scholarly journals The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens

2014 ◽  
Vol 19 (5) ◽  
pp. 696-706 ◽  
Author(s):  
Aurelie Bornot ◽  
Carolyn Blackett ◽  
Ola Engkvist ◽  
Clare Murray ◽  
Claus Bendtsen
Keyword(s):  
Small Molecule ◽  
Large Scale ◽  
Enrichment Analysis ◽  
Large Set ◽  
Phenotypic Response ◽  
Target Deconvolution ◽  
Small Molecule Compound ◽  
Genetic Compensation ◽  
Complex Target ◽  
Factor Α

A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor–α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

scholarly journals STEM-20. A CANCER STEM CELL-SELECTIVE APOPTOSIS-INDUCING SMALL MOLECULE FOR THE TREATMENT OF GBM

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii200-ii200
Author(s):  
Stephen Skirboll ◽  
Natasha Lucki ◽  
Genaro Villa ◽  
Naja Vergani ◽  
Michael Bollong ◽  
...  
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Small Molecules ◽  
Small Molecule ◽  
Large Scale ◽  
Critical Role ◽  
Tumor Formation ◽  
Cell Type ◽  
Caspase 1 ◽  
Activation Assay

Abstract INTRODUCTION Glioblastoma multiforme (GBM) is the most aggressive form of primary brain cancer. A subpopulation of multipotent cells termed GBM cancer stem cells (CSCs) play a critical role in tumor initiation and maintenance, drug resistance, and recurrence following surgery. New therapeutic strategies for the treatment of GBM have recently focused on targeting CSCs. Here we have used an unbiased large-scale screening approach to identify drug-like small molecules that induce apoptosis in GBM CSCs in a cell type-selective manner. METHODS A luciferase-based survival assay of patient-derived GBM CSC lines was established to perform a large-scale screen of ∼one million drug-like small molecules with the goal of identifying novel compounds that are selectively toxic to chemoresistant GBM CSCs. Compounds found to kill GBM CSC lines as compared to control cell types were further characterized. A caspase activation assay was used to evaluate the mechanism of induced cell death. A xenograft animal model using patient-derived GBM CSCs was employed to test the leading candidate for suppression of in vivo tumor formation. RESULTS We identified a small molecule, termed RIPGBM, from the cell-based chemical screen that induces apoptosis in primary patient-derived GBM CSC cultures. The cell type-dependent selectivity of RIPGBM appears to arise at least in part from redox-dependent formation of a proapoptotic derivative, termed cRIPGBM, in GBM CSCs. cRIPGBM induces caspase 1-dependent apoptosis by binding to receptor-interacting protein kinase 2 (RIPK2) and acting as a molecular switch, which reduces the formation of a prosurvival RIPK2/TAK1 complex and increases the formation of a proapoptotic RIPK2/caspase 1 complex. In an intracranial GBM xenograft mouse model, RIPGBM was found to significantly suppress tumor formation. CONCLUSIONS Our chemical genetics-based approach has identified a small molecule drug candidate and a potential drug target that selectively targets cancer stem cells and provides an approach for the treatment of GBMs.

scholarly journals A small molecule compound with an indole moiety inhibits the main protease of SARS-CoV-2 and blocks virus replication

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shin-ichiro Hattori ◽  
Nobuyo Higashi-Kuwata ◽  
Hironori Hayashi ◽  
Srinivasa Rao Allu ◽  
Jakka Raghavaiah ◽  
...  
Keyword(s):  
Small Molecule ◽  
Covalent Bond ◽  
Amino Acid Residues ◽  
X Ray ◽  
Viral Breakthrough ◽  
Main Protease ◽  
Small Molecule Compound ◽  
Polar Interactions ◽  
High Concentrations

AbstractExcept remdesivir, no specific antivirals for SARS-CoV-2 infection are currently available. Here, we characterize two small-molecule-compounds, named GRL-1720 and 5h, containing an indoline and indole moiety, respectively, which target the SARS-CoV-2 main protease (Mpro). We use VeroE6 cell-based assays with RNA-qPCR, cytopathic assays, and immunocytochemistry and show both compounds to block the infectivity of SARS-CoV-2 with EC50 values of 15 ± 4 and 4.2 ± 0.7 μM for GRL-1720 and 5h, respectively. Remdesivir permitted viral breakthrough at high concentrations; however, compound 5h completely blocks SARS-CoV-2 infection in vitro without viral breakthrough or detectable cytotoxicity. Combination of 5h and remdesivir exhibits synergism against SARS-CoV-2. Additional X-ray structural analysis show that 5h forms a covalent bond with Mpro and makes polar interactions with multiple active site amino acid residues. The present data suggest that 5h might serve as a lead Mpro inhibitor for the development of therapeutics for SARS-CoV-2 infection.

Object-based image segmentation in photogrammetry for cartographic use

2021 ◽  
Author(s):  
Béla Kovács ◽  
Márton Pál ◽  
Fanni Vörös
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Unsupervised Classification ◽  
Large Set ◽  
Bare Rock ◽  
Object Based ◽  
Social Fund ◽  
Elevation Data ◽  
Control Technologies

&lt;p&gt;The use of aerial photography in topography has started in the first decades of the 20&lt;sup&gt;th&lt;/sup&gt; century. Remote sensed data have become indispensable for cartographers and GIS staff when doing large-scale mapping: especially topographic, orienteering and thematic maps. The use of UAVs (unmanned aerial vehicles) for this purpose has also become widespread for some years. Various drones and sensors (RGB, multispectral and hyperspectral) with many specifications are used to capture and process the physical properties of an examined area. In parallel with the development of the hardware, new software solutions are emerging to visualize and analyse photogrammetric material: a large set of algorithms with different approaches are available for image processing.&lt;/p&gt;&lt;p&gt;Our study focuses on the large-scale topographic mapping of vegetation and land cover. Most traditional analogue and digital maps use these layers either for background or highlighted thematic purposes. We propose to use the theory of OBIA &amp;#8211; Object-based Image Analysis to differentiate cover types. This method involves pixels to be grouped into larger polygon units based on either spectral or other variables (e.g. elevation, aspect, curvature in case of DEMs). The neighbours of initial seed points are examined whether they should be added to the region according to the similarity of their attributes. Using OBIA, different land cover types (trees, grass, soils, bare rock surfaces) can be distinguished either with supervised or unsupervised classification &amp;#8211; depending on the purposes of the analyst. Our base data were high-resolution RGB and multispectral images (with 5 bands).&lt;/p&gt;&lt;p&gt;Following this methodology, not only elevation data (e.g. shaded relief or vector contour lines) can be derived from UAV imagery but vector land cover data are available for cartographers and GIS analysts. As the number of distinct land cover groups is free to choose, even quite complex thematic layers can be produced. These layers can serve as subjects of further analyses or for cartographic visualization.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;BK is supported by Application Domain Specific Highly Reliable IT Solutions&amp;#8221; project &amp;#160;has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Thematic Excellence Programme TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.&lt;/p&gt;&lt;p&gt;MP and FV are supported by EFOP-3.6.3-VEKOP-16-2017-00001: Talent Management in Autonomous Vehicle Control Technologies &amp;#8211; The Project is financed by the Hungarian Government and co-financed by the European Social Fund.&lt;/p&gt;

scholarly journals Beyond spatial scalability limitations with a massively parallel method for linear oscillatory problems

2017 ◽  
Vol 32 (6) ◽  
pp. 913-933 ◽  
Author(s):  
Martin Schreiber ◽  
Pedro S Peixoto ◽  
Terry Haut ◽  
Beth Wingate
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Weather Prediction ◽  
Finite Difference Methods ◽  
Scaling Limit ◽  
Performance Model ◽  
Massively Parallel ◽  
Large Set ◽  
Problem Size ◽  
Single Node

This paper presents, discusses and analyses a massively parallel-in-time solver for linear oscillatory partial differential equations, which is a key numerical component for evolving weather, ocean, climate and seismic models. The time parallelization in this solver allows us to significantly exceed the computing resources used by parallelization-in-space methods and results in a correspondingly significantly reduced wall-clock time. One of the major difficulties of achieving Exascale performance for weather prediction is that the strong scaling limit – the parallel performance for a fixed problem size with an increasing number of processors – saturates. A main avenue to circumvent this problem is to introduce new numerical techniques that take advantage of time parallelism. In this paper, we use a time-parallel approximation that retains the frequency information of oscillatory problems. This approximation is based on (a) reformulating the original problem into a large set of independent terms and (b) solving each of these terms independently of each other which can now be accomplished on a large number of high-performance computing resources. Our results are conducted on up to 3586 cores for problem sizes with the parallelization-in-space scalability limited already on a single node. We gain significant reductions in the time-to-solution of 118.3× for spectral methods and 1503.0× for finite-difference methods with the parallelization-in-time approach. A developed and calibrated performance model gives the scalability limitations a priori for this new approach and allows us to extrapolate the performance of the method towards large-scale systems. This work has the potential to contribute as a basic building block of parallelization-in-time approaches, with possible major implications in applied areas modelling oscillatory dominated problems.

Age of Acquisition Norms for a Large Set of Object Names and Their Relation to Adult Estimates and Other Variables

1997 ◽  
Vol 50 (3) ◽  
pp. 528-559 ◽  
Author(s):  
Catriona M. Morrison ◽  
Tameron D. Chappell ◽  
Andrew W. Ellis
Keyword(s):  
Word Learning ◽  
Processing Speed ◽  
Large Scale ◽  
Lexical Processing ◽  
Objective Measure ◽  
Age Of Acquisition ◽  
Large Set ◽  
Name Agreement ◽  
Object Familiarity ◽  
The Relationship

Studies of lexical processing have relied heavily on adult ratings of word learning age or age of acquisition, which have been shown to be strongly predictive of processing speed. This study reports a set of objective norms derived in a large-scale study of British children's naming of 297 pictured objects (including 232 from the Snodgrass & Vanderwart, 1980, set). In addition, data were obtained on measures of rated age of acquisition, rated frequency, imageability, object familiarity, picture-name agreement, and name agreement. We discuss the relationship between the objective measure and adult ratings of word learning age. Objective measures should be used when available, but where not, our data suggest that adult ratings provide a reliable and valid measure of real word learning age.

scholarly journals Transcriptional and post-transcriptional regulation and transcriptional memory of chromatin regulators in response to low temperature

2019 ◽  
Author(s):  
K. Vyse ◽  
L. Faivre ◽  
M. Romich ◽  
M. Pagter ◽  
D. Schubert ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Low Temperature ◽  
Transcriptional Activation ◽  
Large Scale ◽  
Transcriptional Profiling ◽  
Large Fraction ◽  
Histone Variants ◽  
Transcriptional Level ◽  
Large Set ◽  
Dynamic Regulation

AbstractChromatin regulation ensures stable repression of stress-inducible genes under non-stress conditions and transcriptional activation and memory of such an activation of those genes when plants are exposed to stress. However, there is only limited knowledge on how chromatin genes are regulated at the transcriptional and post-transcriptional level upon stress exposure and relief from stress. We have therefore set-up a RT-qPCR-based platform for high-throughput transcriptional profiling of a large set of chromatin genes. We find that the expression of a large fraction of these genes is regulated by cold. In addition, we reveal an induction of several DNA and histone demethylase genes and certain histone variants after plants have been shifted back to ambient temperature (deacclimation), suggesting a role in the memory of cold acclimation. We also re-analyse large scale transcriptomic datasets for transcriptional regulation and alternative splicing (AS) of chromatin genes, uncovering an unexpected level of regulation of these genes, particularly at the splicing level. This includes several vernalization regulating genes whose AS results in cold-regulated protein diversity. Overall, we provide a profiling platform for the analysis of chromatin regulatory genes and integrative analyses of their regulation, suggesting a dynamic regulation of key chromatin genes in response to low temperature stress.

scholarly journals Expression of SSEA4 and TRA1-60 as Marker of Induced Pluripotent Stem Cells by Small Molecule Compound VC6TFZ on Peripheral Blood Mononuclear Cell

2020 ◽  
Author(s):  
A Andrianto ◽  
Adityo Basworo ◽  
Ivana Purnama Dewi ◽  
Budi Susetio Pikir
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Small Molecule ◽  
Pluripotent Stem Cells ◽  
Mononuclear Cells ◽  
Control Group ◽  
Peripheral Blood Mononuclear ◽  
Induced Pluripotency ◽  
Small Molecule Compound ◽  
Induced Pluripotent

IntroductionIt is possible to induce pluripotent stem cells from somatic cells, offering an infinite cell resource with the potential for disease research and use in regenerative medicine. Due to ease of accessibility, minimum invasive treatment, and can be kept frozen, peripheral blood mononuclear cells (PBMC) were an attractive source cell. VC6TFZ, a small molecule compound, has been successfully reprogrammed from mouse fibroblast induced pluripotent stem cells (iPSCs). However, it has not been confirmed in humans.ObjectiveThe aim of this research is to determine whether the small molecule compound VC6TFZ can induced pluripotency of PBMC to generate iPSCs detected with expression of SSEA4 and TRA1-60.MethodsUsing the centrifugation gradient density process, mononuclear cells were separated from peripheral venous blood. Mononuclear cells were cultured for 6 days in the expansion medium. The cells were divided into four groups; group 1 (P1), which was not exposed to small molecules (control group) and groups 2-4 (P2-P4), the experimental groups, subjected to various dosages of the small molecule compound VC6TFZ (VPA, CHIR, Tranylcypromine, FSK, Dznep, and TTNPB). The induction of pluripotency using small molecule compound VC6TFZ was completed within 14 days, then for 7 days the medium shifted to 2i medium. iPSCs identification in based on colony morphology and pluripotent gene expression, SSEA4 and TRA1-60 marker, using immunocytochemistry.ResultsColonies appeared on reprogramming process in day 7th. These colonies had round, large, and cobble stone morphology like ESC. Gene expression of SSEA4 and TRA 1-60 increased statisticaly significant than control group (SSEA4 were P2 p=0.007; P3 p=0.001; P4 p=0.009 and TRA 1-60 were P2 p=0.002; P3 p=0.001; P4 p=0.001).ConclusionSmall molecule compound VC6TFZ could induced pluripotency of human PBMC to generate iPSCs. Pluripotxency marker gene expression, SSEA 4 and TRA 1-60, in the experimental group was statistically significantly higher than in the control group.

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