scholarly journals Large-scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high-throughput screens of neuroblastoma cells

2015 ◽  
Vol 87 (6) ◽  
pp. 524-540 ◽  
Author(s):  
Nathalie Harder ◽  
Richa Batra ◽  
Nicolle Diessl ◽  
Sina Gogolin ◽  
Roland Eils ◽  
...  
Keyword(s):  
Cell Migration ◽  
High Throughput ◽  
Large Scale ◽  
Neuroblastoma Cells ◽  
Automatic Analysis ◽  
Experimental Optimization ◽  
High Throughput Screens ◽  
Cell Migration And Proliferation

scholarly journals Large-scale DNA Barcode Library Generation for Biomolecule Identification in High-throughput Screens

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Eli Lyons ◽  
Paul Sheridan ◽  
Georg Tremmel ◽  
Satoru Miyano ◽  
Sumio Sugano
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Dna Barcode ◽  
High Throughput Screens

scholarly journals Dynamic cellular finite-element method for modelling large-scale cell migration and proliferation under the control of mechanical and biochemical cues: a study of re-epithelialization

2017 ◽  
Vol 14 (129) ◽  
pp. 20160959 ◽  
Author(s):  
Jieling Zhao ◽  
Youfang Cao ◽  
Luisa A. DiPietro ◽  
Jie Liang
Keyword(s):  
Finite Element ◽  
Cell Migration ◽  
Large Scale ◽  
Full Range ◽  
Large Population ◽  
Computational Modelling ◽  
Cellular Protein ◽  
Collective Cell Migration ◽  
Cell Shapes ◽  
Cell Migration And Proliferation

Computational modelling of cells can reveal insight into the mechanisms of the important processes of tissue development. However, current cell models have limitations and are challenged to model detailed changes in cellular shapes and physical mechanics when thousands of migrating and interacting cells need to be modelled. Here we describe a novel dynamic cellular finite-element model (DyCelFEM), which accounts for changes in cellular shapes and mechanics. It also models the full range of cell motion, from movements of individual cells to collective cell migrations. The transmission of mechanical forces regulated by intercellular adhesions and their ruptures are also accounted for. Intra-cellular protein signalling networks controlling cell behaviours are embedded in individual cells. We employ DyCelFEM to examine specific effects of biochemical and mechanical cues in regulating cell migration and proliferation, and in controlling tissue patterning using a simplified re-epithelialization model of wound tissue. Our results suggest that biochemical cues are better at guiding cell migration with improved directionality and persistence, while mechanical cues are better at coordinating collective cell migration. Overall, DyCelFEM can be used to study developmental processes when a large population of migrating cells under mechanical and biochemical controls experience complex changes in cell shapes and mechanics.

scholarly journals Combining Cloud-Based Free Energy Calculations, Synthetically Aware Enumerations and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization

2020 ◽  
Author(s):  
Phani Ghanakota ◽  
Pieter Bos ◽  
Kyle Konze ◽  
Joshua Staker ◽  
Gabriel Marques ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
High Throughput ◽  
Large Scale ◽  
Chemical Space ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Fold Enrichment ◽  
Property Space ◽  
High Throughput Screens

The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high throughput screens (HTS) or computational virtual high throughput screens (vHTS). We have previously demonstrated that by coupling reaction-based enumeration, active learning and free energy calculations, a similarly large-scale exploration of chemical space can be extended to the hit-to-lead process. In this work, we augment that approach by coupling large scale enumeration and cloud-based FEP profiling with goal-directed generative machine learning, which results in a higher enrichment of potent ideas compared to large scale enumeration alone, while simultaneously staying within the bounds of a predefined drug-like property space. We are able to achieve this by building the molecular distribution for generative machine learning from the PathFinder rules-based enumeration and optimizing for a weighted sum QSAR based multi-parameter optimization function. We examine the utility of this combined approach by designing potent inhibitors of cyclin-dependent kinase 2 (CDK2) and demonstrate a coupled workflow that can: (1) provide a 6.4 fold enrichment improvement in identifying < 10nM compounds over random selection, and a 1.5 fold enrichment in identifying < 10nM compounds over our previous method (2) rapidly explore relevant chemical space outside the bounds of commercial reagents, (3) use generative ML approaches to “learn” the SAR from large scale in silico enumerations and generate novel idea molecules for a flexible receptor site that are both potent and within relevant physicochemical space and (4) produce over 3,000,000 idea molecules and run 2153 FEP simulations, identifying 69 ideas with a predicted IC<sub>50</sub> < 10nM and 358 ideas with a predicted IC<sub>50</sub> <100 nM. The reported data suggest combining both reaction-based and generative machine learning for ideation results in a higher enrichment of potent compounds over previously described approaches, and can rapidly accelerate the discovery of novel chemical matter within a predefined potency and property space.<br>

scholarly journals Combining Cloud-Based Free Energy Calculations, Synthetically Aware Enumerations and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization

2020 ◽  
Author(s):  
Phani Ghanakota ◽  
Pieter Bos ◽  
Kyle Konze ◽  
Joshua Staker ◽  
Gabriel Marques ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
High Throughput ◽  
Large Scale ◽  
Chemical Space ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Fold Enrichment ◽  
Property Space ◽  
High Throughput Screens

The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high throughput screens (HTS) or computational virtual high throughput screens (vHTS). We have previously demonstrated that by coupling reaction-based enumeration, active learning and free energy calculations, a similarly large-scale exploration of chemical space can be extended to the hit-to-lead process. In this work, we augment that approach by coupling large scale enumeration and cloud-based FEP profiling with goal-directed generative machine learning, which results in a higher enrichment of potent ideas compared to large scale enumeration alone, while simultaneously staying within the bounds of a predefined drug-like property space. We are able to achieve this by building the molecular distribution for generative machine learning from the PathFinder rules-based enumeration and optimizing for a weighted sum QSAR based multi-parameter optimization function. We examine the utility of this combined approach by designing potent inhibitors of cyclin-dependent kinase 2 (CDK2) and demonstrate a coupled workflow that can: (1) provide a 6.4 fold enrichment improvement in identifying < 10nM compounds over random selection, and a 1.5 fold enrichment in identifying < 10nM compounds over our previous method (2) rapidly explore relevant chemical space outside the bounds of commercial reagents, (3) use generative ML approaches to “learn” the SAR from large scale in silico enumerations and generate novel idea molecules for a flexible receptor site that are both potent and within relevant physicochemical space and (4) produce over 3,000,000 idea molecules and run 2153 FEP simulations, identifying 69 ideas with a predicted IC<sub>50</sub> < 10nM and 358 ideas with a predicted IC<sub>50</sub> <100 nM. The reported data suggest combining both reaction-based and generative machine learning for ideation results in a higher enrichment of potent compounds over previously described approaches, and can rapidly accelerate the discovery of novel chemical matter within a predefined potency and property space.<br>

scholarly journals Utility of Large-Scale Transiently Transfected Cells for Cell-Based High-Throughput Screens to Identify Transient Receptor Potential Channel A1 (TRPA1) Antagonists

2006 ◽  
Vol 12 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Jun Chen ◽  
Marc R. Lake ◽  
Reza S. Sabet ◽  
Wende Niforatos ◽  
Steve D. Pratt ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Transient Receptor Potential ◽  
Calcium Influx ◽  
Receptor Potential ◽  
Transient Receptor Potential Channel ◽  
Chemical Library ◽  
Transient Receptor ◽  
High Throughput Screens

Despite increasing use of cell-based assays in high-throughput screening (HTS) and lead optimization, one challenge is the adequate supply of high-quality cells expressing the target of interest. To this end, cell lines stably expressing targets are often established, maintained, and scaled up by cell culture. These steps require large investments of time and resources. Moreover, significant variability invariably occurs in cell yield, viability, expression levels, and target activities. In particular, stable expression of targets such as transient receptor potential A1 (TRPA1) causes toxicity, cell line degeneration, and loss of functional activity. Therefore, in an effort to identify TRPA1 antagonists, the authors used large-scale transiently transfected (LSTT) cells, enabling rapid establishment of assays suitable for HTS. LSTT cells, which could- be stored frozen for a long period of time (e.g., at least 42 weeks), retained TRPA1 protein expression and could be easily revived to produce robust and consistent signals in calcium influx and electrophysiological assays. Using cells from a single transfection, a chemical library of 700,000 compounds was screened, and TRPA1 antagonists were identified. The use of LSTT circumvented issues associated with stable TRPA1 expression, increased flexibility and consistency, and greatly reduced labor and cost. This approach will also be applicable to other pharmaceutical targets.

scholarly journals Stratification and prediction of drug synergy based on target functional similarity

2019 ◽  
Author(s):  
Mi Yang ◽  
Michael P. Menden ◽  
Patricia Jaaks ◽  
Jonathan Dry ◽  
Mathew Garnett ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
Large Scale ◽  
Cancer Cell Lines ◽  
Functional Similarity ◽  
Breast Cancer Cell Lines ◽  
Drug Synergy ◽  
High Throughput Screens ◽  
Synergy Prediction

ABSTRACTTargeted mono-therapies in cancer are hampered by the ability of tumor cells to escape inhibition through rewiring or alternative pathways. Drug combination approaches can provide a means to overcome these resistance mechanisms. Effective use of combinations requires strategies to select combinations from the enormous space of combinations, and to stratify patients according to their likelihood to respond. We here introduce two complementary workflows: One prioritising experiments in high-throughput screens for drug synergy enrichment, and a consecutive workflow to predict hypothesis-driven synergy stratification. Both approaches only need data of efficacy of single drugs. They rely on the notion of target functional similarity between two target proteins. This notion reflects how similarly effective drugs are on different cancer cells as a function of cancer signaling pathways’ activities on those cells. Our synergy prediction workflow revealed that two drugs targeting either the same or functionally opposite pathways are more likely to be synergistic. This enables experimental prioritisation in high-throughput screens and supports the notion that synergy can be achieved by either redundant pathway inhibition or targeting independent compensatory mechanisms. We tested the synergy stratification workflow on seven target protein pairs (AKT/EGFR, AKT/MTOR, BCL2/MTOR, EGFR/MTOR, AKT/BCL2, AKT/ALK and AKT/PARP1, representing 29 combinations and predicted their synergies in 33 breast cancer cell lines (Pearson’s correlation r=0.27). Additionally, we experimentally validated predicted synergy of the BRAF/Insulin Receptor combination (Dabrafenib/BMS−754807) in 48 colorectal cancer cell lines (r=0.5). In conclusion, our synergy prediction workflow can support compound prioritization in large scale drug screenings, and our synergy stratification workflow can select where the efficacy of drugs already known for inducing synergy is higher.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Next-Generation Sequencing: An Emerging Tool for Drug Designing

2019 ◽  
Vol 25 (31) ◽  
pp. 3350-3357 ◽  
Author(s):  
Pooja Tripathi ◽  
Jyotsna Singh ◽  
Jonathan A. Lal ◽  
Vijay Tripathi
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Massively Parallel Sequencing ◽  
Genomic Research ◽  
Biological Research ◽  
Next Generation ◽  
Human Welfare ◽  
Drug Designing ◽  
Generation Sequencing

Background: With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production. Method: In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques. Discussions: The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day. Conclusion: Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

Large-scale High-throughput Screening Revealed 5'-(carbonylamino)-2,3'- bithiophene-4'-carboxylate as Novel Template for Antibacterial Agents

2020 ◽  
Vol 17 (5) ◽  
pp. 716-724
Author(s):  
Yan A. Ivanenkov ◽  
Renat S. Yamidanov ◽  
Ilya A. Osterman ◽  
Petr V. Sergiev ◽  
Vladimir A. Aladinskiy ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Antibacterial Agents ◽  
Sos Response ◽  
Luciferase Assay ◽  
Translational Machinery ◽  
E Coli ◽  
New Class

Background: The key issue in the development of novel antimicrobials is a rapid expansion of new bacterial strains resistant to current antibiotics. Indeed, World Health Organization has reported that bacteria commonly causing infections in hospitals and in the community, e.g. E. Coli, K. pneumoniae and S. aureus, have high resistance vs the last generations of cephalosporins, carbapenems and fluoroquinolones. During the past decades, only few successful efforts to develop and launch new antibacterial medications have been performed. This study aims to identify new class of antibacterial agents using novel high-throughput screening technique. Methods: We have designed library containing 125K compounds not similar in structure (Tanimoto coeff.< 0.7) to that published previously as antibiotics. The HTS platform based on double reporter system pDualrep2 was used to distinguish between molecules able to block translational machinery or induce SOS-response in a model E. coli system. MICs for most active chemicals in LB and M9 medium were determined using broth microdilution assay. Results: In an attempt to discover novel classes of antibacterials, we performed HTS of a large-scale small molecule library using our unique screening platform. This approach permitted us to quickly and robustly evaluate a lot of compounds as well as to determine the mechanism of action in the case of compounds being either translational machinery inhibitors or DNA-damaging agents/replication blockers. HTS has resulted in several new structural classes of molecules exhibiting an attractive antibacterial activity. Herein, we report as promising antibacterials. Two most active compounds from this series showed MIC value of 1.2 (5) and 1.8 μg/mL (6) and good selectivity index. Compound 6 caused RFP induction and low SOS response. In vitro luciferase assay has revealed that it is able to slightly inhibit protein biosynthesis. Compound 5 was tested on several archival strains and exhibited slight activity against gram-negative bacteria and outstanding activity against S. aureus. The key structural requirements for antibacterial potency were also explored. We found, that the unsubstituted carboxylic group is crucial for antibacterial activity as well as the presence of bulky hydrophobic substituents at phenyl fragment. Conclusion: The obtained results provide a solid background for further characterization of the 5'- (carbonylamino)-2,3'-bithiophene-4'-carboxylate derivatives discussed herein as new class of antibacterials and their optimization campaign.

scholarly journals Large-Scale, High-Throughput Validation of Short Hairpin RNA Sequences for RNA Interference

2006 ◽  
Vol 11 (3) ◽  
pp. 236-246 ◽  
Author(s):  
Laurence H. Lamarcq ◽  
Bradley J. Scherer ◽  
Michael L. Phelan ◽  
Nikolai N. Kalnine ◽  
Yen H. Nguyen ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Strong Support ◽  
Gc Content ◽  
Rapid Identification ◽  
Hairpin Rna ◽  
Rna Sequences ◽  
Short Hairpin ◽  
Short Hairpin Rnas ◽  
Interfering Rna

A method for high-throughput cloning and analysis of short hairpin RNAs (shRNAs) is described. Using this approach, 464 shRNAs against 116 different genes were screened for knockdown efficacy, enabling rapid identification of effective shRNAs against 74 genes. Statistical analysis of the effects of various criteria on the activity of the shRNAs confirmed that some of the rules thought to govern small interfering RNA (siRNA) activity also apply to shRNAs. These include moderate GC content, absence of internal hairpins, and asymmetric thermal stability. However, the authors did not find strong support for positionspecific rules. In addition, analysis of the data suggests that not all genes are equally susceptible to RNAinterference (RNAi).

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