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At least 199 records · Page 11Linked to original sources
PubMed2006

The discovery of new 11beta-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening.

11beta-Hydroxysteroid dehydrogenase (11beta-HSD) enzymes catalyze the conversion of biologically inactive 11-ketosteroids into their active 11beta-hydroxy derivatives and vice versa. Inhibition of 11beta-HSD1 has considerable therapeutic potential for glucocorticoid-associated diseases including obesity, diabetes, wound healing, and muscle atrophy. Because inhibition of related enzymes such as 11beta-HSD2 and 17beta-HSDs causes sodium retention and hypertension or interferes with sex steroid hormone metabolism, respectively, highly selective 11beta-HSD1 inhibitors are required for successful therapy. Here, we employed the software package Catalyst to develop ligand-based multifeature pharmacophore models for 11beta-HSD1 inhibitors. Virtual screening experiments and subsequent in vitro evaluation of promising hits revealed several selective inhibitors. Efficient inhibition of recombinant human 11beta-HSD1 in intact transfected cells as well as endogenous enzyme in mouse 3T3-L1 adipocytes and C2C12 myotubes was demonstrated for compound 27, which was able to block subsequent cortisol-dependent activation of glucocorticoid receptors with only minor direct effects on the receptor itself. Our results suggest that inhibitor-based pharmacophore models for 11beta-HSD1 in combination with suitable cell-based activity assays, including such for related enzymes, can be used for the identification of selective and potent inhibitors.

11-beta-Hydroxysteroid Dehydrogenase Type 1↗
PubMed2006

Estrogen receptors: molecular interactions, virtual screening and future prospects.

Identification of the Estrogen Receptor (ER) as a key mediator of the proliferation of breast cancer, and its involvement in pathways leading to osteoporosis and coronary heart disease, has resulted in a surge to discover and design compounds with the ability to modulate its actions (SERMs). Concurrently, a dramatic increase in the number of crystal structures of the ER has led to a more in depth understanding of the governing mechanisms involved in ER modulation. Entwining computational techniques with the availability of 3D structural data, has allowed not only the rational design of potent inhibitors of the ER, but also its incorporation in Virtual Screening (VS) in the search for novel chemotypes that can modulate the ER. An important initial step in the VS process is to filter towards molecules that occupy similar chemical space to a set of known actives prior to docking. We illustrate through Principal Component Analysis (PCA) of 145 descriptors the region of chemical space antiestrogens occupy compared with 'drug-like' space. We also review all available studies involving validation of several docking algorithms utilizing the ER, ultimately focusing on analysis of Enrichment (E) rates and False Positive (FP) rates to illustrate the successes attributed to each docking algorithm. Finally, we relate the recent discovery of non-genomic mechanisms of the ER and subsequently present a model involving a recently identified alternative, second binding-pocket of the ER in our laboratory through cavity analysis that suggests how the same receptor can invoke these, 'classical' and rapid responses concurrently.

Algorithms↗
PubMed2006

MolDIA: XML based system of molecular diversity analysis towards virtual screening and QSPR.

In this paper a new chemoinformatics tool for Molecular Diversity Analysis (MolDIA) is introduced. The objective of this system is the analysis of molecular similarity and diversity through the treatment of structural and physicochemical information. Current needs for chemical databases include the analysis, the management and the retrieval of chemical information. The implementation of eXtended Markup Languages (XML) is proposed as a basis for representing and structuring the chemical information contained in data structures and databases. The adequate descriptor vector and related physicochemical properties have been defined and constructed. The benefits of XML in chemoinformatics are discussed, as well as, the applications of this system in a virtual screening environment.

Computational Biology↗
PubMed2004

Crystallographic study of inhibitors of tRNA-guanine transglycosylase suggests a new structure-based pharmacophore for virtual screening.

The enzyme tRNA-guanine transglycosylase (TGT) is involved in the pathogenicity of Shigellae. As the crystal structure of this protein is known, it is a putative target for the structure-based design of inhibitors. Here we report a crystallographic study of several new ligands exhibiting a 2,6-diamino-3H-quinazolin-4-one scaffold, which has been shown recently to be a promising template for TGT-inhibitors. Crystal structure analysis of these complexes has revealed an unexpected movement of the side-chain of Asp102. A detailed analysis of the water network disrupted by this rotation has lead to the derivation of a new composite pharmacophore. A virtual screening has been performed based on this pharmacophore hypothesis and several new inhibitors of micromolar binding affinity with new skeletons have been discovered.

Aspartic Acid↗
PubMed2000

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.

Journal Article↗
PubMed2004

A virtual screening approach to finding novel and potent antagonists at the melanin-concentrating hormone 1 receptor.

Melanin-concentrating hormone (MCH) has been known to be an appetite-stimulating peptide for a number of years. However, it is only recently that MCH has been discovered to be the natural ligand for a previously "orphan" G-protein-coupled receptor, now designated MCH-1R. This receptor has been shown to mediate the effects of MCH on appetite and body weight, and consequently, drug discovery programs have begun to exploit this information in the search for MCH-1R antagonists for the treatment of obesity. In this paper, we report the rapid discovery of multiple, structurally distinct series of MCH-1R antagonists using a variety of virtual screening techniques. The most potent of these compounds (12) demonstrated an IC(50) value of 55 nM in the primary screen and exhibited antagonist properties in a functional cellular assay measuring Ca(2+) release. More potent compounds were identified by follow-up searches around the initial hit. A proposed binding mode for compound 12 in a homology model of the MCH-1R is also presented.

Amino Acid Sequence↗
PubMed2002

A virtual screening method for prediction of the HERG potassium channel liability of compound libraries.

A computer-based method has been developed for prediction of the hERG (human ether-à-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps (SOM), principal component analysis (PCA), partial least squares fitting (PLS), and supervised neural networks. The most accurate prediction system was based on an artificial neural network. In a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design.

Cation Transport Proteins↗
PubMed2006

The potential of P1 site alterations in peptidomimetic protease inhibitors as suggested by virtual screening and explored by the use of C-C-coupling reagents.

A synthetic concept is presented that allows the construction of peptide isostere libraries through polymer-supported C-acylation reactions. A phosphorane linker reagent is used as a carbanion equivalent; by employing MSNT as a coupling reagent, the C-acylation can be conducted without racemization. Diastereoselective reduction was effected with L-selectride. The reagent linker allows the preparation of a norstatine library with full variation of the isosteric positions including the P1 side chain that addresses the protease S1 pocket. Therefore, the concept was employed to investigate the P1 site specificity of peptide isostere inhibitors systematically. The S1 pocket of several aspartic proteases including plasmepsin II and cathepsin D was modeled and docked with approximately 500 amino acid side chains. Inspired by this virtual screen, a P1 site mutation library was designed, synthesized, and screened against three aspartic proteases (plasmepsin II, HIV protease, and cathepsin D). The potency of norstatine inhibitors was found to depend strongly on the P1 substituent. Large, hydrophobic residues such as biphenyl, 4-bromophenyl, and 4-nitrophenyl enhanced the inhibitory activity (IC50) by up to 70-fold against plasmepsin II. In addition, P1 variation introduced significant selectivity, as up to 9-fold greater activity was found against plasmepsin II relative to human cathepsin D. The active P1 site residues did not fit into the crystal structure; however, molecular dynamics simulation suggested a possible alternative binding mode.

Aminocaproates↗
PubMedDate not supplied

New scoring functions for virtual screening from molecular dynamics simulations with a quantum-refined force-field (QRFF-MD). Application to cyclin-dependent kinase 2.

A recently introduced new methodology based on ultrashort (50-100 ps) molecular dynamics simulations with a quantum-refined force-field (QRFF-MD) is here evaluated in its ability both to predict protein-ligand binding affinities and to discriminate active compounds from inactive ones. Physically based scoring functions are derived from this approach, and their performance is compared to that of several standard knowledge-based scoring functions. About 40 inhibitors of cyclin-dependent kinase 2 (CDK2) representing a broad chemical diversity were considered. The QRFF-MD method achieves a correlation coefficient, R(2), of 0.55, which is significantly better than that obtained by a number of traditional approaches in virtual screening but only slightly better than that obtained by consensus scoring (R(2) = 0.50). Compounds from the Available Chemical Directory, along with the known active compounds, were docked into the ATP binding site of CDK2 using the program Glide, and the 650 ligands from the top scored poses were considered for a QRFF-MD analysis. Combined with structural information extracted from the simulations, the QRFF-MD methodology results in similar enrichment of known actives compared to consensus scoring. Moreover, a new scoring function is introduced that combines a QRFF-MD based scoring function with consensus scoring, which results in substantial improvement on the enrichment profile.

Computer Simulation↗
PubMedDate not supplied

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.

Binding Sites↗
PubMed2004

Multiple active site corrections for docking and virtual screening.

Several docking programs are now available that can reproduce the bound conformation of a ligand in an active site, for a wide variety of experimentally determined complexes. However, these programs generally perform less well at ranking multiple possible ligands in one site. Since accurate identification of potential ligands is a prerequisite for many aspects of structure-based drug design, this is a serious limitation. We have tested the ability of two docking programs, FlexX and Gold, to match ligands and active sites for multiple complexes. We show that none of the docking scores from either program are able to match consistently ligands and active sites in our tests. We propose a simple statistical correction, the multiple active site correction (MASC), which greatly ameliorates this problem. We have also tested the correction method against an extended set of 63 cocrystals and in a virtual screening experiment. In all cases, MASC significantly improves the results of the docking experiments.

Binding Sites↗
PubMed2002

Virtual screening for binding of phenylalanine analogues to phenylalanyl-tRNA synthetase.

Although incorporation of nonnatural amino acids provides a powerful means of controlling protein structure and function, experimental investigations of amino acid analogues for utilization by the protein biosynthetic machinery can be costly and time-consuming. In this paper, we describe a computational protocol (HierDock) for predicting the relative energies of binding of phenylalanine analogues to phenylalanyl-tRNA synthetase (PheRS). Starting with the crystal structure of Thermus thermophilus PheRS without bound ligand, HierDock predicts the binding site of phenylalanine (Phe) within 1.1 A of that revealed by the crystal structure of PheRS cocrystallized with Phe. The calculated binding energies of Phe analogues in PheRS, using HierDock, correlate well with the translational activities of the same analogues in Escherichia coli. HierDock identifies p-fluorophenylalanine and 3-thienylalanine as especially good substrates for PheRS, in agreement with experiment. These results suggest that the HierDock protocol may be useful for virtual screening of amino acid analogues prior to experiment.

Amino Acid Sequence↗
PubMedDate not supplied

Enhanced virtual screening by combined use of two docking methods: getting the most on a limited budget.

Flexible ligand docking is a routine part of a modern structure-based lead discovery process. As of today, there are quite a number of commercial docking programs that can be used to screen large databases (hundreds of thousands to millions of compounds). However, limiting factors such as the number of commercial software licenses needed to perform docking simultaneously on multiple processors ("software cost") and the relatively long time required per molecule to get good results ("quality-to-speed") should be taken into account when planning a large docking run. How can we optimize the efficiency of selecting lead candidates by docking, in respect to the quality of the results, search speed, and software cost? We present a combination of two methods, our "fast-free-approximate" in-house docking program and the "slow-costly-accurate" ICM-Dock, as an example of one solution to the problem. Our proposed protocol is illustrated by a series of virtual screening experiments aimed at identifying active compounds in the MDL Drug Data Report database. In more than half of the 20 cases examined, at least several actives per protein target were identified in approximately 24 hours per target.

Algorithms↗
PubMedDate not supplied

2D QSAR consensus prediction for high-throughput virtual screening. An application to COX-2 inhibition modeling and screening of the NCI database.

Using classification (SOM, LVQ, Binary, Decision Tree) and regression algorithms (PLS, BRANN, k-NN, Linear), this paper details the building of eight 2D-QSAR models from a 266 COX-2 inhibitor training set. The predictive performances of these eight models were subsequently compared using an 88 COX-2 inhibitor test set. Each ligand is described by 52 2D descriptors expressed as van der Waals Surface Areas (P_VSA) and its COX-2 binding IC50. One of our best predictive models is the neural network model (BRANN), which is able to select a subset, from the 88 ligand test set, that contains 94% COX-2 active inhibitors (pIC50>7.5) and detects 71% of all the actives. We then introduce a QSAR consensus prediction protocol that is shown to be more predictive than any single QSAR model: our C3 consensus approach is able to select a subset from the 88 ligand test set that contains 94% active inhibitors and 83% of all the actives. The 2D QSAR consensus protocol was finally applied to the high-throughput virtual screening of the NCI database, containing 193,477 organic compounds.

Cyclooxygenase 2↗
PubMedDate not supplied

Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR.

This study provides results from two case studies involving the application of the HypoGenRefine algorithm within Catalyst for the automated generation of excluded volume from ligand information alone. A limitation of pharmacophore feature hypothesis alone is that activity prediction is based purely on the presence and arrangement of pharmacophoric features; steric effects remained unaccounted. Recently reported studies have illustrated the usefulness of combining excluded volumes to the pharmacophore models. In general, these excluded volumes attempt to penalize molecules occupying steric regions that are not occupied by active molecules. The HypoGenRefine algorithm in Catalyst accounts for steric effects on activity, based on the targeted addition of excluded volume features to the pharmacophores. The automated inclusion of excluded volumes to pharmacophore models has been applied to two systems: CDK2 and human DHFR. These studies are used as examples to illustrate how ligands could bind in the protein active site with respect to allowed and disallowed binding regions. Additionally, automated refinement of the pharmacophore with these excluded volume features provides a more selective model to reduce false positives and a better enrichment rate in virtual screening.

Algorithms↗
PubMed2006

TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.

A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six using the non-stochastic total and local bond-based linear indices as well as the last six ones, the stochastic molecular descriptors. The best two discriminant models computed using the non-stochastic and stochastic molecular descriptors (Eqs. , respectively) had globally good classifications of 98.95% and 89.75% in the training set, with high Matthews correlation coefficients (C) of 0.98 and 0.78. The external prediction sets had accuracies of 98.89% and 89.44%, and (C) values of 0.98 and 0.78, for models 7 and 13, respectively. A virtual screening of compounds reported in the literature with such activity was carried out, to prove the ability of present models to search for tyrosinase inhibitors, not included in the training or test set. At the end, the fitted discriminant functions were used in the selection/identification of new ethylsteroids isolated from herbal plants, looking for tyrosinase inhibitory activity. A good behavior is shown between the theoretical and experimental results on mushroom tyrosinase enzyme. It might be highlighted that all the compounds showed values under 10microM and that ES2 (IC(50)=1.25microM) showed higher activity in the inhibition against the enzyme than reference compounds kojic acid (IC(50)=16.67microM) and l-mimosine (IC(50)=3.68microM). In addition, a comparison with other established methods was carried to prove the adequate discriminatory performance of the molecular descriptors used here. The present algorithm provided useful clues that can be used to speed up in the identification of new tyrosinase inhibitor compounds.

Agaricales↗
PubMed2006

Identification of chemically diverse Chk1 inhibitors by receptor-based virtual screening.

Inhibition of the Chk1 kinase by small molecules is of great therapeutic interest for oncology and in understanding the cellular regulation of the G2/M checkpoint. We report how computational docking of a large electronic catalogue of compounds to an X-ray structure of the Chk1 ATP-binding site allowed prioritisation of a small subset of these compounds for assay. This led to the discovery of 10 novel Chk1 inhibitors, distributed among nine new and clearly different chemical scaffolds. Several of these scaffolds have promising lead-like properties. All these ligands act by competitive binding to the targeted ATP site. The crystal structures of four of these compounds bound to this site are presented, and reasonable modelled docking modes are suggested for the 5 other scaffolds. This structural context is used to assess the potential of these scaffolds for further medicinal chemistry efforts, suggesting that several of them could be elaborated to make additional interactions with the buried part of the ATP site. Some unusual interactions with the conserved kinase backbone motif are pointed out. The ligand-binding modes are also used to discuss their medicinal chemistry potential with respect to undesirable chemical functionalities, whether these functionalities bind directly to the protein or not. Overall, this work illustrates how virtual screening can identify a diverse set of ligands which bind to the targeted site. The structural models for these ligands in the Chk1 ATP-binding site will facilitate further medicinal chemistry efforts targeting this kinase.

Adenosine Triphosphate↗
PubMedDate not supplied

Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients.

This paper evaluates the effectiveness of various similarity coefficients for 2D similarity searching when multiple bioactive target structures are available. Similarity searches using several different activity classes within the MDL Drug Data Report and the Dictionary of Natural Products databases are performed using BCI 2D fingerprints. Using data fusion techniques to combine the resulting nearest neighbor lists we obtain group recall results which, in many cases, are a considerable improvement on standard average recall values obtained for individual structures. It is shown that the degree of improvement can be related to the structural diversity of the activity class that is searched for, the best results being found for the most diverse groups. The group recall of active compounds using subsets of the class is also investigated: for highly self-similar activity classes, the group recall improvement saturates well before the full activity class size is reached. A rough correlation is found between the relative improvement using the group recall and the square of the number of unique compounds available in all of the merged lists. The Tanimoto coefficient is found unambiguously to be the best coefficient to use for the recovery of active compounds using multiple targets. Furthermore, when using the Tanimoto coefficient, the "MAX" fusion rule is found to be more effective than the "SUM" rule for the combination of similarity searches from multiple targets. The use of group recall can lead to improved enrichment in database searches and virtual screening.

Pharmaceutical Preparations↗