Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups

Non-specific chemical modification of protein thiol groups continues to be a significant source of false positive hits from high-throughput screening campaigns and can even plague certain protein targets and chemical series well into lead optimization. While experimental tools exist to assess the risk and promiscuity associated with the chemical reactivity of existing compounds, computational tools are desired that can reliably identify substructures that are associated with chemical reactivity to aid in triage of HTS hit lists, external compound purchases, and library design. Here we describe a Bayesian classification model derived from more than 8,800 compounds that have been experimentally assessed for their potential to covalently modify protein targets. The resulting model can be implemented in the large-scale assessment of compound libraries for purchase or design. In addition, the individual substructures identified as highly reactive in the model can be used as look-up tables to guide chemists during hit-to-lead and lead optimization campaigns.     

An informatic pipeline for managing high-throughput screening experiments and analyzing data from stereochemically diverse libraries

Integration of flexible data-analysis tools with cheminformatics methods is a prerequisite for successful identification and validation of “hits” in high-throughput screening (HTS) campaigns. We have designed, developed, and implemented a suite of robust yet flexible cheminformatics tools to support HTS activities at the Broad Institute, three of which are described herein. The “hit-calling” tool allows a researcher to set a hit threshold that can be varied during downstream analysis. The results from the hit-calling exercise are reported to a database for record keeping and further data analysis. The “cherry-picking” tool enables creation of an optimized list of hits for confirmatory and follow-up assays from an HTS hit list. This tool allows filtering by computed chemical property and by substructure. In addition, similarity searches can be performed on hits of interest and sets of related compounds can be selected. The third tool, an “S/SAR viewer,” has been designed specifically for the Broad Institute’s diversity-oriented synthesis (DOS) collection. The compounds in this collection are rich in chiral centers and the full complement of all possible stereoisomers of a given compound are present in the collection. The S/SAR viewer allows rapid identification of both structure/activity relationships and stereo-structure/activity relationships present in HTS data from the DOS collection. Together, these tools enable the prioritization and analysis of hits from diverse compound collections, and enable informed decisions for follow-up biology and chemistry efforts.     

Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.     

Using IMAP technology to identify kinase inhibitors: comparison with a substrate depletion approach and analysis of the nature of false positives

IMAP is a fluorescence polarisation-based assay method which can be applied to the measurement of protein kinase activity. Using a model serine/threonine kinase we found that IMAP generated a good assay window (Z' > 0.8), was very tolerant of DMSO, and was flexible with respect to sample processing (stopped reactions were stable over a period of several days). Using a set of six low molecular weight inhibitors of the kinase, we found a good correlation between IMAP and scintillation proximity assay (SPA) potency data. IMAP, which measures product accumulation, was compared in an HTS setting with a substrate depletion method (luminescence-based measurement of ATP concentration). There was a reasonable (approximately 50%) overlap in primary hits from a 17,000 compound set, but more apparent false positives were generated from the IMAP method. We followed up the compounds that showed activity in the IMAP method but not in the luminescence assay. Approximately 10% of these compounds displayed intrinsic fluorescence, suggesting that they were false actives by virtue of intrinsic spectroscopic properties. Compound activity by competition of phosphopeptide binding to IMAP beads can occur with high concentrations of chelating compounds, but did not occur with any of the false actives, suggesting that this form of interference is rare.     

Structure-based screening as applied to human FABP4: a highly efficient alternative to HTS for hit generation

The time-limiting step in HTS often is the development of an appropriate assay. In addition, hits from HTS fairly often turn out to be false positives and generally display unfavorable properties for further development. Here we describe an alternative process for hit generation, applied to the human adipocyte fatty acid binding protein FABP4. A small molecular ligand for FABP4 that blocks the binding of endogenous ligands may be developed into a drug for the treatment of type-2 diabetes. Using NMR spectroscopy, we screened FABP4 for low-affinity binders in a diversity library consisting of small soluble scaffolds, which yielded 52 initial hits in total. The potencies of these hits were ranked, and crystal structures of FABP4 complexes for two of the hits were obtained. The structural data were subsequently used to direct similarity searches for available analogues, as well as chemical synthesis of 12 novel analogues. In this way, a series of three selective FABP4 ligands with attractive pharmacochemical profiles and potencies of 10 microM or better was obtained.     

An empirical process for the design of high-throughput screening deck filters

A process for objective identification and filtering of undesirable compounds that contribute to high-throughput screening (HTS) deck promiscuity is described. Two methods of mapping hit promiscuity have been developed linking SMARTS-based structural queries with historical primary HTS data. The first compares an expected assay hit rate to actual hit rates. The second examines the propensity of an individual compound to hit multiple assays. Statistical evaluation of the data indicates a correlation between the resultant functional group filters and compound promiscuity. These data corroborate a number of commonly applied filters as well as producing some unexpected results. Application of these models to HTS collection triage reduced the number of in-house compounds considered for screening by 12%. The implications of these findings are further discussed in the context of the HTS screening set and combinatorial library design as well as compound acquisition.     

Analysis of a high-throughput screening data set using potency-scaled molecular similarity algorithms

Molecular similarity methods for ligand-based virtual screening (VS) generally do not take compound potency as a variable or search parameter into account. We have incorporated a logarithmic potency scaling function into two conceptually distinct VS algorithms to account for relative compound potency during search calculations. A high-throughput screening (HTS) data set containing cathepsin B inhibitors was analyzed to evaluate the effects of potency scaling. Sets of template compounds were randomly selected from the HTS data and used to search for hits having varying potency levels in the presence or absence of potency scaling. Enrichment of potent compounds in small subsets of the HTS data set was observed as a consequence of potency scaling. In part, observed enrichments could be rationalized as a result of recentering chemical reference space on a subspace populated by potent compounds. Our findings suggest that VS calculations using multiple reference compounds can be directed toward the preferential detection of potent database hits by scaling compound contributions according to potency differences.     

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.     

Large-scale annotation of small-molecule libraries using public databases

While many large publicly accessible databases provide excellent annotation for biological macromolecules, the same is not true for small chemical compounds. Commercial data sources also fail to encompass an annotation interface for large numbers of compounds and tend to be cost prohibitive to be widely available to biomedical researchers. Therefore, using annotation information for the selection of lead compounds from a modern day high-throughput screening (HTS) campaign presently occurs only under a very limited scale. The recent rapid expansion of the NIH PubChem database provides an opportunity to link existing biological databases with compound catalogs and provides relevant information that potentially could improve the information garnered from large-scale screening efforts. Using the 2.5 million compound collection at the Genomics Institute of the Novartis Research Foundation (GNF) as a model, we determined that approximately 4% of the library contained compounds with potential annotation in such databases as PubChem and the World Drug Index (WDI) as well as related databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and ChemIDplus. Furthermore, the exact structure match analysis showed 32% of GNF compounds can be linked to third party databases via PubChem. We also showed annotations such as MeSH (medical subject headings) terms can be applied to in-house HTS databases in identifying signature biological inhibition profiles of interest as well as expediting the assay validation process. The automated annotation of thousands of screening hits in batch is becoming feasible and has the potential to play an essential role in the hit-to-lead decision making process.     

Learning from the data: mining of large high-throughput screening databases

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.     

NIR-mbc94, a fluorescent ligand that binds to endogenous CB(2) receptors and is amenable to high-throughput screening

High-throughput screening (HTS) of chemical libraries is often used for the unbiased identification of compounds interacting with G protein-coupled receptors (GPCRs), the largest family of therapeutic targets. However, current HTS methods require removing GPCRs from their native environment, which modifies their pharmacodynamic properties and biases the screen toward false positive hits. Here, we developed and validated a molecular imaging (MI) agent, NIR-mbc94, which emits near infrared (NIR) light and selectively binds to endogenously expressed cannabinoid CB(2) receptors,?a recognized target for treating autoimmune diseases, chronic pain and cancer. The precision and ease of this assay allows for the HTS of compounds interacting with CB(2) receptors expressed in their native environment.     

On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data

It has been shown that the generalized F-statistics can give satisfactory performances in identifying differentially expressed genes with microarray data. However, for some complex diseases, it is still possible to identify a high proportion of false positives because of the modest differential expressions of disease related genes and the systematic noises of microarrays. The main purpose of this study is to develop statistical methods for Affymetrix microarray gene expression data so that the impact on false positives from non-expressed genes can be reduced. I proposed two novel generalized F-statistics for identifying differentially expressed genes and a novel approach for estimating adjusting factors. The proposed statistical methods systematically combine filtering of non-expressed genes and identification of differentially expressed genes. For comparison, the discussed statistical methods were applied to an experimental data set for a type 2 diabetes study. In both two- and three-sample analyses, the proposed statistics showed improvement on the control of false positives.     

GFscore: a general nonlinear consensus scoring function for high-throughput docking

Most of the recent published works in the field of docking and scoring protein/ligand complexes have focused on ranking true positives resulting from a Virtual Library Screening (VLS) through the use of a specified or consensus linear scoring function. In this work, we present a methodology to speed up the High Throughput Screening (HTS) process, by allowing focused screens or for hitlist triaging when a prohibitively large number of hits is identified in the primary screen, where we have extended the principle of consensus scoring in a nonlinear neural network manner. This led us to introduce a nonlinear Generalist scoring Function, GFscore, which was trained to discriminate true positives from false positives in a data set of diverse chemical compounds. This original Generalist scoring Function is a combination of the five scoring functions found in the CScore package from Tripos Inc. GFscore eliminates up to 75% of molecules, with a confidence rate of 90%. The final result is a Hit Enrichment in the list of molecules to investigate during a research campaign for biological active compounds where the remaining 25% of molecules would be sent to in vitro screening experiments. GFscore is therefore a powerful tool for the biologist, saving both time and money.     

Comparison of luminescence ADP production assay and radiometric scintillation proximity assay for Cdc7 kinase

Several assay technologies have been successfully adapted and used in HTS to screen for protein kinase inhibitors; however, emerging comparative analysis studies report very low hit overlap between the different technologies, which challenges the working assumption that hit identification is not dependent on the assay method of choice. To help address this issue, we performed two screens on the cancer target, Cdc7-Dbf4 heterodimeric protein kinase, using a direct assay detection method measuring [(33)P]-phosphate incorporation into the substrate and an indirect method measuring residual ADP production using luminescence. We conducted the two screens under similar conditions, where in one, we measured [(33)P]-phosphate incorporation using scintillation proximity assay (SPA), and in the other, we detected luminescence signal of the ATP-dependent luciferase after regenerating ATP from residual ADP (LUM). Surprisingly, little or no correlation were observed between the positives identified by the two methods; at a threshold of 30% inhibition, 25 positives were identified in the LUM screen whereas the SPA screen only identified two positives, Tannic acid and Gentian violet, with Tannic acid being common to both. We tested 20 out of the 25 positive compounds in secondary confirmatory study and confirmed 12 compounds including Tannic acid as Cdc7-Dbf4 kinase inhibitors. Gentian violet, which was only positive in the SPA screen, inhibited luminescence detection and categorized as a false positive. This report demonstrates the strong impact in detection format on the success of a screening campaign and the importance of carefully designed confirmatory assays to eliminate those compounds that target the detection part of the assay.     

Identifying biologically active compound classes using phenotypic screening data and sampling statistics

Scoring the activity of compounds in phenotypic high-throughput assays presents a unique challenge because of the limited resolution and inherent measurement error of these assays. Techniques that leverage the structural similarity of compounds within an assay can be used to improve the hit-recovery rate from screening data. A technique is presented that uses clustering and sampling statistics to predict likely compound activity by scoring entire structural classes. A set of phenotypic assays performed against a commercially available compound library was used as a test set. Using the class-scoring technique, the resultant activity prediction scores were more reproducible than individual assay measurements, and class scoring recovered known active compounds more efficiently than individual assay measurements because class scoring had fewer false positives. Known biologically active compounds were recovered 87% of the time using class scores, suggesting a low false-negative rate that compared well to individual assay measurements. In addition, many weak and potentially novel classes of active compounds, overlooked by individual assay measurements, were suggested.     

Identification of biologically active PDE11-selective inhibitors using a yeast-based high-throughput screen

The biological roles of cyclic nucleotide phosphodiesterase 11 (PDE11) enzymes are poorly understood, in part due to the lack of selective inhibitors. To address the need for such compounds, we completed an ~200,000 compound high-throughput screen (HTS) for PDE11 inhibitors using a yeast-based growth assay, and identified 4 potent and selective PDE11 inhibitors. One compound, along with two structural analogs, elevates cAMP and cortisol levels in human adrenocortical cells, consistent with gene association studies that link PDE11 activity to adrenal function. As such, these compounds can immediately serve as chemical tools to study PDE11 function in cell culture, and as leads to develop therapeutics for the treatment of adrenal insufficiencies. Our results further validate this yeast-based HTS platform for the discovery of potent, selective, and biologically active PDE inhibitors.     

Fragment-based drug design: computational & experimental state of the art

Fragment-based screening is an emerging technology which is used as an alternative to high-throughput screening (HTS), and often in parallel. Fragment screening focuses on very small compounds. Because of their small size and simplicity, fragments exhibit a low to medium binding affinity (mM to μM) and must therefore be screened at high concentration in order to detect binding events. Since some issues are associated with high-concentration screening in biochemical assays, biophysical methods are generally employed in fragment screening campaigns. Moreover, these techniques are very sensitive and some of them can give precise information about the binding mode of fragments, which facilitates the mandatory hit-to-lead optimization. One of the main advantages of fragment-based screening is that fragment hits generally exhibit a strong binding with respect to their size, and their subsequent optimization should lead to compounds with better pharmacokinetic properties compared to molecules evolved from HTS hits. In other words, fragments are interesting starting points for drug discovery projects. Besides, the chemical space of low-complexity compounds is very limited in comparison to that of drug-like molecules, and thus easier to explore with a screening library of limited size. Furthermore, the "combinatorial explosion" effect ensures that the resulting combinations of interlinked binding fragments may cover a significant part of "drug-like" chemical space. In parallel to experimental screening, virtual screening techniques, dedicated to fragments or wider compounds, are gaining momentum in order to further reduce the number of compounds to test. This article is a review of the latest news in both experimental and in silico virtual screening in the fragment-based discovery field. Given the specificity of this journal, special attention will be given to fragment library design.     

Fluorine-NMR competition binding experiments for high-throughput screening of large compound mixtures

High-throughput ligand-based NMR screening with competition binding experiments is extended to (19)F detection. Fluorine is a favorable nucleus for these experiments because of the significant contribution of the Chemical Shift Anisotropy (CSA) to the (19)F transverse relaxation of the ligand signal when bound to a macromolecular target. A low to moderate affinity ligand containing a fluorine atom is used as a reference molecule for the detection and characterization of new ligands. Titration NMR experiments with the selected reference compound are performed for finding the optimal set-up conditions for HTS and for deriving the binding constants of the identified NMR hits. Rapid HTS of large chemical mixtures and plant or fungi extracts against the receptor of interest is possible due to the high sensitivity of the (19)F nucleus and the absence of overlap with the signals of the mixtures to be screened. Finally, a novel approach for HTS using a reference molecule in combination with a control molecule is presented.     

ADLOC: An Aptamer‐Displacement Assay Based on Luminescent Oxygen Channeling

Functional nucleic acids, such as aptamers and allosteric ribozymes, can sense their ligands specifically, thereby undergoing structural alterations that can be converted into a detectable signal. The direct coupling of molecular recognition to signal generation enables the production of versatile reporters that can be applied as molecular probes for various purposes, including high‐throughput screening. Here we describe an unprecedented type of a nucleic acid‐based sensor system and show that it is amenable to high‐throughput screening (HTS) applications. The approach detects the displacement of an aptamer from its bound protein partner by means of luminescent oxygen channeling. In a proof‐of‐principle study we demonstrate that the format is feasible for efficient identification of small drug‐like molecules that bind to a protein target, in this case to the Sec7 domain of cytohesin. We extended the approach to a new cytohesin‐specific single chain DNA aptamer, C10.41, which exhibits a similar binding behavior to cytohesins but has the advantage of being more stable and easier to synthesize and to modify than the RNA‐aptamer M69. The results obtained with both aptamers indicate the general suitability of the aptamer‐displacement assay based on luminescent oxygen channelling (ADLOC) for HTS. We also analyzed the potential for false positive hits and identified from a library of 18 000 drug‐like small molecules two compounds as strong singlet‐oxygen quenchers. With full automation and the use of commercially available plate readers, we estimate that the ADLOC‐based assay described here could be used to screen at least 100 000 compounds per day.