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PubMedDate not supplied

Physics-based scoring of protein-ligand complexes: enrichment of known inhibitors in large-scale virtual screening.

We demonstrate that using an all-atom molecular mechanics force field combined with an implicit solvent model for scoring protein-ligand complexes is a promising approach for improving inhibitor enrichment in the virtual screening of large compound databases. The rescoring method is evaluated by the extent to which known binders for nine diverse, therapeutically relevant enzymes are enriched against a background of approximately 100,000 drug-like decoys. The improvement in enrichment is most robust and dramatic within the top 1% of the ranked database, that is, the first thousand compounds; below the first few percent of the ranked database, there is little overall improvement. The improved early enrichment is likely due to the more realistic treatment of ligand and receptor desolvation in the rescoring procedure. We also present anecdotal but encouraging results assessing the ability of the rescoring method to predict specificity of inhibitors for structurally related proteins.

Biophysical Phenomena↗
PubMed2000

Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening.

Many contemporary applications in computer-aided drug discovery and chemoinformatics depend on representations of molecules by descriptors that capture their structural characteristics and properties. Such applications include, among others, diversity analysis, library design, and virtual screening. Hundreds of molecular descriptors have been reported in the literature, ranging from simple bulk properties to elaborate three-dimensional formulations and complex molecular fingerprints, which sometimes consist of thousands of bit positions. Knowledge-based selection of descriptors that are suitable for specific applications is an important task in chemoinformatics research. If descriptors are to be selected on rational grounds, rather than guesses or chemical intuition, detailed evaluation of their performance is required. A number of studies have been reported that investigate the performance of molecular descriptors in specific applications and/or introduce novel types of descriptors. Progress made in this area is reviewed here in the context of other computational developments in combinatorial chemistry and compound screening.

Algorithms↗
PubMed2005

SARS-CoV protease inhibitors design using virtual screening method from natural products libraries.

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.

Algorithms↗
PubMed1997

A virtual screening approach applied to the search for trypanothione reductase inhibitors.

A prediction algorithm of the binding affinity of ligands to trypanothione reductase (TR), the enzyme replacing glutathione reductase in the metabolism of trypanosomatidae, has been used for the "virtual screening" of a data base of 2500 molecular sketches and has detected several structures of putative TR ligands. Most of these compounds turned out to be micromolar inhibitors of TR, as predicted by the algorithm. While their inhibitory potencies are lower than those of previously reported compounds, one of the molecules reported here could represent the lead toward a structurally different class of TR inhibitors. The fully automated prediction algorithm converts the 2D molecular sketches into 3D ligand structures, explores the conformational space of the latter, and performs a grid-based, rigid-body docking of the resulting family of ligand conformations into the TR site, calculating enthalpic and entropic binding indexes and predicting the binding affinity. The docking model has also been used to obtain hints about the binding modes of TR ligands.

Algorithms↗
PubMed2000

Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations.

Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to discriminate about 7 out of 10 true hits from a random database of 990 ligands. The use of consensus lists common to two or three scoring functions clearly enhances hit rates among the top 5% scorers from 10% (single scoring) to 25-40% (double scoring) and up to 65-70% (triple scoring). However, in all tested cases, no clear relationships could be found between docking and ranking accuracies. Moreover, predicting the absolute binding free energy of true hits was not possible whatever docking accuracy was achieved and scoring function used. As the best docking/consensus scoring combination varies with the selected target and the physicochemistry of target-ligand interactions, we propose a two-step protocol for screening large databases: (i) screening of a reduced dataset containing a few known ligands for deriving the optimal docking/consensus scoring scheme, (ii) applying the latter parameters to the screening of the entire database.

Algorithms↗
PubMed2002

High-throughput virtual screening for drug discovery in parallel.

With the influx of targets generated by genomics and proteomics initiatives, a new drug discovery paradigm is emerging. Many companies are setting up target family platforms that tackle multiple targets and therapeutic areas simultaneously. Virtual screening (VS) techniques are a fundamental component of such platforms for in silico filtering of compound collections and prioritization of chemistry and screening efforts. At the heart of these, structure-based docking and scoring methods are especially effective in identifying bioactive molecules if the structure of a target is available. As structural genomics maps the structural space of the proteome, these techniques are expected to become commonplace. In light of this, an overview of the latest developments in VS methodology is given here. In particular, emphasis is placed on those techniques adaptable to high-throughput VS in parallel drug discovery platforms. The first examples of docking across multiple targets have already appeared in the literature and will be reviewed here.

Animals↗
PubMed2006

Structure-based virtual screening of chemical libraries for drug discovery.

One of the main goals in drug discovery is to identify new chemical entities that have a high likelihood of binding to the target protein to elicit the desired biological response. To this end, virtual screening is being increasingly used as a complement to high-throughput screening to improve the speed and efficiency of the drug discovery and development process. The availability of inexpensive high-performance computing platforms in recent years has transformed this field into one that is highly diverse and rapidly evolving, where large chemical databases have been successfully screened to identify hits for a wide range of targets such as Bcl-2 family proteins, G protein-coupled receptors, kinases, metalloproteins, nuclear hormone receptors, proteases and many more.

Acetylcholinesterase↗
PubMed2006

Structure-based drug design of a novel family of PPARgamma partial agonists: virtual screening, X-ray crystallography, and in vitro/in vivo biological activities.

Peroxisome proliferator-activated receptor gamma (PPARgamma) is well-known as the receptor of thiazolidinedione antidiabetic drugs. In this paper, we present a successful example of employing structure-based virtual screening, a method that combines shape-based database search with a docking study and analogue search, to discover a novel family of PPARgamma agonists based upon pyrazol-5-ylbenzenesulfonamide. Two analogues in the family show high affinity for, and specificity to, PPARgamma and act as partial agonists. They also demonstrate glucose-lowering efficacy in vivo. A structural biology study reveals that they both adopt a distinct binding mode and have no H-bonding interactions with PPARgamma. The absence of H-bonding interaction with the protein provides an explanation why both function as partial agonists since most full agonists form conserved H-bonds with the activation function helix (AF-2 helix) which, in turn, enhances the recruitment of coactivators. Moreover, the structural biology and computer docking studies reveal the specificity of the compounds for PPARgamma could be due to the restricted access to the binding pocket of other PPAR subtypes, i.e., PPARalpha and PPARdelta, and steric hindrance upon the ligand binding.

Adipocytes↗
PubMedDate not supplied

Virtual screening of molecular databases using a support vector machine.

The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.

Algorithms↗
PubMed2004

Identification of novel parasitic cysteine protease inhibitors using virtual screening. 1. The ChemBridge database.

Trypanosomiasis, leishmaniasis, and malaria are major parasitic diseases in developing countries. The existing chemotherapy of these diseases suffers from lack of safe and effective drugs and/or the presence of widespread drug resistance. Cysteine proteases are exciting novel targets for antiparasitic drug design. Virtual screening was performed in an attempt to identify novel druglike nonpeptide inhibitors of parasitic cysteine proteases. The ChemBridge database consisting of approximately 241 000 compounds was screened against homology models of falcipain-2 and falcipain-3 in three consecutive stages of docking. A total of 24 diverse inhibitors were identified from an initial group of 84, of which 12 compounds appeared to be dual inhibitors of falcipain-2 and falcipain-3. Four compounds showed inhibition of both the malarial cysteine proteases as well as Leishmania donovani cysteine protease.

Animals↗
PubMed2004

Virtual screening for inhibitors of human aldose reductase.

The inhibition of aldose reductase (AR) provides an interesting strategy to prevent the complications of chronic diabetes. Although a large number of different AR inhibitors are known, very few of these compounds exhibit sufficient efficacy in clinical trials. We performed a virtual screening based on the ultrahigh resolution crystal structure of the inhibitor IDD594 in complex with human AR. AR operates on a large scale of structurally different substrates. To achieve this pronounced promiscuity, the enzyme can adapt rather flexibly to its substrates. Likewise, it has a similar adaptability for the binding of inhibitors. We applied a protocol of consecutive hierarchical filters to search the Available Chemicals Directory. In the first selection step, putative ligands were chosen that exhibit functional groups to anchor the anion-binding pocket of AR. Subsequently, a pharmacophore model based on the binding geometry of IDD594 and the mapping of the binding pocket in terms of putative "hot spots" of binding was applied as a second consecutive filter. In a third and final filtering step, the remaining candidate molecules were flexibly docked into the binding pocket of IDD594 with FlexX and ranked according to their estimated DrugScore values. Out of 206 compounds selected by this search and complemented by a cluster analysis and visual inspection, 9 compounds were selected and subjected to biological testing. Of these, 6 compounds showed IC50 values in the micromolar range. According to the proposed binding mode, the two inhibitors BTB02809 (IC50 = 2.4 +/- 0.5 microM) and JFD00882 (IC50 = 4.1 +/- 1.0 microM) both place a nitro group into the hydrophobic specificity pocket of human AR in an orientation coinciding with the position of the bromine atom of IDD594. The interaction of this Br with Thr113 has been identified as a key feature that is responsible for selectivity enhancement.

Acetates↗
PubMed2004

Structure-based virtual screening: an application to human topoisomerase II alpha.

The eukaryotic topoisomerase II is involved in several vital processes, such as replication, transcription, and recombination. Many compounds interfering with the catalytic action of this enzyme are efficient in human cancer chemotherapy. We applied a methodology combining molecular modeling and virtual screening techniques to identify human topoisomerase II alphainhibitors. Data from structural biology and enzymatic assays together with a good background on the enzyme mechanism of action were helpful in the approach. A human topoisomerase II alpha model provided an insight into the structural features responsible for the activity of the enzyme. A protocol comprising several substructural and protein structure-based three-dimensional pharmacophore filters enabled the successful retrieving of inhibitors of the enzyme from large databases of compounds, thus validating the approach. A subset of protein structural features required for the enzyme inhibition at the protein-DNA interface were identified and incorporated into the pharmacophore models. Compounds sharing a DNA-intercalating chromophore and a moiety interfering with the protein active site emerged as good inhibitors.

Amino Acid Sequence↗
PubMed2006

Identification of COMT and ErmC inhibitors by using a microplate assay in combination with library focusing by virtual screening.

The paper describes a process of facilitated screening by using a combination of molecular modelling and a 96-well microplate assay for the identification of novel inhibitors of catechol-O-methyltransferase (COMT) and bacteria expressing ErmC. With the help of virtual screening the number of compounds processed in the in vitro screening assay was reduced from over 200,000 to 49. Out of the 49, two structurally very similar compounds were identified as confirmed hits with reasonable activity (IC50 values of 26 and 73 microM) and thus as potential core structures for further drug design and development.

Catechol O-Methyltransferase Inhibitors↗
PubMed2005

Structure-based virtual screening for plant-based ERbeta-selective ligands as potential preventative therapy against age-related neurodegenerative diseases.

ERbeta has been associated with estrogen-induced promotion of memory function and neuronal survival. Based on the optimized complex structure of human ERbeta LBD bound with genistein, computer-aided structure-based virtual screening against a natural source chemical database was conducted to determine the occurrence of plant-based ERbeta-selective ligands. Twelve representative hits derived from database screening were assessed for their binding profiles to both ERs, three of which displayed over 100-fold binding selectivity to ERbeta over ERalpha.

Aging↗
PubMed2003

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin.

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.

Binding Sites↗
PubMed2006

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

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

Computational Biology↗
PubMed2006

Discovery of small molecule inhibitors of integrin alphavbeta3 through structure-based virtual screening.

Inhibitors of integrin alphavbeta3 have been implicated in the treatment of a variety of diseases, including tumor metastasis, neovascularization, osteoporosis, and rheumatoid arthritis. It is therefore desirable to develop new types of small molecule inhibitors of integrin alphavbeta3. Here we describe the discovery of novel classes of small molecule inhibitors, via structure-based virtual screening, that target the ligand binding site of integrin alphavbeta3. Application of the docking procedure for screening of a commercially available compound database resulted in a 1774-fold reduction in the size of the screening set (88695 to 50 compounds) and gave a hit-rate of 14% upon biological evaluation (IC50 value ranging from 30 to 200 microM). The best hit, compound 37, 3,4-dichloro-phenylbiguanide, showed inhibitory activity, in a time- and dose-dependent manner, in both cell motility and angiogenesis assays. Based on the best hit, compound 37, a more effective derivative compound 62 has been identified. Furthermore, molecular graphics analyses of a series of substituted phenylbiguanides were carried out to predict the binding mode between the active compounds and integrin alphavbeta3. Our results indicate that the substituted phenylbiguanides might be involved in the inhibition of bivalent cation-mediated ligand binding of integrin alphavbeta3.

Biguanides↗
PubMedDate not supplied

The pharmacophore kernel for virtual screening with support vector machines.

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-point pharmacophores present in the 3D structures of molecules, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approaches. Experimental results suggest that this new approach is competitive with state-of-the-art algorithms based on the 2D structure of molecules for the detection of inhibitors of several drug targets.

Algorithms↗