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At least 469 records · Page 26Linked to original sources
PubMed2006

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist.

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental pK (i) values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

Amino Acid Sequence↗
PubMed2006

Successful computer guided planned synthesis of (4R)-thiazolidine carboxylic acid and its 2-substituted analogues as urease inhibitors.

By using internal combinatorial library we were able to identify (4R)-thiazolidines carboxylic acid and its 2-substituted analogs as active inhibitors of urease. Molecular modeling and virtual screening were utilized to find out potential compounds. Computational techniques were employed at database of 90,000 ligands and selected the structure representing the low energy conformations, Grid and FlexX docking algorithms were used and the top binding ligands were synthesized and screened in wet-lab.

Computer-Aided Design↗
PubMed2004

Studies of binding modes of (S)-mephenytoin to wild types and mutants of cytochrome P450 2C19 and 2C9 using homology modeling and computational docking.

PURPOSE: This study investigated the structural features of CYP2C19 complexed with (S)-mephenytoin, using computational methods. In addition to wild-type CYP2C19 proteins (1A and 1B), which have selective 4-hydroxylase activities of (S)-mephenytoin, CYP2C19 mutants were also studied, together with a wild type and artificial mutants of CYP2C19. METHODS: Three-dimensional structures of wild-type and mutant proteins of CYP2C19 and CYP2C9 were estimated from homology modeling using the crystal structure of rabbit CYP2C5 as a reference. The binding mode of (S)-mephenytoin to CYP2C19 was investigated using computational docking. RESULTS: The results reproduced the specific bindings between (S)-mephenytoin and the wild types of CYP2C19. Our findings suggest that Asp293 of CYP2C19 plays an important role in the binding of (S)-mephenytoin, which was surrounded by Vall13 and Ala297, and points the phenyl ring at the heme iron. In addition the wild types of CYP2C19, the computational docking studies also accounted for the experimental activities of CYP2C19 mutants, and wild-type and mutant CYP2C19 proteins. CONCLUSIONS: These results confirm that the predicted three-dimensional structure of the CYP2C19-(S)-mephenytoin complex is reasonable, and that this strategy is useful for investigating complex structures. Virtual screening for drug discovery can also be carried out using these methods.

Amino Acid Sequence↗
PubMed2005

Targeting malaria with specific CDK inhibitors.

Cyclin-dependent protein kinases (CDKs) are attractive targets for drug discovery and efforts have led to the identification of novel CDK selective inhibitors in the development of treatments for cancers, neurological disorders, and infectious diseases. More recently, they have become the focus of rational drug design programs for the development of new antimalarial agents. CDKs are valid targets as they function as essential regulators of cell growth and differentiation. To date, several CDKs have been characterized from the genome of the malaria-causing protozoan Plasmodium falciparum. Our approach employs experimental and virtual screening methodologies to identify and refine chemical inhibitors of the parasite CDK Pfmrk, a sequence homologue of human CDK7. Chemotypes of Pfmrk inhibitors include the purines, quinolinones, oxindoles, and chalcones, which have sub-micromolar IC50 values against the parasite enzyme, but not the human CDKs. Additionally, we have developed and validated a pharmacophore, based on Pfmrk inhibitors, which contains two hydrogen bond acceptor functions and two hydrophobic sites, including one aromatic ring hydrophobic site. This pharmacophore has been exploited to identify additional compounds that demonstrate significant inhibitory activity against Pfmrk. A molecular model of Pfmrk designed using the crystal structure of human CDK7 highlights key amino acid substitutions in the ATP binding pocket. Molecular modeling and docking of the active site pocket with selective inhibitors has identified possible receptor-ligand interactions that may be responsible for inhibitor specificity. Overall, the unique biochemical characteristics associated with this protein, to include distinctive active site amino acid residues and variable inhibitor profiles, distinguishes the Pfmrk drug screen as a paradigm for CDK inhibitor analysis in the parasite.

Adenosine Triphosphate↗
PubMed2005

Accounting for global protein deformability during protein-protein and protein-ligand docking.

Computational docking methods are valuable tools aimed to simplify the costly process of drug development and improvement. Most current approaches assume a rigid receptor structure to allow virtual screening of large numbers of possible ligands and putative binding sites on a receptor molecule. However, inclusion of receptor flexibility can be of critical importance since binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a ligand. Recent approaches to efficiently account for receptor flexibility during docking simulations are reviewed. In particular, accounting efficiently for global conformational changes of the protein backbone during docking is a still challenging unsolved problem. An approximate method has recently been suggested that is based on relaxing the receptor conformation during docking in pre-calculated soft collective degrees of freedom (M. Zacharias, Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP, Proteins: Struct., Funct., Genet. 54 (2004) 759-767). Test applications on protein-protein docking and on docking the inhibitor staurosporine to the apo-form of cAMP-dependent protein kinase A catalytic domain indicate significant improvement of docking results compared to rigid docking at a very modest computational demand. Accounting for receptor conformational changes in pre-calculated global degrees of freedom might offer a promising route to improve systematic docking screening simulations.

Binding Sites↗
PubMed2026

Discovery of NAT-6-321056 as a novel modulator of VEGFR2 signaling to suppress tumor angiogenesis.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a master regulator of angiogenesis and cancer progression. However, current VEGFR2 modulators face significant challenges, including off-target toxicity and acquired resistance, underscoring the urgent need for novel therapeutic agents with improved efficacy and safety profiles. Here, we reported that virtual screening of 39,442 natural products from the ZINC natural products-derived library, coupled with molecular docking and molecular dynamics (MD) simulations to evaluate the binding stability of candidate compounds, identified NAT-6-321056 as a highly promising modulator of VEGFR2 signaling. Biological evaluations demonstrated that NAT-6-321056 exerted potent inhibition on the growth of a broad spectrum of cancer cells, including both solid tumors and hematological malignancies. In EA.hy 926 endothelial cells and SK-N-DZ neuroblast cells, the compound significantly suppressed proliferation, migration, and invasion. Microscale thermophoresis (MST) confirmed direct binding of NAT-6-321056 to VEGFR2 with favorable affinity. Kinase profiling against a panel of 33 kinases indicated that NAT-6-321056 exhibited a multi-kinase modulation profile. Mechanistic studies revealed that NAT-6-321056 suppressed the expression of hypoxia-inducible factor 1-alpha (HIF-1α) and was associated with reduced VEGFR2 phosphorylation and attenuation of the downstream ERK/JNK/AKT signaling pathways. Moreover, NAT-6-321056 exhibited robust in vivo anti-angiogenic effects in both the chick chorioallantoic membrane (CAM) assay and transgenic zebrafish vascular fluorescence imaging models. Computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction suggested acceptable drug-like properties. Collectively, these findings demonstrated that NAT-6-321056 is a promising modulator of VEGFR2 signaling with potent anti-angiogenic activity and represents a viable candidate for cancer therapy.

Vascular Endothelial Growth Factor Receptor-2↗
PubMed2004

A predictive pharmacophore model of human melanocortin-4 receptor as derived from the solution structures of cyclic peptides.

Using nuclear magnetic resonance (NMR) spectroscopy, we have determined the solution structures for a series of potent agonists for the human melanocortin-4 receptor (hMC4R), based on the cyclic peptide MT-II [Ac-Nle-cyclo-(Asp-Lys) (Asp-His-(D)Phe-Arg-Trp-Lys)-NH2]. Members of this series were designed to improve selectivity for MC4R versus the other melanocortin receptors, and to reduce the flexibility of the side chains. The most selective and rigid analog [penta-cyclo(D-K)-Asp-Apc-(D)Phe-Arg-(2S,3S)-beta-methylTrp-Lys-NH2] was found to be a full agonist of hMC4R with an EC50 of 11nM against hMC4R, and to exhibit 65-fold selectivity against hMC1R. This compound represents the most constrained hMC4R peptide agonist described to date. A beta-turn structure was conserved among all of the cyclic peptides studied. The rigidity of the analogs allowed an exceptionally well-defined pharmacophore model to be derived. This model was used to perform a virtual screen using a library of 1000 drug-like compounds, to which a small set of known potent ligands had been intentionally added. The utility of the model was validated by its ability to identify the known ligands from among this large library.

Humans↗
PubMed2005

Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity.

The TOpological MOlecular COMputer Design (TOMOCOMD-CARDD) approach has been introduced for the classification and design of antimicrobial agents using computer-aided molecular design. For this propose, atom, atom-type, and total quadratic indices have been generalized to codify chemical structure information. In this sense, stochastic quadratic indices have been introduced for the description of the molecular structure. These stochastic fingerprints are based on a simple model for the intramolecular movement of all valence-bond electrons. In this work, a complete data set containing 1006 antimicrobial agents is collected and presented. Two structure-based antibacterial activity classification models have been generated. The models (including nonstochastic and stochastic indices) classify correctly more than 90% of 1525 compounds in training sets. These models permit the correct classification of 92.28% and 89.31% of 505 compounds in an external test sets. The TOMOCOMD-CARDD approach, also, satisfactorily compares with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, a virtual screening of 87 new compounds reported in the antiinfective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new leads as antibacterial.

Anti-Bacterial Agents↗
PubMed2006

Classification of dopamine, serotonin, and dual antagonists by decision trees.

Dopamine antagonists (DA), serotonin antagonists (SA), and serotonin-dopamine dual antagonists (Dual) are being used as antipsychotics. A lot of dopamine and serotonin antagonists reveal non-selective binding affinity against these two receptors because the antagonists share structurally common features originated from conserved residues of binding site of the aminergic receptor family. Therefore, classification of dopamine and serotonin antagonists into their own receptors can be useful in the designing of selective antagonist for individual therapy of antipsychotic disorders. Data set containing 1135 dopamine antagonists (D2, D3, and D4), 1251 serotonin antagonists (5-HT1A, 5-HT2A, and 5-HT2C), and 386 serotonin-dopamine dual antagonists was collected from the MDDR database. Cerius2 descriptors were employed to develop a classification model for the 2772 compounds with antipsychotic activity. LDA (linear discriminant analysis), SIMCA (soft independent modeling of class analogy), RP (recursive partitioning), and ANN (artificial neural network) algorithms successfully classified the active class of each compound at the average 73.6% and predicted at the average 69.8%. The decision trees from RP, the best model, were generated to identify and interpret those descriptors that discriminate the active classes more easily. These classification models could be used as a virtual screening tool to predict the active class of new candidates.

Algorithms↗
PubMed2006

In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems.

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Algorithms↗
PubMed2006

QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.

The anti-invasive activity of 139 compounds was correlated by an artificial neural network approach with descriptors calculated solely from the molecular structures using CODESSA Pro. The best multilinear regression method implemented in CODESSA Pro was used for a pre-selection of descriptors. The resulting nonlinear (artificial neural network) QSAR model predicted the exact class for 66 (71%) of the training set of 93 compounds and 32 (70%) of validation set of 46 compounds. The standard deviation ratios for the both training and validation sets are less than unity, indicating a satisfactory predictive capability for classification of the nature of the anti-invasive activity data. The proposed model can be used for the prediction of the anti-invasive activity of novel classes of compounds enabling a virtual screening of large databases of anticancer drugs.

Algorithms↗
PubMed2006

Novel chemo-enzymatic oligomers of cinnamic acids as direct and indirect inhibitors of coagulation proteinases.

Thrombin and factor Xa, two important procoagulant enzymes, have been prime targets for regulation of clotting through the direct and indirect mechanism of inhibition. Our efforts on exploiting the indirect mechanism led us to study a carboxylic acid-based scaffold, which displayed major acceleration in the inhibition of these enzymes [J. Med. Chem.2005, 48, 1269, 5360]. This work advances the study to chemo-enzymatically prepared oligomers of 4-hydroxycinnamic acids, DHPs, which display interesting anticoagulant properties. Oligomers, ranging in size from tetramers to pentadecamers, were prepared through peroxidase-catalyzed oxidative coupling of caffeic, ferulic, and sinapic acids, and sulfated using triethylamine-sulfur trioxide complex. Chromatographic, spectroscopic, and elemental studies suggest that the DHPs are heterogeneous, polydisperse preparations composed of inter-monomer linkages similar to those found in natural lignins. Measurement of activated thromboplastin and prothrombin time indicates that both the sulfated and unsulfated derivatives of the DHPs display anticoagulant activity, which is dramatically higher than that of the reference polyacrylic acids. More interestingly, this activity approaches that of low-molecular-weight heparin with the sulfated derivative showing approximately 2- to 3-fold greater potency than the unsulfated parent. Studies on the inhibition of factor Xa and thrombin indicate that the oligomers exert their anticoagulant effect through both direct and indirect inhibition mechanisms. This dual inhibition property of 4-hydroxycinnamic acid-based DHP oligomers is the first example in inhibitors of coagulation. This work puts forward a novel, non-heparin structure, which may be exploited for the design of potent, dual action inhibitors of coagulation through combinatorial virtual screening on a library of DHP oligomers.

Anticoagulants↗
PubMed2006

Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance.

There are many different kinds of pathogenic bacteria species with very different susceptibility profiles to different antibacterial drugs. One limitation of QSAR models is that they consider the biological activity of drugs against only one species of bacteria. In a previous paper, we developed a unified Markov model to describe the biological activity of different drugs tested in the literature against some antimicrobial species. Consequently, predicting the probability with which a drug is active against different species of bacteria with a single unified model is a goal of major importance. The work described here develops the unified Markov model to describe the biological activity of more than 70 drugs from the literature tested against 96 species of bacteria. We applied linear discriminant analysis (LDA) to classify drugs as active or inactive against the different tested bacterial species. The model correctly classified 199 out of 237 active compounds (83.9%) and 168 out of 200 inactive compounds (84%). Overall training predictability was 84% (367 out of 437 cases). Validation of the model was carried out using an external predicting series, with the model classifying 202 out of 243 (i.e., 83.13%) of the compounds. In order to show how the model functions in practice, a virtual screening was carried out and the model recognized as active 84.5% (480 out of 568) antibacterial compounds not used in the training or predicting series. The current study is an attempt to calculate within a unified framework the probabilities of antibacterial action of drugs against many different species.

Algorithms↗
PubMed2006

Lead discovery and optimization of T-type calcium channel blockers.

A series of compounds were designed as T-type calcium channel blocker containing 6 or 5 pharmacophore features from structure-based virtual screening. To optimize the suggested structure, over 130 derivatives were synthesized and their inhibitory activities on T-type calcium channel were assayed using in vitro screening system with alpha1(G) and alpha1(H) clones. For the compounds with higher activities in FDSS assay system, the efficacy was measured by patch-clamp method. Among the library with 5 features, alkaneamide derivatives (7b, 9j, 11b, 11g, 11h) with 4-arylsubstituted piperazine showed better IC(50) values than Mibefradil.

Alkanes↗
PubMed2004

HIV-1 integrase pharmacophore model derived from diverse classes of inhibitors.

A three-dimensional pharmacophore model has been generated for HIV-1 integrase (HIV-1 IN) from known inhibitors. A dataset consisting of 26 inhibitors was selected on the basis of the information content of the structures and activity data as required by the catalyst/HypoGen program. Our model was able to predict the activity of other known HIV-1 IN inhibitors not included in the model generation, and can be further used to identify structurally diverse compounds with desired biological activity by virtual screening.

HIV Integrase↗
PubMed2004

Protein-dynamics of the putative HCV receptor CD81 large extracellular loop.

The human CD81 protein is a likely receptor for the binding of hepatitis C virus (HCV) to hepatocytes and therefore a possible target for novel anti-HCV drugs. The two published X-ray structures of the HCV binding region of CD81 (1G8Q and 1IV5) have, particularly in a substructure that is formed by two helices, a slightly different conformation. The abovementioned substructure is a candidate target region for virtual screening approaches. We present here a molecular dynamics study of the two X-ray structures. Our results indicate that the conformation of the two helical regions in one of the X-ray structures (1G8Q) is affected by crystallographic contacts and most likely does not represent the native state of the protein.

Antigens, CD↗
PubMed2004

Discovery and optimization of 2-aryl oxazolo-pyrimidines as adenosine kinase inhibitors using liquid phase parallel synthesis.

Adenosine kinase inhibition is an attractive therapeutic approach for several conditions for example, neurodegeneration, seizures, ischemia, inflammation and pain. Several nucleosidic and non-nucleosidic inhibitors are available. Using a virtual screening approach, we have discovered that 2-aryl oxazolo-pyrimidines are adenosine kinase inhibitors. Subsequent high throughput derivatization enabled the optimization of this new inhibitor chemotype resulting in highly potent derivatives. A variety of analogues were produced by applying liquid phase parallel synthesis to vary the 7-amino residues as well as the 2-aryl moiety.

Adenosine Kinase↗
PubMed2004

Pharmacophore-based search, synthesis, and biological evaluation of anthranilic amides as novel blockers of the Kv1.5 channel.

The search for novel, potent Kv1.5 blockers based on an anthranilic amide scaffold employing a pharmacophore-based virtual screening approach is described. The synthesis and structure-activity relationships (SAR) with respect to inhibition of the Kv1.5 channel are discussed. The most potent compounds display sub-micromolar inhibition of Kv1.5 and no significant effect on the HERG channel. In addition, good oral bioavailability is demonstrated for compound 3i in rats.

Administration, Oral↗