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G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Algorithms↗

In silico phenotypic screening method of mutants based on statistical modeling of genetically mixed samples.

In comprehensive functional genomics projects, systematic analysis of phenotypes is vital. However, conventional phenotypic screening is done mainly by imprecise visual observation of qualitative traits, and, therefore, in silico screening techniques for quantitative traits are required. In this report, we propose in silico phenotypic screening method that utilizes a Gaussian mixture model for the trait distribution in the offspring of a mutagenized line and the likelihood ratio test between the estimated Gaussian mixture model and the wild-type single Gaussian model. In order to evaluate the proposed method, we performed a screening experiment using real trait data of Arabidopsis. In this experiment, the proposed screening method properly distinguished the mutant line from the wild-type line. Furthermore, we conducted power analysis of the proposed method and two conventional methods under various simulated conditions of sample size and distribution of trait frequency. The result of the power analysis confirmed the effectiveness of the proposed method compared to the conventional methods.

Algorithms↗

A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening.

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein-compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein-compound docking program calculates the protein-compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.

Binding Sites↗

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

Bacterial Proteins↗

Fragment-based lead discovery: a chemical update.

Fragment-based lead discovery constructs drug leads from small molecular fragments. In theory, this is a highly efficient method for drug discovery, and the technique has become enormously popular in the past few years. In this review, I describe how a variety of approaches in fragment-based lead discovery--including NMR, X-ray crystallography, mass spectrometry, functional screening, and in silico screening--have produced drug leads. Although the examples show that the technique can reliably generate potent molecules, there is still much work to be done to maintain the efficiency of molecules' binding affinities as fragments are linked, expanded, and otherwise improved.

Binding Sites↗

Generalized fragment-substructure based property prediction method.

The need for fast and accurate predictors of pharmaceutically important properties has been increasing due to pressure from high-throughput screening, in-silico screening, and the need to more rapidly identify potential pharmacokinetic issues before drugs advance to the more expensive clinical development stages. A novel method for making predictive models based on decomposing 2D structure into component structural fragments is used to model logP, water solubility, and melting point. The fragment orientation of the method facilitates understanding of how molecules might be altered to improve the desired properties. The 2D structure-based descriptor is computed by analysis of the target molecules with a substructure searching algorithm and a set of fragments selected for chemical and pharmaceutical relevance. These are combined with partial least squares to create predictive models. The correlation coefficients achieved are 0.86 for logP (SE = 0.68), 0.73 for logS (SE = 0.89), and 0.64 (SE = 48.9 degrees) for melting point over diverse data sets of 11,447, 2427, and 5598 molecules, respectively. The models were verified via test sets of compounds not included in the training set.

Journal Article↗

Identification of endogenous surrogate ligands for human P2Y12 receptors by in silico and in vitro methods.

Endogenous ligands acting on a human P2Y12 receptor, one of the G-protein coupled receptors, were searched by in silico screening against our own database, which contains more than 500 animal metabolites. The in silico screening using the docking software AutoDock resulted in selection of cysteinylleukotrienes (CysLTs) and 5-phosphoribosyl 1-pyrophosphate (PRPP), with high free energy changes, in addition to the known P2Y12 ligands such as 2MeSADP and ADP. These candidates were subjected to an in vitro Ca2+ assay using the CHO cells stably expressing P2Y12-G16alpha fusion proteins. We found that CysLTE4 and PRPP acted on the P2Y12 receptor as agonists with the EC50 values of 1.3 and 7.8 nM, respectively. Furthermore, we analyzed the phylogenetic relationship of the P2Y, P2Y-like, and CysLT receptors based on sequence alignment followed by evolutionary analyses. The analyses showed that the P2Y12, P2Y13, P2Y14, GPR87, CysLT-1, and CysLT-2 receptors formed a P2Y-related receptor subfamily with common sequence motifs in the transmembrane regions.

Amino Acid Motifs↗

Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening.

Random screening provided no suitable lead structures in a search for novel inhibitors of the bacterial enzyme DNA gyrase. Therefore, an alternative approach had to be developed. Relying on the detailed 3D structural information of the targeted ATP binding site, our approach combines as key techniques (1) an in silico screening for potential low molecular weight inhibitors, (2) a biased high throughput DNA gyrase screen, (3) validation of the screening hits by biophysical methods, and (4) a 3D guided optimization process. When the in silico screening was performed, the initial data set containing 350 000 compounds could be reduced to 3000 molecules. Testing these 3000 selected compounds in the DNA gyrase assay provided 150 hits clustered in 14 classes. Seven classes could be validated as true, novel DNA gyrase inhibitors that act by binding to the ATP binding site located on subunit B: phenols, 2-amino-triazines, 4-amino-pyrimidines, 2-amino-pyrimidines, pyrrolopyrimidines, indazoles, and 2-hydroxymethyl-indoles. The 3D guided optimization provided highly potent DNA gyrase inhibitors, e. g., the 3,4-disubstituted indazole 23 being a 10 times more potent DNA gyrase inhibitor than novobiocin (3).

Anti-Infective Agents↗

Novel curcumin- and emodin-related compounds identified by in silico 2D/3D conformer screening induce apoptosis in tumor cells.

BACKGROUND: Inhibition of the COP9 signalosome (CSN) associated kinases CK2 and PKD by curcumin causes stabilization of the tumor suppressor p53. It has been shown that curcumin induces tumor cell death and apoptosis. Curcumin and emodin block the CSN-directed c-Jun signaling pathway, which results in diminished c-Jun steady state levels in HeLa cells. The aim of this work was to search for new CSN kinase inhibitors analogue to curcumin and emodin by means of an in silico screening method. METHODS: Here we present a novel method to identify efficient inhibitors of CSN-associated kinases. Using curcumin and emodin as lead structures an in silico screening with our in-house database containing more than 10(6) structures was carried out. Thirty-five compounds were identified and further evaluated by the Lipinski's rule-of-five. Two groups of compounds can be clearly discriminated according to their structures: the curcumin-group and the emodin-group. The compounds were evaluated in in vitro kinase assays and in cell culture experiments. RESULTS: The data revealed 3 compounds of the curcumin-group (e.g. piceatannol) and 4 of the emodin-group (e.g. anthrachinone) as potent inhibitors of CSN-associated kinases. Identified agents increased p53 levels and induced apoptosis in tumor cells as determined by annexin V-FITC binding, DNA fragmentation and caspase activity assays. CONCLUSION: Our data demonstrate that the new in silico screening method is highly efficient for identifying potential anti-tumor drugs.

Annexin A5↗

FAF-Drugs: free ADME/tox filtering of compound collections.

In silico screening based on the structures of the ligands or of the receptors has become an essential tool to facilitate the drug discovery process but compound collections are needed to carry out such in silico experiments. It has been recognized that absorption, distribution, metabolism, excretion and toxicity (ADME/tox) are key properties that need to be considered early on, even during the database preparation stage. FAF-Drugs is an online service based on Frowns (a chemoinformatics toolkit) that allows users to process their own compound collections via simple ADME/Tox filtering rules such as molecular weight, polar surface area, logP or number of rotatable bonds. SMILES (Simplified Molecular Input Line Entry System), CANSMILES (canonical smiles) or SDF (structure data file) files are required as input and molecules that pass or do not pass the filters are sent back in CANSMILES format. This service should thus help scientists engaging in drug discovery campaigns. Other utilities and several compound collections suitable for in silico screening are available at our site. FAF-Drugs can be accessed at http://bioserv.rpbs.jussieu.fr/FAFDrugs.html.

Drug Design↗

Absence of functional insulin receptor substrate-3 (IRS-3) gene in humans.

AIM/HYPOTHESIS: Insulin receptor substrate (IRS) proteins play important roles in insulin action and pancreatic beta-cell function. At least four mammalian IRS molecules have been identified. Although genes and cDNAs encoding human IRS-1, IRS-2, and IRS-4 have been cloned, IRS-3 has been identified only in rodents. Thus, we have attempted to clone the human IRS-3 gene. METHODS: Insulin-stimulated rat or human adipocytes were subjected to Western blot analysis to assess IRS-3 tyrosine phosphorylation. Human liver and adipose cDNA libraries were screened in an effort to clone IRS-3 cDNA. A PCR-based approach was designed to amplify IRS-3 cDNA. Reverse transcription PCR was carried out using mRNA from adipose tissue, liver, and skeletal muscle as templates in combination with an in silico screen using mouse IRS-1, IRS-2 and IRS-3 in a tblastn search of the draft public human genome. RESULTS: In human adipocytes we did not detect a M(r) 60 000 phosphoprotein corresponding to IRS-3, whereas in rat adipocytes IRS-3 protein and insulin-stimulated tyrosine phosphorylation was readily observed. None of the molecular approaches provided evidence for a functional IRS-3gene in human tissue. Two deletions in human IRS-3 gene were identified using bioinformatics. The human IRS-3 gene product is predicted to lack a phosphotyrosine binding domain and also the sequence corresponding amino acid 353-407 of murine IRS-3. The contiguous sequence of genomic DNA between these two homologous regions does not have the coding information for human IRS-3. CONCLUSION/INTERPRETATION: In silico screening of the human IRS-3 genome region, combined with further biological and molecular validation, provides evidence against a functional IRS-3 in humans.

Adipocytes↗

Receptor-based computational screening of compound databases: the main docking-scoring engines.

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance with many new and exciting techniques. The number of protein structures and/or chemical ligands is constantly growing, through the use of parallel chemistry, X-ray crystallography, NMR or homology modeling methods and so is the theoretical understanding of protein-ligand interactions. As such, structure-based approaches to drug-design and in silico screening are becoming routine part of most modern lead discovery programs. Prioritization of compound libraries is an extremely important task that aims at the rapid identification of tight-binding ligands and ultimately new therapeutic compounds. These in silico approaches combined with other experimental methods facilitate the design of new medicines to treat cardiovascular, degenerative, infectious, and neoplastic diseases, among others. Here, we review key concepts and specific features of several selected ligand-receptor docking/scoring methods while several other topics pertaining to the field of in silico screening are reviewed in the following articles of this special issue of Current Protein and Peptide Science.

Animals↗

HTS Technologies--IBC Informa Conference.

In 1997, approximately US $6 billion were spent on worldwide screening technologies. The estimate for the total global market for high-throughput screening (HTS) products and services was about US $1.5 billion (source: Medical and Healthcare Marketplace Guide, 15th Edition, 1999-2000). Genomics and combinatorial chemistry demand a technological shift away from present-day laboratory to fully automated HTS and ultra HTS (uHTS), with over 100,000 compounds screened per day. The potential of new technology in miniaturization and automation will ensure faster and cheaper solutions to discovery. The conference explored the rapidly advancing field of HTS and examined numerous areas of HTS application. Identification and prioritization of viable targets, and the development of 'information rich' screens without reducing screening capacity, are important topics. Furthermore, the essential integration of all these screening, analytical and parallel synthesis systems in one coherent process is considered to be of significant interest. Focused sessions addressed the rapidly advancing areas of ADME (absorption, distribution, metabolism, elimination), toxicity and formulation screening, including virtual 'in silico' screening.

Journal Article↗

The impact of tautomer forms on pharmacophore-based virtual screening.

In the field of in silico screening, many applications do not automatically consider possible tautomeric states of molecules. However, the detection of new compound candidates might rely on correct structural description, which is important for the perfect fit toward the biologically relevant interactions. In this paper, we present a new exhaustive tautomer enumeration approach implemented by means of the CACTVS software package. The approach contains a set of 21 predefined SMIRKS-based transforms and a powerful transformation engine that is capable of generating most tautomers described comprehensively in the literature or found in databases in the field of medicinal chemistry. User-defined tautomer rules applied to specific structural databases or scientific issues can be implemented easily and used instead of the predefined rules. In addition, we describe the impact of tautomer-enriched databases on pharmacophore screening approaches for human matrix metalloproteinase 8 as an example of a protein-based pharmacophore screening scenario and for human cyclin-dependent kinases as an example of a ligand-based pharmacophore screening approach. In both test cases, as a preprocessing step, we have used our new tautomer enumerator tool for the tautomer enrichment of the screening data sets and have used it as a postprocessing step to remove tautomeric duplicates from the results. We could demonstrate that the tautomer-enriched screening data sets show significant advantages compared to their non-enhanced counterparts. The discrimination between hits and nonhits was significantly better in the case of tautomer-enriched databases. Moreover, it has been proved that tautomer-enhanced databases will lead to a higher number of potential hits.

CDC2 Protein Kinase↗

Drug discovery today.

In recent years, tools for the development of new drugs have been dramatically improved. These include genomic and proteomic research, numerous biophysical methods, combinatorial chemistry and screening technologies. In addition, early ADMET studies are employed in order to significantly reduce the failure rate in the development of drug candidates. As a consequence, the lead finding, lead optimization and development process has gained marked enhancement in speed and efficiency. In parallel to this development, major pharma companies are increasingly outsourcing many components of drug discovery research to biotech companies. All these measures are designed to address the need for a faster time to market. New screening methodologies have contributed significantly to the efficiency of the drug discovery process. The conventional screening of single compounds or compound libraries has been dramatically accelerated by high throughput screening methods. In addition, in silico screening methods allow the evaluation of virtual compounds. A wide range of new lead finding and lead optimization opportunities result from novel screening methods by NMR, which are the topic of this review article.

Animals↗

Development and virtual screening of target libraries.

The concomitant development of in silico screening technologies and of three-dimensional information on therapeutically relevant macromolecular targets makes it possible to navigate in the structural proteome and to identify targets fulfilling user-defined queries. This review illustrates some in-house recent advances in the development of target libraries and how they can be browsed to unravel chemogenomic information.

Animals↗

Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening.

In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.

Cholinesterase Inhibitors↗

Streamlining lead discovery by aligning in silico and high-throughput screening.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Bayes Theorem↗