High-throughput Screening Experiments Research Articles

A Specific Mechanism for Nonspecific Activation in Reporter-Gene Assays

The importance of bioluminescence in enabling a broad range of high-throughput screening (HTS) assay formats is evidenced by widespread use in industry and academia. Therefore, understanding the mechanisms by which reporter enzyme activity can be modulated by small molecules is critical to the interpretation of HTS data. In this Perspective, we provide evidence for stabilization of luciferase by inhibitors in cell-based luciferase reporter-gene assays resulting in the counterintuitive phenomenon of signal activation. These data were derived from our analysis of luciferase inhibitor compound structures and their prevalence in the Molecular Libraries Small Molecule Repository using 100 HTS experiments available in PubChem. Accordingly, we found an enrichment of luciferase inhibitors in luciferase reporter-gene activation assays but not in assays using other reporters. In addition, for several luciferase inhibitor chemotypes, we measured reporter stabilization and signal activation in cells that paralleled the inhibition determined using purified luciferase to provide further experimental support for these contrasting effects.

Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.

Pharmacophore-Based Virtual Screening

Virtual screening (VS) is an important component of cheminformatics and molecular modeling. An abundance of structural information, indicated by both the ever-increasing availability of 3-dimensional (3D) protein structures and the readiness of free conformational databases of commercially available compounds, such as ZINC, supplies a broad platform for VS. At the same time, new technology enables the implementation of more accurate and sophisticated pharmacophore models and the screening of millions of compounds within a manageable period. Therefore, VS is expected to play a more important role in future drug discovery efforts. This paper will examine and compare the advantages and disadvantages of VS against experimental high-throughput screening (HTS). It will also evaluate pharmacophore-based VS against docking-based VS. The strategies leading to successful pharmacophore-based VS are outlined, including how to enumerate a conformational database efficiently, how to select chemical features for a specific pharmacophore model, how to incorporate excluded volumes to enhance the geographical restrictions, and how to optimize a pharmacophore model. Successful examples of pharmacophore-based VS will be presented.

Quantitative Assessment of Hit Detection and Confirmation in Single and Duplicate High-Throughput Screenings

The process of identifying active targets (hits) in high-throughput screening (HTS) usually involves 2 steps: first, removing or adjusting for systematic variation in the measurement process so that extreme values represent strong biological activity instead of systematic biases such as plate effect or edge effect and, second, choosing a meaningful cutoff on the calculated statistic to declare positive compounds. Both false-positive and false-negative errors are inevitable in this process. Common control or estimation of error rates is often based on an assumption of normal distribution of the noise. The error rates in hit detection, especially false-negative rates, are hard to verify because in most assays, only compounds selected in primary screening are followed up in confirmation experiments. In this article, the authors take advantage of a quantitative HTS experiment in which all compounds are tested 42 times over a wide range of 14 concentrations so true positives can be found through a dose-response curve. Using the activity status defined by dose curve, the authors analyzed the effect of various data-processing procedures on the sensitivity and specificity of hit detection, the control of error rate, and hit confirmation. A new summary score is proposed and demonstrated to perform well in hit detection and useful in confirmation rate estimation. In general, adjusting for positional effects is beneficial, but a robust test can prevent overadjustment. Error rates estimated based on normal assumption do not agree with actual error rates, for the tails of noise distribution deviate from normal distribution. However, false discovery rate based on empirically estimated null distribution is very close to observed false discovery proportion.

Simulation of Sequential Screening Experiments Using Emerging Chemical Patterns

A method called "Emerging Chemical Patterns" (ECP) has recently been introduced as a novel approach to binary molecular classification (for example, "active" versus "inactive"). The underlying pattern recognition algorithm was first introduced in computer science and then adopted for applications in medicinal chemistry and compound screening. A special feature is its ability to accurately classify molecules on the basis of very small training sets containing only a few compounds. This feature is highly relevant for virtual compound screening when only very few experimental hits are available as templates. Here we adopt ECP calculations to simulate sequential screening using an experimental high-throughput screening (HTS) data set containing inhibitors of dihydrofolate reductase. In doing so, we focus on minimizing the number of database compounds that need to be evaluated in order to identify a substantial fraction of available hits. We demonstrate that iterative ECP calculations recover on average between approximately 19% and approximately 39% of available hits in the data set while dramatically reducing the number of compounds that need to be tested to between approximately 0.002% and approximately 9% of the screening database.

Evaluation of Micro-Parallel Liquid Chromatography as a Method for HTS-Coupled Actives Verification

The identification of biologically active compounds from high-throughput screening (HTS) can involve considerable postscreening analysis to verify the nature of the sample activity. In this study we evaluated the performance of micro-parallel liquid chromatography (microPLC) as a separation-based enzyme assay platform for follow-up of compound activities found in quantitative HTS of two different targets, a hydrolase and an oxidoreductase. In an effort to couple secondary analysis to primary screening we explored the application of microPLC immediately after a primary screen. In microPLC, up to 24 samples can be loaded and analyzed simultaneously via high-performance liquid chromatography within a specially designed cartridge. In a proof-of-concept experiment for screen-coupled actives verification, we identified, selected, and consolidated the contents of "active" wells from a 1,536-well format HTS experiment into a 384-well plate and subsequently analyzed these samples by a 24-channel microPLC system. The method utilized 0.6% of the original 6-microl 1,536-well assay for the analysis. The analysis revealed several non-biological-based "positive" samples. The main examples included "false" enzyme activators resulting from an increase in well fluorescence due to fluorescent compound or impurity. The microPLC analysis also provided a verification of the activity of two activators of glucocerebrosidase. We discuss the benefits of microPLC and its limitations from the standpoint of ease of use and integration into a seamless postscreen workflow.

High-Throughput Screen for Small Molecule Inhibitors of Mint1-PDZ Domains

Several hundred PDZ (postsynaptic density-95, Drosophila disks-large, ZO-1) domain-containing proteins have been identified in the human genome. PDZ domains play a critical role in organization and function of cellular signaling pathways. Thus, small molecule inhibitors of PDZ domain association with their targets have wide potential applications as research and therapeutic agents. PDZ domains typically bind to a carboxyl-terminal tail of the target protein. Here we describe a high-throughput screening (HTS) assay for small molecule inhibitors of association between Mint1-PDZ domains and N-type Ca2+ channel carboxyl-terminal peptide (NC peptide). The performance of a homogeneous time-resolved fluorescence resonance energy transfer (HTRF) and an amplified luminescent proximity homogeneous assay (ALPHA) were systematically compared in parallel pilot HTS experiments with glutathione S-transferase-Mint1-PDZ1/2 protein and biotinylated NC peptide. Both of the two assays showed similar sensitivities in our target protein assay. Using HTRF-based assay we screened a library of 100,000 small molecule compounds and identified a number of potential "hits." The activity of isolated "hits" was confirmed by ALPHA assay. However, further evaluation revealed that isolated "hits" most likely act as "promiscuous binders," not as specific Mint-PDZ inhibitors, and that additional screening will be required to identify the true Mint-PDZ inhibitors. The assays described provided an example of HTS for a small molecule inhibitor of Mint-PDZ domain that can be easily adapted to other PDZ domain-mediated interactions.

In silico screening against wild-type and mutant Plasmodium falciparum dihydrofolate reductase

Modeling studies were performed on known inhibitors of wild-type as well as quadruple mutant Plasmodium falciparum dihydrofolate reductase (DHFR). GOLD was used to dock 31 pyrimethamine derivatives into the active site of DHFR obtained from the X-ray crystal structures 1J3I.pdb and 1J3K.pdb. Predicted binding affinities from a scoring function were analyzed and evaluated in order to develop criteria for selecting compounds having a greater chance of activity versus wild-type and resistant strains of P. falciparum for future high-throughput screening experiments.

Introduction of a Generally Applicable Method to Estimate Retrieval of Active Molecules for Similarity Searching using Fingerprints

Fingerprints are bit string representations of molecular structure and properties and are among the most widely used computational tools for similarity searching and database screening. Various fingerprint designs are available and their search performance is in general strongly dependent on the compound classes under study and the chemical characteristics of screening databases. Currently, it is not possible to predict the probability of identifying novel hits through fingerprint searching. However, for practical applications, such estimations would be very useful because one might be able, for example, to prioritize fingerprints and compound selection strategies or decide whether or not a similarity search campaign with subsequent experimental evaluation of candidate compounds would be promising at all. We have developed a method that makes it possible to predict the outcome of similarity search calculations using any type of keyed fingerprint. The methodology incorporates bit frequency distributions of reference molecules and the screening database into an information-theoretic function and determines the principally possible recall of active compounds within selection sets of varying size. We calibrate the function on diverse compound classes and accurately predict compound recovery in retrospective virtual screening trials. Furthermore, we correctly predict fingerprint search performance on two experimental high-throughput screening data sets (HTS). Our findings indicate that given a set of reference molecules, a fingerprint, and a screening database, we can readily estimate how likely it will be to retrieve active compounds, without knowledge about the distribution of potential hits in the database.

Modelling of potentially promising SARS protease inhibitors

In many cases, at the beginning of a high throughput screening experiment someinformation about active molecules is already available. Active compounds (suchas substrate analogues, natural products and inhibitors of related proteins) areoften identified in low throughput validation studies on a biochemical target.Sometimes the additional structural information is also available from crystallographicstudies on protein and ligand complexes. In addition, the structural or sequencesimilarity of various protein targets yields a novel possibility for drug discovery.Co-crystallized compounds from homologous proteins can be used to design leadsfor a new target without co-crystallized ligands. In this paper we evaluate howfar such an approach can be used in a real drug campaign, with severe acuterespiratory syndrome (SARS) coronavirus providing an example. Our methodis able to construct small molecules as plausible inhibitors solely on the basisof the set of ligands from crystallized complexes of a protein target, and otherproteins from its structurally homologous family. The accuracy and sensitivity of themethod are estimated here by the subsequent use of an electronic high throughputscreening flexible docking algorithm. The best performing ligands are then used for avery restrictive similarity search for potential inhibitors of the SARS proteasewithin the million compounds from the Ligand.Info small molecule meta-database.The selected molecules can be passed on for further experimental validation.

The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments

RNA interference (RNAi) high-throughput screening (HTS) has been hailed as the 2nd genomics wave following the 1st genomics wave of gene expression microarrays and single-nucleotide polymorphism discovery platforms. Following an RNAi HTS, the authors are interested in identifying short interfering RNA (siRNA) hits with large inhibition/activation effects. For hit selection, the z-score method and its variants are commonly used in primary RNAi HTS experiments. Recently, strictly standardized mean difference (SSMD) has been proposed to measure the siRNA effect represented by the magnitude of difference between an siRNA and a negative reference group. The links between SSMD and d+-probability offer a clear interpretation of siRNA effects from a probability perspective. Hence, SSMD can be used as a ranking metric for hit selection. In this article, the authors investigated both the SSMD-based testing process and the use of SSMD as a ranking metric for hit selection in 2 primary siRNA HTS experiments. The analysis results showed that, as a ranking metric, SSMD was more stable and reliable than percentage inhibition and led to more robust hit selection results. Using the SSMD -based testing method, the false-negative rate can more readily be obtained. More important, the use of the SSMD-based method can result in a reduction in both the false-negative and false-positive rates. The applications presented in this article demonstrate that the SSMD method addresses scientific questions and fills scientific needs better than both percentage inhibition and the commonly used z-score method for hit selection.

“Plate Cherry Picking”: A Novel Semi-Sequential Screening Paradigm for Cheaper, Faster, Information-Rich Compound Selection

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC(50) < 10 microM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming "classic" sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.

Nanoliter microfluidic hybrid method for simultaneous screening and optimization validated with crystallization of membrane proteins

High-throughput screening and optimization experiments are critical to a number of fields, including chemistry and structural and molecular biology. The separation of these two steps may introduce false negatives and a time delay between initial screening and subsequent optimization. Although a hybrid method combining both steps may address these problems, miniaturization is required to minimize sample consumption. This article reports a "hybrid" droplet-based microfluidic approach that combines the steps of screening and optimization into one simple experiment and uses nanoliter-sized plugs to minimize sample consumption. Many distinct reagents were sequentially introduced as approximately 140-nl plugs into a microfluidic device and combined with a substrate and a diluting buffer. Tests were conducted in approximately 10-nl plugs containing different concentrations of a reagent. Methods were developed to form plugs of controlled concentrations, index concentrations, and incubate thousands of plugs inexpensively and without evaporation. To validate the hybrid method and demonstrate its applicability to challenging problems, crystallization of model membrane proteins and handling of solutions of detergents and viscous precipitants were demonstrated. By using 10 microl of protein solution, approximately 1,300 crystallization trials were set up within 20 min by one researcher. This method was compatible with growth, manipulation, and extraction of high-quality crystals of membrane proteins, demonstrated by obtaining high-resolution diffraction images and solving a crystal structure. This robust method requires inexpensive equipment and supplies, should be especially suitable for use in individual laboratories, and could find applications in a number of areas that require chemical, biochemical, and biological screening and optimization.

Steady State Analysis and Experimental Validation of a Model for Hepatic High‐Density Lipoprotein Transport

Transport of high-density lipoprotein (HDL) in the hepatocyte plays a fundamental role in reverse cholesterol transport and regulation of plasma HDL levels. On the basis of a recently developed kinetic model, the steady state distribution of HDL was analyzed. Fractional fluorescence of labeled HDL in the basolateral membrane, sorting endosomes (SE), the subapical compartment/ apical recycling compartment, the biliary canaliculus and in late endosomes and lysosomes (LE/LYS) including expected standard deviation is predicted. Improved parameter estimation was obtained by including kinetic data of apical endocytosis of fluorescent markers for LE/LYS, asialoorosomucoid and Rhodamine-dextran, in the regression. Predicted values using the refined kinetic parameters are in good agreement with experimental values of compartmental steady state fluorescence of Alexa488-HDL in polarized hepatic HepG2 cells. From calculated steady state fluxes, it is suggested that export of HDL from basolateral SE is the key step for determining the transport of HDL through the hepatocyte. The analysis provides testable predictions for high-throughput fluorescence microscopy screening experiments on potential inhibitors of hepatic HDL processing. By quantitative fluorescence imaging and model analysis, it is shown that the phosphoinositide kinase inhibitor wortmannin prevents apical transport of fluorescent HDL from basolateral SE. The results support that endosomes of polarized hepatic cells have different sorting functions and that apical endocytosis is an integrative trafficking step in hepatocytes.

HTS-Corrector: software for the statistical analysis and correction of experimental high-throughput screening data

High-throughput screening (HTS) plays a central role in modern drug discovery, allowing for testing of >100,000 compounds per screen. The aim of our work was to develop and implement methods for minimizing the impact of systematic error in the analysis of HTS data. To the best of our knowledge, two new data correction methods included in HTS-Corrector are not available in any existing commercial software or freeware. This paper describes HTS-Corrector, a software application for the analysis of HTS data, detection and visualization of systematic error, and corresponding correction of HTS signals. Three new methods for the statistical analysis and correction of raw HTS data are included in HTS-Corrector: background evaluation, well correction and hit-sigma distribution procedures intended to minimize the impact of systematic errors. We discuss the main features of HTS-Corrector and demonstrate the benefits of the algorithms.

Robust Statistical Methods for Hit Selection in RNA Interference High-Throughput Screening Experiments

RNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean +/- k standard deviation (SD) and median +/- 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean +/- k SD under the same preset error rate. The number of hits selected by median +/- k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (determining effective siRNAs on a plate-by-plate basis) can adjust for systematic errors in different plates, while an experimentwise analysis, in which effective siRNAs are identified in an analysis of the entire experiment, cannot. However, experimentwise analysis may detect a cluster of true positive hits placed together in one or several plates, while platewise analysis may not. To display hit selection results, we designed a specific figure called a plate-well series plot. We thus suggest the following strategy for hit selection in RNAi HTS experiments. First, choose the quartile-based method, or median +/- k MAD, for identifying effective siRNAs. Second, perform the chosen method experimentwise on transformed/normalized data, such as percentage inhibition, to check the possibility of hit clusters. If a cluster of selected hits are observed, repeat the analysis based on untransformed data to determine whether the cluster is due to an artifact in the data. If no clusters of hits are observed, select hits by performing platewise analysis on transformed data. Third, adopt the plate-well series plot to visualize both the data and the hit selection results, as well as to check for artifacts.

Kinetic analysis of a trehalase-overexpressing strain grown on trehalose: a new tool for respiro-fermentative transition studies in Saccharomyces cerevisiae

The aim was to demonstrate the use of a trehalase-overexpressing Saccharomyces cerevisiae strain grown on trehalose as a valuable tool in the studies of respiro-fermentative transition at a reduced scale. A trehalase-overexpressing strain was cultivated in synthetic medium on trehalose under aerobic conditions. This strain grew at a maximum specific growth rate of 0.16 h(-1) and showed a pure oxidative metabolism. Glucose pulse experiments were carried out in this system in order to quantify the short-term Crabtree effect. These data were then compared with glucose pulse experiments carried out in the conventional way with the wild-type strain in glucose-limited chemostats. Glucose-pulse experiments in aerobic batch cultures grown on trehalose led to a metabolic respiro-fermentative transition similar to the one observed in glucose-limited chemostats. This cultivation system allowed us to quantitatively mimic at the flask scale the Crabtree effect observed in conventional chemostat studies. This study is of primary interest in S. cerevisiae studies in which: (i) the implementation of oxidative growth is required (as with studies of the Crabtree effect and heterologous protein production); (ii) small-scale culture systems are required (e.g. high-throughput mutant screening and isotopic labelling experiments).

Computational Approaches for Predicting Protein–Protein Interactions: A Survey

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.

High Throughput Screening of Chromatographic Phases for Rapid Process Development

AbstractThe necessity of being “first on the market” paired with the need to cut production costs highlights the need for strategies and techniques allowing a fast process development while at the same time covering a high number of potential process parameters in order to reach an optimal process sequence. This can only be met by the use of high throughput screening (HTS) techniques. In this paper HTS applications presently applied in bioprocess development are reviewed, their use in chromatographic process parameter evaluation is discussed and concepts for the rational design of HTS experiments are presented. A rational approach for chromatographic parameter screening comprehends an initial screening procedure for ligand selection followed by the incorporation of specific adsorbent features using HTS pipetting systems finalized by a fine tuning step using packed mini‐columns. The analytical part of these screening techniques clearly sets the bottleneck of these procedures with a trade off between analytical information and analysis time. Finally, intelligent HTS strategies should in addition include efficient data processing paired with modern tools for design of experiments or model based process design.

Statistical Analysis of Systematic Errors in High-Throughput Screening

High-throughput screening (HTS) is an efficient technology for drug discovery. It allows for screening of more than 100,000 compounds a day per screen and requires effective procedures for quality control. The authors have developed a method for evaluating a background surface of an HTS assay; it can be used to correct raw HTS data. This correction is necessary to take into account systematic errors that may affect the procedure of hit selection. The described method allows one to analyze experimental HTS data and determine trends and local fluctuations of the corresponding background surfaces. For an assay with a large number of plates, the deviations of the background surface from a plane are caused by systematic errors. Their influence can be minimized by the subtraction of the systematic background from the raw data. Two experimental HTS assays from the ChemBank database are examined in this article. The systematic error present in these data was estimated and removed from them. It enabled the authors to correct the hit selection procedure for both assays.