Discovery of New Protein Targets of BPA Analogs and Derivatives Associated with Noncommunicable Diseases: A Virtual High-Throughput Screening

Combinatorial methods enable the synthesis of chemical libraries on scales of millions to billions of compounds, but the ability to efficiently screen and sequence such large libraries has remained a major bottleneck for molecular discovery. We developed a novel technology for screening and sequencing libraries of synthetic molecules of up to a billion compounds in size. This method utilizes Fiber-optic Array Scanning Technology (FAST) to screen bead-based libraries of synthetic compounds at a rate of 5 million compounds per minute (~83,000 Hz). This ultra-high-throughput screening platform has been used to screen libraries of synthetic “self-readable” non-natural polymers that can be sequenced at femtomole scale by chemical fragmentation and highresolution mass spectrometry. The versatility and throughput of the platform was demonstrated by screening two libraries of non-natural polyamide polymers with sizes of 1.77M and 1B compounds against the protein targets K-Ras, asialoglycoprotein receptor (ASGPR), IL-6, IL-6 receptor and TNFα. Hits with nanomolar binding affinities were found against all targets, including competitive inhibitors of K-Ras binding to Raf and functionally active uptake ligands for ASGPR facilitating intracellular delivery.

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Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data

CrossRef Listing of Deleted DOIs ◽

10.1177/1087057103258284 ◽

2003 ◽

Vol 8 (6) ◽

pp. 624-633 ◽

Cited By ~ 47

Author(s):

Bert Gunter ◽

Christine Brideau ◽

Bill Pikounis ◽

Andy Liaw

Keyword(s):

Quality Control ◽

High Throughput ◽

High Throughput Screening ◽

Software Tool ◽

Quality Data ◽

Assay Sensitivity ◽

Protein Targets ◽

Modern Drug ◽

Assay Performance ◽

Server Software

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server® HTS application, a custom-developed software tool built from the commercially available S-PLUS® and Stat Server® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.

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A Mega-High-Throughput Screening Platform for the Discovery of Biologically Relevant Sequence-Defined Non-Natural Polymers

10.26434/chemrxiv.13106069.v1 ◽

2020 ◽

Author(s):

Michal Avital-Shmilovici ◽

Xiaohe Liu ◽

Thomas Shaler ◽

Andrew Lowenthal ◽

Pauline Bourbon ◽

...

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Binding Affinities ◽

Natural Polymers ◽

Protein Targets ◽

Biologically Relevant ◽

Novel Technology ◽

Major Bottleneck ◽

Screening Platform ◽

Optic Array

Combinatorial methods enable the synthesis of chemical libraries on scales of millions to billions of compounds, but the ability to efficiently screen and sequence such large libraries has remained a major bottleneck for molecular discovery. We developed a novel technology for screening and sequencing libraries of synthetic molecules of up to a billion compounds in size. This method utilizes Fiber-optic Array Scanning Technology (FAST) to screen bead-based libraries of synthetic compounds at a rate of 5 million compounds per minute (~83,000 Hz). This ultra-high-throughput screening platform has been used to screen libraries of synthetic “self-readable” non-natural polymers that can be sequenced at femtomole scale by chemical fragmentation and highresolution mass spectrometry. The versatility and throughput of the platform was demonstrated by screening two libraries of non-natural polyamide polymers with sizes of 1.77M and 1B compounds against the protein targets K-Ras, asialoglycoprotein receptor (ASGPR), IL-6, IL-6 receptor and TNFα. Hits with nanomolar binding affinities were found against all targets, including competitive inhibitors of K-Ras binding to Raf and functionally active uptake ligands for ASGPR facilitating intracellular delivery.

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Development of small molecules targeting the Wnt pathway for the treatment of colon cancer: a high-throughput screening approach

AJP Gastrointestinal and Liver Physiology ◽

10.1152/ajpgi.00005.2010 ◽

2010 ◽

Vol 299 (2) ◽

pp. G293-G300 ◽

Cited By ~ 31

Author(s):

Wei Chen ◽

Minyong Chen ◽

Larry S. Barak

Keyword(s):

Colon Cancer ◽

Drug Discovery ◽

Wnt Signaling ◽

Small Molecule ◽

High Throughput ◽

High Throughput Screening ◽

Wnt Pathway ◽

Protein Targets ◽

Wnt Proteins ◽

Development And Differentiation

Wnt proteins play major roles in development and differentiation, and abnormalities in their regulation are believed to contribute to the formation of many cancers, including colorectal malignancies. As a result, there has been an interest in identifying small molecule inhibitors of Wnt signaling as tool compounds for research or as precursors to new generations of anticancer drugs. Advancements in robotic technology along with reductions in the costs of equipment, chemical libraries, and information handling have made high-throughput drug discovery programs possible in an academic setting. In this minireview we discuss the most plausible protein targets for inhibiting Wnt signaling in colon cancer therapy, list small molecule Wnt inhibitors that have been identified through recent drug discovery efforts, and provide our laboratory's strategy for identifying novel Wnt signaling antagonists using high-throughput screening. In particular, we summarize the results of a screen of over 1,200 drug and druglike compounds we recently completed in which niclosamide was identified as a Wnt pathway antagonist.

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The utility of yeast as a tool for cell-based, target-directed high-throughput screening

Parasitology ◽

10.1017/s0031182013000425 ◽

2013 ◽

Vol 141 (1) ◽

pp. 8-16 ◽

Cited By ~ 16

Author(s):

J. L. NORCLIFFE ◽

E. ALVAREZ-RUIZ ◽

J. J. MARTIN-PLAZA ◽

P. G. STEEL ◽

P. W. DENNY

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Neglected Tropical Diseases ◽

Cost Effective ◽

Tropical Diseases ◽

Protein Targets ◽

Biochemical Assays ◽

Cost Effective Approach ◽

Cellular Context

SUMMARYMany Neglected Tropical Diseases (NTDs) have recently been subject of increased focus, particularly with relation to high-throughput screening (HTS) initiatives. These vital endeavours largely rely of two approaches, in vitro target-directed screening using biochemical assays or cell-based screening which takes no account of the target or targets being hit. Despite their successes both of these approaches have limitations; for example, the production of soluble protein and a lack of cellular context or the problems and expense of parasite cell culture. In addition, both can be challenging to miniaturize for ultra (u)HTS and expensive to utilize. Yeast-based systems offer a cost-effective approach to study and screen protein targets in a direct-directed manner within a eukaryotic cellular context. In this review, we examine the utility and limitations of yeast cell-based, target-directed screening. In particular we focus on the currently under-explored possibility of using such formats in uHTS screening campaigns for NTDs.

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Using Single Protein/Ligand Binding Models to Predict Active Ligands for Unseen Proteins

10.1101/2020.08.02.233155 ◽

2020 ◽

Author(s):

Vikram Sundar ◽

Lucy Colwell

Keyword(s):

Experimental Data ◽

Ligand Binding ◽

High Throughput Screening ◽

Data Augmentation ◽

Protein Targets ◽

Performance Improvements ◽

Screening Experiments ◽

Binding Models ◽

Single Protein ◽

New Protein

Machine learning models that predict which small molecule ligands bind a single protein target report high levels of accuracy for held-out test data. An important challenge is to extrapolate and make accurate predictions for new protein targets. Improvements in drug-target interaction (DTI) models that address this challenge would have significant impact on drug discovery by eliminating the need for high-throughput screening experiments against new protein targets. Here we propose a data augmentation strategy that addresses this challenge to enable accurate prediction in cases where no experimental data is available. To proceed, we first build single protein-ligand binding models and use these models to predict whether additional ligands bind to each protein. We then use these predictions to augment the experimental data, train standard DTI models, and predict interactions between unseen test proteins and ligands. This approach achieves Area Under the Receiver Operator Characteristic (AUC) > 0.9 consistently on test sets consisting exclusively of proteins and ligands for which the model is given no experimental data. We verify that performance improvements extend to held-out test proteins distant from the training set. Our data augmentation framework can be applied to any DTI model, and enhances performance on a range of simple models.

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