA and CoMSIA models are both statistically significant. The Topomer CoMFA model yielded the non-cross-validated correlation coefficient (r2) and the leave-one-out correlation coefficient (q2) of 0.931 and 0.707, respectively. The corresponding r2 and q2 of the CoMSIA model were 0.816 and 0.651, respectively. The theoretically predicted separation efficiencies are in excellent agreement with the experimentally measured values, validating the high predictive ability of the 3D-QSAR models. Valuable information concerning the hydrophobic, steric and electrostatic properties of substituted cupferrons has been obtained and analysed in detail. The 3D-QSAR contour maps indicate that the aliphatic chains with significant hydrophobicity together with electron rich groups at 4-position of the benzene are of utmost importance for the flotation performance of substituted cupferrons. The addition of the electron-withdrawing group to the 2, 3-positions of the benzene ring is undesirable for enhancing the separation efficiencies. Structural modification of substituted cupferrons has been evaluated and novel cupferron derivatives with theoretically improved separation efficiency have been proposed. Despite the fact that 3D-QSAR methods have matured into a wide variety of applications such as drug discovery, catalyst science, environmental science, pesticide science, computational toxicology and material science, this work is the first attempt of introducing 3D-QSAR into the structure-activity relationship of flotation collectors. The generated 3D-QSAR models in this work could provide a new perspective on the structure-activity relationship of flotation collectors and potentially hold great promise for the rational design, selection and prediction of untested flotation collectors through virtual screening of chemical database.

Towards Dynamic Pharmacophore Models by Coarse Grain Molecular Dynamics

Pharmacophore models play a key role in computer aided drug discovery e.g. in virtual screening of chemical databases, de novo drug design, and lead optimization. Structure-based methods for developing pharmacophore models are of particular importance, and there have been a number of studies combining such methods with the use of molecular dynamics (MD) simulations to model protein flexibility. At the same time, ongoing developments in multi-scale simulations, in which atomistic and CG MD simulations are combined in a sequential fashion, have been successfully used to explore the interactions of proteins with membranes and their lipids [1,2]. Here we describe the use of CG-MD simulations to explore the interactions of CG-particle probes with proteins, combined with cavity detection methods, in order to identify potential protein binding sites. Using cyclin dependent kinase 2 (CDK2) as a test case, we demonstrate the potential of this approach for identification of ligand binding sites, and as part of an overall process of structure-based development of pharmacophore models.

Computer-aided identification of new histone deacetylase 6 selective inhibitor with anti-sepsis activity

Histone deacetylase (HDAC) inhibitors have been recognized as promising approaches to the treatment of various human diseases including cancer, inflammation, neurodegenerative diseases, and metabolic disorders. Several pan-HDAC inhibitors are currently approved only as anticancer drugs. Interestingly, SAHA (vorinostat), one of clinically available pan-HDAC inhibitors, shows an anti-inflammatory effect at concentrations lower than those required for inhibition of tumor cell growth. It was also reported that HDAC6 selective inhibitor tubastatin A has anti-inflammatory and anti-rheumatic effect. In our efforts to develop novel HDAC inhibitors, we rationally designed various HDAC inhibitors based on the structures of two hit compounds identified by virtual screening of chemical database. Among them, 9a ((E)-N-hydroxy-4-(2-styrylthiazol-4-yl)butanamide) was identified as a HDAC6 selective inhibitor (IC50 values of 0.199 μM for HDAC6 versus 13.8 μM for HDAC1), and it did not show significant cytotoxicity against HeLa cells. In vivo biological evaluation of 9a was conducted on a lipopolysaccharide (LPS)-induced mouse model of sepsis. The compound 9a significantly improved 40% survival rate (P = 0.0483), and suppressed the LPS-induced increase of TNF-α and IL-6 mRNA expression in the liver of mice. Our study identified novel HDAC6 selective inhibitor 9a, which may serve as a potential lead for the development of anti-inflammatory or anti-sepsis agents.

Energy-Based Pharmacophore and Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR) Modeling Combined with Virtual Screening To Identify Novel Small-Molecule Inhibitors of Silent Mating-Type Information Regulation 2 Homologue 1 (SIRT1)

Silent mating-type information regulation 2 homologue 1 (SIRT1), being the homologous enzyme of silent information regulator-2 gene in yeast, has multifaceted functions. It deacetylates a wide range of histone and nonhistone proteins; hence, it has good therapeutic importance. SIRT1 was believed to be overexpressed in many cancers (prostate, colon) and inflammatory disorders (rheumatoid arthritis). Hence, designing inhibitors against SIRT1 could be considered valuable. Both structure-based and ligand-based drug design strategies were employed to design novel inhibitors utilizing high-throughput virtual screening of chemical databases. An energy-based pharmacophore was generated using the crystal structure of SIRT1 bound with a small molecule inhibitor and compared with a ligand-based pharmacophore model that showed four similar features. A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed and validated to be employed in the virtual screening protocol. Among the designed compounds, Lead 17 emerged as a promising SIRT1 inhibitor with IC50 of 4.34 μM and, at nanomolar concentration (360 nM), attenuated the proliferation of prostate cancer cells (LnCAP). In addition, Lead 17 significantly reduced production of reactive oxygen species, thereby reducing pro inflammatory cytokines such as IL6 and TNF-α. Furthermore, the anti-inflammatory potential of the compound was ascertained using an animal paw inflammation model induced by carrageenan. Thus, the identified SIRT1 inhibitors could be considered as potent leads to treat both cancer and inflammation.

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling.

Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.

Virtual Screening, Molecular Docking and Molecular Dynamics Studies For Discovery of Novel Vegfr-2 Inhibitors

VEGFR-2 is considered as potential target for cancer therapy. In this work, the stability, binding mode between the VEGFR-2 protein and its ligand have been evaluated using the pharmacophore guided virtual screening (VS), molecular docking, and molecular dynamics (MD) simulations. The small molecule VEGFR-2 inhibitors were identified through virtual screening of chemical databases based on pharmacophore guided VS approach, that searches multi-conformer representations efficiently using PHASE module of Schrodinger. In addition, the molecular docking, using GLIDE module of Schrodinger; and molecular dynamics simulation, using GROMACS software were performed to study the interaction between the protein and the ligand. Molecular docking enables the extraordinary structural diversity of synthetic products to be harnessed in an efficient manner. The best six ligands (ZINC01056202, ZINC06091460, ZINC06091450, ZINC04107510, ZINC04623218, and ZINC81582433) with different scaffolds are selected from docking studies. VEGFR-2 and ligand complex was found to be stable at room temperature demonstrated by 1000 ps molecular dynamic simulation study using water as a solvent. The predicted inhibitors are quite novel compared with the known VEGFR-2 inhibitors. The work provides insight for molecular understanding of VEGFR-2 and can be used for development of anticancer drugs.

Exploration of Virtual Candidates for Human HMG-CoA Reductase Inhibitors Using Pharmacophore Modeling and Molecular Dynamics Simulations

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.

Designing and Binding Mode Prediction of Juvenile Hormone Analogues as Potential Inhibitor for Galleria mellonella

Virtual screening of chemical databases has become an integral part of ligand design. Docking is one of the most important methods in computer assisted screening. If a three dimensional structure of target receptor is available, along with information regarding nature of the ligand-binding site, ligand-binding mode; the interactions between the ligand and receptor can be studied extensively, in order to design and develop target specific new compounds in a short time period. Juvenile Hormone Analogues, sesqui-terpenoid series of compound act as an insect growth regulator, and presently in use as a potential environment friendly pesticide. Juvenile hormone is the molting hormone responsible for each molt, and involved in a wide range of physiological processes in both developing and mature insect. Designing of various juvenile hormone analogues are new and emerging area to counter the insect problem. In this paper, we report protein-ligand interactions using a standard protocol of docking. We perform screening of synthesized (A-B) and proposed (C-D) series of Juvenile Hormone Analogues with hemolymph binding proteins of Galleria mellonella. Further binding energy profile of all the series have been compared with the phenoxy derivatives of juvenile hormone mimics, as well as natural JH III, in order to design targeted JHAs with improved biological activities. Our proposed series of juvenile hormone analogues exhibit better energy profile over in use phenoxy derivatives.

Development of 3D-pharmacophore model followed by successive virtual screening, molecular docking and ADME studies for the design of potent CCR2 antagonists for inflammation-driven diseases

In order to elucidate the essential structural features for CC chemokine receptor 2 (CCR2) antagonism, 3D-pharmacophore hypotheses were built based on a set of known compounds from the literature. The hypotheses were developed with the aid of HypoGen module within Discovery Studio 2.5 program. Multiple validation approaches provided the confidence in utilising the predictive pharmacophore models developed in this study. The most predictive pharmacophore model (Hypo1) was found to be statistically significant along with its ability to predict activities of the known CCR2 antagonists in the training and test set with high correlation coefficient. The best model was then used as a 3D search query in the virtual screening of chemical databases including ChemDiv and MiniMaybridge. Lipinski's rule of five and molecular docking studies were applied to the screened hits for retrieving potential lead compounds. Eight hits showed better in silico CCR2-binding affinities than the reported CCR2 antagonists, along with good absorption, distribution, metabolism and excretion profiles. The current 3D-quantitative structure–activity relationship (QSAR) pharmacophore modelling and molecular docking studies attempt to elucidate QSAR for CCR2 antagonism and identify novel potent CCR2 antagonist scaffolds.

Novel indoline-2,3-dione derivatives as inhibitors of aminopeptidase N (APN)

Aminopeptidase N (APN/CD13), as a zinc-containing ectoenzyme, plays a critical role in the process of tumor angiogenesis, invasion and metastasis. Through the docking-based virtual screening of chemical databases and the further activity assay, we discovered that compound 10c exhibits potent and selective inhibitory ability towards APN. In addition, a series of indoline-2,3-dione derivates have been designed and synthesized as APN inhibitors. The results of preliminary activity evaluation showed that compound 12a (IC50=0.074±0.0026μM) exhibited the best inhibitory activity against APN, which could be used for further anticancer agent research.

Integrated Virtual Screening for the Identification of Novel and Selective Peroxisome Proliferator-Activated Receptor (PPAR) Scaffolds

We describe a fully customizable and integrated target-specific "tiered" virtual screening approach tailored to identifying and characterizing novel peroxisome proliferator activated receptor γ (PPARγ) scaffolds. Built on structure- and ligand-based computational techniques, a consensus protocol was developed for use in the virtual screening of chemical databases, focused toward retrieval of novel bioactive chemical scaffolds for PPARγ. Consequent from application, three novel PPAR scaffolds displaying distinct chemotypes have been identified, namely, 5-(4-(benzyloxy)-3-chlorobenzylidene)dihydro-2-thioxopyrimidine-4,6(1H,5H)-dione (MDG 548), 3-((4-bromophenoxy)methyl)-N-(4-nitro-1H-pyrazol-1-yl)benzamide (MDG 559), and ethyl 2-[3-hydroxy-5-(5-methyl-2-furyl)-2-oxo-4-(2-thienylcarbonyl)-2,5-dihydro-1H-pyrrol-1-yl]-4-methyl-1,3-thiazole-5-carboxylate (MDG 582). Fluorescence polarization(FP) and time resolved fluorescence resonance energy transfer (TR-FRET) show that these compounds display high affinity competitive binding to the PPARγ-LBD (EC(50) of 215 nM to 5.45 μM). Consequent characterization by a TR-FRET activation reporter assay demonstrated agonism of PPARγ by all three compounds (EC(50) of 467-594 nM). Additionally, differential PPAR isotype specificity was demonstrated through assay against PPARα and PPARδ subtypes. This work showcases the ability of target specific "tiered screen" protocols to successfully identify novel scaffolds of individual receptor subtypes with greater efficacy than isolated screening methods.

Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery

Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design.

Structural determinants of the alpha2 adrenoceptor subtype selectivity

Alpha2-adrenergic receptor (α2-AR) subtypes, acting mainly on the central nervous and cardiovascular systems, represent important targets for drug design, confirmed by the high number of studies published so far. Presently, only a few α2-AR subtype selective compounds are known. Using homology modeling and ligand docking, the present study analyzes the similarities and differences between binding sites, and between extracellular loops of the three subtypes of α2-ARs. Several α2-AR subtype selective ligands were docked into the active sites of the three α2-AR subtypes, key interactions between ligands and receptors were mapped, and the predicted results were compared with the available experimental data. Binding site analysis reveals a strong identity between important amino acid residues in each receptor, the very few differences being the key toward modulating selectivity of α2-AR ligands. The observed differences between binding site residues provide an excellent starting point for virtual screening of chemical databases, in order to identify potentially selective ligands for α2-ARs.

Identification of Constitutive Androstane Receptor (CAR) Activators by Integrated Computational and Biological Approaches

The nuclear receptor CAR is a well established xenobiotic sensor governing the transcription of numerous hepatic genes associated with xenobiotic metabolism and clearance. Recent evidence suggests that mouse CAR also functions as a mediator of energy metabolism and cancer development. However, whether its human counterpart holds these novel functionsis largely unknown due to the lack of selective human (h) CAR modulators. Here, we have identified several novel hCAR activators by applying 1) virtual screening of chemical databases with ligand‐based structure‐activity models; and 2) cell‐based assays that proved to be efficient in the determination of hCAR activation. Initial screening of the CDD drug database using structure‐activity models and docking has resulted in 20 lead compounds with optimal pharmacophore parameters. In cell‐based reporter assays, two of the 20 leads exhibited robust activation of hCAR. In human primary hepatocyte cultures, these two compounds efficiently translocated hCAR from cytoplasm to nucleus, which has been recognized as the initial step of CAR activation. Moreover, these compounds also induced the expression of the prototypical hCAR target genes, CYP2B6 and CYP3A4. These results clearly indicate that our combined strategy is efficient in the identification of novel hCAR modulators. This work was partly supported by NIH Grant (DK061652).

ChemInform Abstract: Successful Virtual Screening of a Chemical Database for Farnesyltransferase Inhibitor Leads.

Virtual screening of chemical databases is an emerging approach in drug discovery that uses computers to dock chemicals into the active site of a drug target to identify leads through evaluation of binding affinities of the chemicals. However, there are concerns about the validity and scope of the reported virtual screens due to lack of studies to show that randomly selected chemicals are not equally active and due to the fact that metalloproteins were rarely used as drug targets. We have performed a virtual screening of a chemical database to identify prototypic inhibitors of farnesyltransferase (FT) with zinc present in the active site. Among the 21 compounds identified by computers, four inhibited FT in vitro with IC50 values in the range from 25 to 100 μM. The most potent inhibitor also inhibited FT in human lung cancer cells. In contrast, none of 21 randomly selected compounds have an IC50 lower than 100 μM. The results demonstrate the validity of virtual screening and the feasibility of applications...

Abstract 361: Large scale virtual screening to identify novel inhibitors of Fyn as potential drugs for cancer treatment

Abstract Protein kinases are involved in a number of signaling cascades and they are considered as one of the major classes of potential drug targets. Overexpression of protein kinases is often found in many human cancers. Fyn, a member of the Src family of tyrosine kinases, plays a critical role in T-cell receptor signaling, brain function, and cell adhesion-mediated signaling. More importantly, Fyn has oncogenic potential and is involved in carcinogenesis processes, including skin cancer development. These properties make Fyn an ideal molecular target for chemoprevention of skin cancer and other diseases. Molecular docking-based virtual screening has become an important tool in the drug discovery process, and it significantly reduces the number of possible chemical compounds to be investigated. In the present study, we carried out virtual screening of chemical databases to find potential inhibitors of Fyn by targeting the ATP-binding pocket. Because binding of ATP to a protein kinase is essential for the phosphotransferase activity of kinases, the ATP-binding pocket is often considered as the first potentially effective target of most inhibitor screens. Potential Fyn kinase activity inhibitors were identified based on the docking score and selected compounds were subjected to in vitro and cellular assays. The in vitro kinase assay has resulted in the identification of a compound with strong (<50 nM) inhibition of Fyn kinase activity. The same compound was found to suppress UVB-induced Fyn kinase activity in JB6 P+ cells. The inhibitor also suppressed UVB-induced cyclooxygenase-2 expression in JB6 P+ cells, indicating its involvement in skin carcinogenesis. Thus, by using virtual screening, we have identified a potential drug candidate to prevent UVB-induced skin cancer. Studies are in progress to validate our observations in vivo. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 361.