Three-dimensional Pharmacophore Model Research Articles

A three-dimensional pharmacophore model for RXR α agonists

Three-dimensional pharmacophore models were generated for RXR α receptor agonists using quantitative approach (CATALYST HypoRefine). One optimal pharmacophore model for selective RXR α agonists was determined through careful validation processes. The best quantitative model (Hypo-1) had four features and eight excluded volumes: three hydrophobic groups and one hydrogen bond receptor. The model was validated using a wide range of test molecules. It could predict agonist activity and identify highly potent molecules. The present results are valuable to discover and develop specific RXR α agonists with desired biological activities.

Treatment of arthritis with a selective inhibitor of c-Fos/activator protein-1

To inhibit arthritis upstream of inflammatory cytokine release and matrix metalloproteinase (MMP) action, we designed de novo a small-molecule inhibitor of c-Fos/activator protein-1 (AP-1) using three-dimensional (3D) pharmacophore modeling. This model was based on the 3D structure of the basic region-leucine zipper domain of AP-1-DNA complex. Administration of this inhibitor prevented type II collagen-induced arthritis from day 21, before the onset of arthritis, or from day 27, resolved arthritis after its onset. Suppression of disease was accomplished by reducing the amounts of inflammatory cytokines and MMPs in vivo in sera and joints and in vitro in synovial cell and chondrocyte cultures. The primary action of this molecule was the inhibition of matrix-degrading MMPs and inflammatory cytokines including interleukin 1beta; this molecule also synergized with anti-tumor necrosis factor alpha to inhibit arthritis. Thus, selective inhibition of c-Fos/AP-1 resolves arthritis in a preclinical model of the disease.

A refined pharmacophore model for HIV-1 integrase inhibitors: Optimization of potency in the 1H-benzylindole series

We report herein the development of a new three-dimensional pharmacophore model for HIV-1 integrase inhibitors which led to the discovery of some 4-[1-(4-fluorobenzyl)-1H-indol-3-yl]-2-hydroxy-4-oxobut-2-enoic acids that are able to specifically inhibit the strand transfer step of integration at nanomolar concentration. The synthesis of the new designed molecules is also described.

6-Amino-1-benzyl-4-(4-chlorophenyl)-3-(4-pyridyl)-1,4-dihydropyrano[2,3-c]pyrazole-5-carbonitrile

The crystal structure of the title compound, C25H18ClN5O, was determined in the course of our studies on the synthesis of 1,4-dihydro­pyrano[2,3-c]pyrazole as an inhibitor of the p38 mitogen-activated protein kinase (MAPK). The compound was prepared via a base-catalysed synthesis from 1-benzyl-3-(4-pyrid­yl)-1H-pyrazol-5(4H)-one with p-chloro­aldehyde and malononitrile. The crystal data obtained were used to generate a three-dimensional pharmacophore model for in silico database screening. The phenyl ring is disordered over two positions, with site occupancy factors of 0.55 and 0.45. The dihedral angles between the 1,4-dihydropyrano[2,3-c]pyrazole unit and the chloro­phenyl and pyridine rings are 83.7 (1) and 16.0 (1)°, respectively. The chloro­phenyl and pyridine rings make a dihedral angle of 86.8 (2)°.

3-(2-Fluorophenyl)-6-(phenoxymethyl)-1,2,4-triazolo[3,4-b][1,3,4]thiadiazole

The crystal structure of the title compound, C16H11FN4OS, was synthesized in the course of our studies on 1,2,4-triazolo[3,4-b][1,3,4]thia­diazo­les as inhibitors of p38 mitogen-activated protein kinase (MAPK). The three-dimensional data obtained were used to generate a three-dimensional pharmacophore model for in silico database screening. The dihedral angles between the central heterocylic system and the fluoro­phenyl and phenyl rings are 20.21 (3) and 5.43 (1)°, respectively; the dihedral angle between the two benzene rings is 15.80 (4)°.

Pharmacophore identification of Raf-1 kinase inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available Raf-1 kinase inhibitors. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic groups), has a correlation coefficient of 0.972. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Pharmacophore Model Generation of HMG-CoA Reductase Inhibitors

A three-dimensional pharmacophore model of 3-hydro-3-methyl glutaryl coenzyme A (HMG-CoA)reductase inhibitors (RI) was developed based on 21 HMG-CoA reductase inhibitors from a rat liver. The selected training set had great diversity in both molecular structure and bioactivity as required by HypoGen program in the Catalyst software. The inhibitors in training set showed HMG-CoA RI inhibiting activity with IC50 values of 0.3-8000 nmol·L-1. The best statistical hypothesis, consisting of four features, one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic feature, and one hydrophobic point, had a correlation coefficient of 0.8883, a root-mean-square (RMS) deviation of 1.269, a Fixed cost of 88.75, a Total cost of 111.5, and a Configuration cost of 16.98, this hypothesis had highly predictive ability. The predictive ability was approved by the results of the activity estimated by mapping the compounds of the testing set with it. The cross-validation provided strong confidence on this hypothesis. This pharmacophore model can contribute to the finding and designing of new-type HMG-CoA reductase inhibitors, and to the development of traditional Chinese medicine and materia medica.

The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

AbstractA three‐dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti‐cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis (Hypo l) was defined by four features: one hydrogen‐bond donor and three hydrophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross‐validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.

A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors

Dipeptidyl peptidase IV (DPP-IV) is a valid drug target for type-2 diabetes and DPP-IV inhibitors have been proven to efficiently improve glucose tolerance. In our study, 3D pharmacophore models were generated using a training set of 22 DPP-IV inhibitors. The best model consisted of important chemical features and mapped well into the active site of DPP-IV. The model gave high correlation coefficients of 0.97 and 0.84 for the training set and the test set, respectively, showing its good predictive ability for biological activity. Furthermore, the pharmacophore model demonstrated the capability to retrieve inhibitors from database with a high enrichment factor of 42.58. All results suggest that the model provides a useful tool for designing novel DPP-IV inhibitors.

Pharmacophore Model Generation Based on Pyrrolidine- and Butane-derived CCR5 Antagonists

A three-dimensional pharmacophore model was developed for a considerable number of pyrrolidine-based and butane-based chemokine (C-C motif) receptor 5 (CCR5) antagonists, which can block the entry of human immunodeficiency virus type 1 (HIV-1) by inhibiting the interaction of HIV-1 envelope protein and CCR5. The pharmacophore model was generated using a training set consisting of 25 carefully selected antagonists with the diverse molecular architecture and bioactivity, as required by the Catalyst/HypoGen program. The activity of the training set molecules expressed in IC 50 (half-inhibitory concentration) covered from 0.06 to 10000 nmol·L –1. The most predictive pharmacophore model (Hypo 1), consisting of two positive ionizable points and three hydrophobic groups, had a correlation of 0.924 and a root mean square of 1.068, and a cost difference of 63.67 bits between the null cost and the total cost. The model was applied in predicting the activity of 74 compounds as a test set. The results indicated that the model was able to provide clear guidelines and accurate activity prediction for novel antagonist design.

3D‐Pharmacophore Models for Selective A2A and A2B Adenosine Receptor Antagonists.

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3D-Pharmacophore Models for Selective A2Aand A2BAdenosine Receptor Antagonists

Three-dimensional pharmacophore models were generated for A2A and A2B adenosine receptors (ARs) based on highly selective A2A and A2B antagonists using the Catalyst program. The best pharmacophore model for selective A2A antagonists (Hypo-A2A) was obtained through a careful validation process. Four features contained in Hypo-A2A (one ring aromatic feature (R), one positively ionizable feature (P), one hydrogen bond acceptor lipid feature (L), and one hydrophobic feature (H)) seem to be essential for antagonists in terms of binding activity and A2A AR selectivity. The best pharmacophore model for selective A2B antagonists (Hypo-A2B) was elaborated by modifying the Catalyst common features (HipHop) hypotheses generated from the selective A2B antagonists training set. Hypo-A2B also consists of four features: one ring aromatic feature (R), one hydrophobic aliphatic feature (Z), and two hydrogen bond acceptor lipid features (L). All features play an important role in A2B AR binding affinity and are essential for A2B selectivity. Both A2A and A2B pharmacophore models have been validated toward a wide set of test molecules containing structurally diverse selective antagonists of all AR subtypes. They are capable of identifying correspondingly high potent antagonists and differentiating antagonists between subtypes. The results of our study will act as a valuable tool for retrieving structurally diverse compounds with desired biological activities and designing novel selective adenosine receptor ligands.

Flavonoids with Aldose Reductase Inhibiting Activity: PharmacophoreModeling and Implications forMechanism

A three-dimensional pharmacophore model was generated for aldose reductase (ALR2) inhibitors with flavonoid skeleton, using the software Catalyst. Specially customized features of hydrogen-bond acceptor and donor were used in this study, which perform better than the default features of Catalyst. The hypothesis model was scored based on the cost of the hypothesis from the null hypothesis. The final selected pharmacophore model had three features; one hydrogen bond acceptor and two donors. Six flavonoid compounds were used to dock into ALR2 active site, using InsightII/Affinity. Comparison between the pharmacophore model and the docking results suggested that the C7 and C4' hydroxyls on the flavone skeleton were key functional groups influencing ALR2 inhibotory activity, and TYR48, VAL47, GLN49, HIS 110, and TRP111 at the active site of ALR2 were the key residues for the binding.

Pharmacophore identification of KSP inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available KSP (kinesin spindle protein) inhibitors in Catalyst software package. The best pharmacophore hypothesis (Hypo1), consisting of four chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group), has a correlation coefficient of 0.965. The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Farnesyltransferase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 25 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity as required by HypoGen program in the Catalyst software, for discovering new farnesyltransferase (FTase) inhibitors. The best hypothesis (Hypo1), consisting of four features, namely, two hydrogen-bond acceptors, one hydrophobic point, and one ring aromatic feature, has a correlation coefficient of 0.949, a root-mean-square deviation of 1.321, and a cost difference of 163.15, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 227 known FTase inhibitors in our test set with a correlation coefficient of 0.776 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.

Urokinase Inhibitor Design Based on Pharmacophore Model Derived from Diverse Classes of Inhibitors

A three-dimensional pharmacophore model was developed based on 24 currently available inhibitors, which were rationally selected from 472 compounds with diverse molecular structure and bioactivity, for generating pharmacophore of uPA (Urokinase Plasminogen Activator) inhibitors. The best hypothesis (Hypo1) comprised of five features, namely, one positive ionizable group, one hydrogen-bond acceptor group and three hydrophobic aromatic groups. The correlation coefficient, root mean square deviation and cost difference were 0.973, 0.695, and 94.291 respectively, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model showed great success in predicting the activities of 251 known uPA inhibitors (test set) with a correlation coefficient of 0.837, and there was also none of the outcome hypotheses that had similar cost difference and RMS deviation (RMSD) with that of the initial hypothesis generated by Cat-Scramble validation test with 95% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity. Corresponding Author: Keun Woo Lee (Email: kwlee@ gnu.ac.kr). This work was supported by grants from the MOST/KOSEF to the Environmental Biotechnology National Core Research Center (R15-2003-012-02001-0) and the Basic Research Program (R01-2005-000-10373-0), Korea.

Neuraminidase pharmacophore model derived from diverse classes of inhibitors

A three-dimensional pharmacophore model was developed based on 22 currently available inhibitors, which were carefully selected with great diversity in both molecular structure and bioactivity, for discovering new potent neuraminidase (NA) inhibitors to fight against avian influenza virus. The best hypothesis (Hypo1), consisting of five features, namely, one positive ionizable group, one negative ionizable group, one hydrophobic point, and two hydrogen-bond donors, has a correlation coefficient of 0.902, a root mean square deviation of 1.392, and a cost difference of 72.88, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 88 known NA inhibitors in our test set with a correlation coefficient of 0.818 with a cross-validation of 98% confidence level. Accordingly, our model should be reliable in identifying structurally diverse compounds with desired biological activity.

Discovery of Nonpeptidic Small-Molecule AP-1 Inhibitors: Lead Hopping Based on a Three-Dimensional Pharmacophore Model

We designed and synthesized small-molecule activator protein-1 (AP-1) inhibitors based on a three-dimensional (3D) pharmacophore model that we had previously derived from a cyclic decapeptide exhibiting AP-1 inhibitory activity. New AP-1 inhibitors with a 1-thia-4-azaspiro[4.5]decane or a benzophenone scaffold, which inhibit the DNA-binding and transactivation activities of AP-1, were discovered using a "lead hopping" procedure. An additional investigation of the benzophenone analogues confirmed the reliability of the pharmacophore model, its utility to discover AP-1 inhibitors, and the potency of the benzophenone derivatives as a lead series.

Human Rhinovirus 3C Protease: Generation of Pharmacophore Models for Peptidic and Nonpeptidic Inhibitors and Their Application in Virtual Screening

Three-dimensional pharmacophore models for peptidic and small organic nonpeptidic inhibitors of the human rhinovirus 3C protease were generated in a structure-based as well as in a ligand-based approach, using the software package Catalyst. The inhibitors possess an electrophilic moiety, often a Michael acceptor function, which covalently binds to a cysteine in the active site of the enzyme. Since this process presents the key step for virus inactivation, the creation of a new function in Catalyst was required in order to include this decisive functionality into the pharmacophore models. In the present study we focus on this feature definition process because it presents an innovative strategy to expand the pharmacophore description ability of the Catalyst software to also include covalent bonds between ligand and binding site. The resulting hypotheses were then used for virtual screening of 3D databases in order to verify their quality and to search for structurally diverse, possible new lead substances.

Structure-based Drug Discovery Using GPCR Homology Modeling: Successful Virtual Screening for Antagonists of the Alpha1A Adrenergic Receptor

In this paper, we describe homology modeling of the alpha1A receptor based on the X-ray structure of bovine rhodopsin. The protein model has been generated by applying ligand-supported homology modeling, using mutational and ligand SAR data to guide the protein modeling procedure. We performed a virtual screening of the company's compound collection to test how well this model is suited to identify alpha1A antagonists. We applied a hierarchical virtual screening procedure guided by 2D filters and three-dimensional pharmacophore models. The ca. 23,000 filtered compounds were docked into the alpha1A homology model with GOLD and scored with PMF. From the top-ranked compounds, 80 diverse compounds were tested in a radioligand displacement assay. 37 compounds revealed K(i) values better than 10 microM; the most active compound binds with 1.4 nM to the alpha1A receptor. Our findings suggest that rhodopsin-based homology models may be used as the structural basis for GPCR lead finding and compound optimization.