High-throughput screening by mass spectrometry: comparison with the scintillation proximity assay with a focused-file screen of AKT1/PKB alpha

Mass spectrometry is an emerging format for label-free high-throughput screening. The main limitation of mass spectrometry is throughput, due to the requirement to purify samples prior to ionization. Here the authors compare an automated high-throughput mass spectrometry (HTMS) system (RapidFire) with the scintillation proximity assay (SPA). The cancer therapy target AKT1/PKBalpha was screened against a focused library of kinase inhibitors and IC50 values determined for all compounds that exhibit > 50% inhibition. A selection of additional compounds that exhibited 相似文献   

"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.     

Identifying actives from HTS data sets: practical approaches for the selection of an appropriate HTS data-processing method and quality control review

High-throughput screening (HTS) has achieved a dominant role in drug discovery over the past 2 decades. The goal of HTS is to identify active compounds (hits) by screening large numbers of diverse chemical compounds against selected targets and/or cellular phenotypes. The HTS process consists of multiple automated steps involving compound handling, liquid transfers, and assay signal capture, all of which unavoidably contribute to systematic variation in the screening data. The challenge is to distinguish biologically active compounds from assay variability. Traditional plate controls-based and non-controls-based statistical methods have been widely used for HTS data processing and active identification by both the pharmaceutical industry and academic sectors. More recently, improved robust statistical methods have been introduced, reducing the impact of systematic row/column effects in HTS data. To apply such robust methods effectively and properly, we need to understand their necessity and functionality. Data from 6 HTS case histories are presented to illustrate that robust statistical methods may sometimes be misleading and can result in more, rather than less, false positives or false negatives. In practice, no single method is the best hit detection method for every HTS data set. However, to aid the selection of the most appropriate HTS data-processing and active identification methods, the authors developed a 3-step statistical decision methodology. Step 1 is to determine the most appropriate HTS data-processing method and establish criteria for quality control review and active identification from 3-day assay signal window and DMSO validation tests. Step 2 is to perform a multilevel statistical and graphical review of the screening data to exclude data that fall outside the quality control criteria. Step 3 is to apply the established active criterion to the quality-assured data to identify the active compounds.     

Comparison of assay technologies for a tyrosine kinase assay generates different results in high throughput screening

In today's high-throughput screening (HTS) environment, an increasing number of assay detection technologies are routinely utilized in lead finding programs. Because of the relatively broad applicability of several of these technologies, one is often faced with a choice of which technology to utilize for a specific assay. The aim of this study was to address the question of whether the same compounds would be identified from screening a set of samples in three different versions of an HTS assay. Here, three different versions of a tyrosine kinase assay were established using scintillation proximity assay (SPA), homogeneous time-resolved fluorescence resonance energy transfer (HTR-FRET), and fluorescence polarization (FP) technologies. In this study, 30,000 compounds were evaluated in each version of the kinase assay in primary screening, deconvolution, and dose-response experiments. From this effort, there was only a small degree of overlap of active compounds identified subsequent to the deconvolution experiment. When all active compounds were then profiled in all three assays, 100 and 101 active compounds were identified in the HTR-FRET and FP assays, respectively. In contrast, 40 compounds were identified in the SPA version of the kinase assay, whereas all of these compounds were detected in the HTR-FRET assay only 35 were active in the FP assay. Although there was good correlation between the IC(50) values obtained in the HTR-FRET and FP assays, poor correlations were obtained with the IC(50) values obtained in the SPA assay. These findings suggest that significant differences can be observed from HTS depending on the assay technology that is utilized, particularly in assays with high hit rates.     

Identification of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives as inhibitors of the phosphatase SerB653 in Porphyromonas gingivalis, implicated in periodontitis

The serine phosphatase SerB653 plays a crucial role in the infection of Porphyromonas gingivalis, which contributes to the pathogenesis of periodontitis, an inflammatory disease of teeth-supporting tissues. Because functional loss of SerB653 eliminates the virulence of P. gingivalis, SerB653 inhibitors are considered potential periodontitis therapeutic or preventive agents. To identify SerB653 inhibitors with potent anti-periodontitis activity, we conducted a high-throughput screen of a representative 6800-compound subset of a synthetic chemical library of the Korea Chemical Bank (KCB) for compounds with activity against SerB653. The primary screening yielded 150 hits, and subsequent confirmatory studies identified eight compounds, mainly within a single cluster of 3-acyl-2-phenylamino-1,4-dihydroquinolin-4-one derivatives, that showed greater than 50% inhibition of SerB653 activity at a concentration of 50μM. A second screening with a focused library identified 10 compounds with IC(50) values less than 10μM. In antibacterial tests, three of these compounds showed a minimum inhibitory concentration against P. gingivalis growth of 5-50nM.     

Density guided importance sampling: application to a reduced model of protein folding

MOTIVATION: Monte Carlo methods are the most effective means of exploring the energy landscapes of protein folding. The rugged topography of folding energy landscapes causes sampling inefficiencies however, particularly at low, physiological temperatures. RESULTS: A hybrid Monte Carlo method, termed density guided importance sampling (DGIS), is presented that overcomes these sampling inefficiencies. The method is shown to be highly accurate and efficient in determining Boltzmann weighted structural metrics of a discrete off-lattice protein model. In comparison to the Metropolis Monte Carlo method, and the hybrid Monte Carlo methods, jump-walking, smart-walking and replica-exchange, the DGIS method is shown to be more efficient, requiring no parameter optimization. The method guides the simulation towards under-sampled regions of the energy spectrum and recognizes when equilibrium has been reached, avoiding arbitrary and excessively long simulation times. AVAILABILITY: Fortran code available from authors upon request. CONTACT: m.j.parker@leeds.ac.uk.     

Test of the Inverse Monte Carlo Method for the Calculation of Interatomic Potential Energies in Atomic Liquids

Abstract

The principle purpose of this paper is to demonstrate the use of the Inverse Monte Carlo technique for calculating pair interaction energies in monoatomic liquids from a given equilibrium property. This method is based on the mathematical relation between transition probability and pair potential given by the fundamental equation of the “importance sampling” Monte Carlo method. In order to have well defined conditions for the test of the Inverse Monte Carlo method a Metropolis Monte Carlo simulation of a Lennard Jones liquid is carried out to give the equilibrium pair correlation function determined by the assumed potential. Because an equilibrium configuration is prerequisite for an Inverse Monte Carlo simulation a model system is generated reproducing the pair correlation function, which has been calculated by the Metropolis Monte Carlo simulation and therefore representing the system in thermal equilibrium. This configuration is used to simulate virtual atom displacements. The resulting changes in atom distribution for each single simulation step are inserted in a set of non-linear equations defining the transition probability for the virtual change of configuration. The solution of the set of equations for pair interaction energies yields the Lennard Jones potential by which the equilibrium configuration has been determined.     

Monte Carlo loop refinement and virtual screening of the thyroid-stimulating hormone receptor transmembrane domain

Metropolis Monte Carlo (MMC) loop refinement has been performed on the three extracellular loops (ECLs) of rhodopsin and opsin-based homology models of the thyroid-stimulating hormone receptor transmembrane domain, a class A type G protein-coupled receptor. The Monte Carlo sampling technique, employing torsion angles of amino acid side chains and local moves for the six consecutive backbone torsion angles, has previously reproduced the conformation of several loops with known crystal structures with accuracy consistently less than 2?Å. A grid-based potential map, which includes van der Waals, electrostatics, hydrophobic as well as hydrogen-bond potentials for bulk protein environment and the solvation effect, has been used to significantly reduce the computational cost of energy evaluation. A modified sigmoidal distance-dependent dielectric function has been implemented in conjunction with the desolvation and hydrogen-bonding terms. A long high-temperature simulation with 2?kcal/mol repulsion potential resulted in extensive sampling of the conformational space. The slow annealing leading to the low-energy structures predicted secondary structure by the MMC technique. Molecular docking with the reported agonist reproduced the binding site within 1.5?Å. Virtual screening performed on the three lowest structures showed that the ligand-binding mode in the inter-helical region is dependent on the ECL conformations.     

Pooling for improved screening of combinatorial libraries for directed evolution

Following diversity generation in combinatorial protein engineering, a significant amount of effort is expended in screening the library for improved variants. Pooling, or combining multiple cells into the same assay well when screening, is a means to increase throughput and screen a larger portion of the library with less time and effort. We have developed and validated a Monte Carlo simulation model of pooling and used it to screen a library of beta-galactosidase mutants randomized in the active site to increase their activity toward fucosides. Here, we show that our model can successfully predict the number of highly improved mutants obtained via pooling and that pooling does increase the number of good mutants obtained. In unpooled conditions, we found a total of three mutants with higher activity toward p-nitrophenyl-beta-D-fucoside than that of the wild-type beta-galactosidase, whereas when pooling 10 cells per well we found a total of approximately 10 improved mutants. In addition, the number of "supermutants", those with the highest activity increase, was also higher when pooling was used. Pooling is a useful tool for increasing the efficiency of screening combinatorial protein engineering libraries.     

Structure of the hydration envelope of the B-form of polydeoxyribonucleotides poly(dA-dC).poly(dG-dT) and poly(dA-dG).poly(dTs-dT) from the data of Monte-Carlo simulation

The results of a Monte Carlo simulation of the hydration shell of two polynucleotides poly (dA-dC).poly(dG-dT) and poly(dA-dG).poly(dC-dT) are reported. This study is a part of a series of Monte Carlo computations of the hydration of regular polydeoxyribonucleotides with dinucleotide repeat aimed at looking for dependences of hydration shell structure on base sequence. The coordinates of the main local maximal of water density near the polymers and the topology of the most probable one- and two-membered water bridges are published. For most of the sequences a common primary hydration of base edges of successive base pairs is characteristic. The AT-homopolymeric sequence represents an exception with autonomous primary hydration of a base pair in both grooves, which correlates with the sequence-dependent flexibility and the occurrence of bends of DNA.     

A Note on Interval Estimation of the Simple Difference in Data with Correlated Matched Pairs

When we employ cluster sampling to collect data with matched pairs, the assumption of independence between all matched pairs is not likely true. This paper notes that applying interval estimators, that do not account for the intraclass correlation between matched pairs, to estimate the simple difference between two proportions of response can be quite misleading, especially when both the number of matched pairs per cluster and the intraclass correlation between matched pairs within clusters are large. This paper develops two asymptotic interval estimators of the simple difference, that accommodate the data of cluster sampling with correlated matched pairs. This paper further applies Monte Carlo simulation to compare the finite sample performance of these estimators and demonstrates that the interval estimator, derived from a quadratic equation proposed here, can actually perform quite well in a variety of situations.     

A model-free way of representing hyperthermia cell survival data

We present a method of fitting curves to cell survival data that is free from all model assumptions, requiring only that the fitted curves be decreasing and reasonably smooth, where the degree of smoothness is determined from considerations of experimental error. The fitted curves are then differentiated to yield frequency distributions of cell killing times, which may be of value in defining subpopulations with different sensitivities to the cytotoxic agent under study. In addition, confidence intervals on the fitted curves and frequency distributions are obtained by Monte Carlo simulation. The results allow the objective and model-free assessment of the effects of various experimental interventions on cell survival.     

Bayesian prediction of spatial count data using generalized linear mixed models

Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.     

A screen against Leishmania intracellular amastigotes: comparison to a promastigote screen and identification of a host cell-specific hit

The ability to screen compounds in a high-throughput manner is essential in the process of small molecule drug discovery. Critical to the success of screening strategies is the proper design of the assay, often implying a compromise between ease/speed and a biologically relevant setting. Leishmaniasis is a major neglected disease with limited therapeutic options. In order to streamline efforts for the design of productive drug screens against Leishmania, we compared the efficiency of two screening methods, one targeting the free living and easily cultured promastigote (insect-infective) stage, the other targeting the clinically relevant but more difficult to culture intra-macrophage amastigote (mammal-infective) stage. Screening of a 909-member library of bioactive compounds against Leishmania donovani revealed 59 hits in the promastigote primary screen and 27 in the intracellular amastigote screen, with 26 hits shared by both screens. This suggested that screening against the promastigote stage, although more suitable for automation, fails to identify all active compounds and leads to numerous false positive hits. Of particular interest was the identification of one compound specific to the infective amastigote stage of the parasite. This compound affects intracellular but not axenic parasites, suggesting a host cell-dependent mechanism of action, opening new avenues for anti-leishmanial chemotherapy.     

Monte Carlo estimates of natural variation in HIV infection

We describe a Monte Carlo simulation of the within-host dynamics of human immunodeficiency virus 1 (HIV-1). The simulation proceeds at the level of individual T-cells and virions in a small volume of plasma, thus capturing the inherent stochasticity in viral replication, mutation and T-cell infection. When cell lifetimes are distributed exponentially in the Monte Carlo approach, our simulation results are in perfect agreement with the predictions of the corresponding systems of differential equations from the literature. The Monte Carlo model, however, uniquely allows us to estimate the natural variability in important parameters such as the T-cell count, viral load, and the basic reproductive ratio, in both the presence and absence of drug therapy. The simulation also yields the probability that an infection will not become established after exposure to a viral inoculum of a given size. Finally, we extend the Monte Carlo approach to include distributions of cell lifetimes that are less-dispersed than exponential.     

Statistical practice in high-throughput screening data analysis

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.     

Evaluation of Mycobacterium smegmatis as a possible surrogate screen for selecting molecules active against multi-drug resistant Mycobacterium tuberculosis

New and better drugs are needed for tuberculosis (TB), particularly for the multi-drug resistant (MDR) disease. However, the highly infectious nature of MDR Mycobacterium tuberculosis restricts its use for large scale screening of probable drug candidates. We have evaluated the potential of a screen based on a 'fast grower' mycobacterium to shortlist compounds which could be active against MDR M. tuberculosis. Sensitivity profiles of M. smegmatis, M. phlei and M. fortuitum as well as MDR clinical isolates of M. tuberculosis were determined against anti-TB drugs isoniazid and rifampicin. Among the three fast growers, M. smegmatis was found to display a profile similar to MDR M. tuberculosis. Subsequently we evaluated the performance of M. smegmatis as a 'surrogate' screen for 120 compounds which were synthesized for anti-TB activity. Fifty of these molecules were active against M. tuberculosis H(37)Rv at a minimum inhibitory concentration (MIC) cutoff of 相似文献   

Identification of a small molecule that induces mitotic arrest using a simplified high-content screening assay and data analysis method

High-content screening has emerged as a new and powerful technique for identifying small-molecule modulators of mammalian cell biology. The authors describe the development and execution of a high-content screen to identify small molecules that induce mitotic arrest in mammalian cancer cells. Many widely used chemotherapeutics, such as Taxol and vinblastine, induce mitotic arrest, and the creation of new drugs that also induce mitotic arrest may have tremendous therapeutic value. In their screen, the authors employed a simple DNA stain (DAPI) and a sensitive nonparametric statistical test to identify compounds from an internal collection of approximately 13,000 high-quality lead-like small molecules. Subsequent analysis of 1 active compound indicated that it induces mitotic arrest, assessed using a high-content phosphohistone H3 detection assay, and caused cell proliferation defects in multiple cancer cell lines. The active compound, a quinazolinone originating from a natural product-like subset of the screened compounds, is active in cells at approximately 500 nM and appears to act by inhibiting the polymerization of tubulin.