scholarly journals High-Throughput Cellular Thermal Shift Assays in Research and Drug Discovery

2019 ◽  
Vol 25 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Mark J. Henderson ◽  
Marc A. Holbert ◽  
Anton Simeonov ◽  
Lorena A. Kallal
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Microtiter Plate ◽  
Target Engagement ◽  
Biochemical Activity ◽  
Future Directions ◽  
Protein Target ◽  
High Throughput Screens ◽  
Beta Galactosidase ◽  
Thermal Shift

Thermal shift assays (TSAs) can reveal changes in protein structure, due to a resultant change in protein thermal stability. Since proteins are often stabilized upon binding of ligand molecules, these assays can provide a readout for protein target engagement. TSA has traditionally been applied using purified proteins and more recently has been extended to study target engagement in cellular environments with the emergence of cellular thermal shift assays (CETSAs). The utility of CETSA in confirming molecular interaction with targets in a more native context, and the desire to apply this technique more broadly, has fueled the emergence of higher-throughput techniques for CETSA (HT-CETSA). Recent studies have demonstrated that HT-CETSA can be performed in standard 96-, 384-, and 1536-well microtiter plate formats using methods such as beta-galactosidase and NanoLuciferase reporters and AlphaLISA assays. HT-CETSA methods can be used to select and characterize compounds from high-throughput screens and to prioritize compounds in lead optimization by facilitating dose–response experiments. In conjunction with cellular and biochemical activity assays for targets, HT-CETSA can be a valuable addition to the suite of assays available to characterize molecules of interest. Despite the successes in implementing HT-CETSA for a diverse set of targets, caveats and challenges must also be recognized to avoid overinterpretation of results. Here, we review the current landscape of HT-CETSA and discuss the methodologies, practical considerations, challenges, and applications of this approach in research and drug discovery. Additionally, a perspective on potential future directions for the technology is presented.

scholarly journals Positioning High-Throughput CETSA in Early Drug Discovery through Screening against B-Raf and PARP1

2018 ◽  
Vol 24 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Joseph Shaw ◽  
Ian Dale ◽  
Paul Hemsley ◽  
Lindsey Leach ◽  
Nancy Dekki ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Target Engagement ◽  
Native State ◽  
Cellular Target ◽  
Protein Thermal Stability ◽  
Cellular Thermal Shift Assay ◽  
Hit Identification ◽  
Early Drug ◽  
Thermal Shift

Methods to measure cellular target engagement are increasingly being used in early drug discovery. The Cellular Thermal Shift Assay (CETSA) is one such method. CETSA can investigate target engagement by measuring changes in protein thermal stability upon compound binding within the intracellular environment. It can be performed in high-throughput, microplate-based formats to enable broader application to early drug discovery campaigns, though high-throughput forms of CETSA have only been reported for a limited number of targets. CETSA offers the advantage of investigating the target of interest in its physiological environment and native state, but it is not clear yet how well this technology correlates to more established and conventional cellular and biochemical approaches widely used in drug discovery. We report two novel high-throughput CETSA (CETSA HT) assays for B-Raf and PARP1, demonstrating the application of this technology to additional targets. By performing comparative analyses with other assays, we show that CETSA HT correlates well with other screening technologies and can be applied throughout various stages of hit identification and lead optimization. Our results support the use of CETSA HT as a broadly applicable and valuable methodology to help drive drug discovery campaigns to molecules that engage the intended target in cells.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals High-Density Miniaturized Thermal Shift Assays as a General Strategy for Drug Discovery

2001 ◽  
Vol 6 (6) ◽  
pp. 429-440 ◽  
Author(s):  
Michael W. Pantoliano ◽  
Eugene C. Petrella ◽  
Joseph D. Kwasnoski ◽  
Victor S. Lobanov ◽  
James Myslik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening Strategy ◽  
General Strategy ◽  
General Applicability ◽  
High Throughput Drug Screening ◽  
Compound Libraries ◽  
Thermal Shift

More general and universally applicable drug discovery assay technologies are needed in order to keep pace with the recent advances in combinatorial chemistry and genomics-based target generation. Ligand-induced conformational stabilization of proteins is a well-understood phenomenon in which substrates, inhibitors, cofactors, and even other proteins provide enhanced stability to proteins on binding. This phenomenon is based on the energetic coupling of the ligand-binding and protein-melting reactions. In an attempt to harness these biophysical properties for drug discovery, fully automated instrumentation was designed and implemented to perform miniaturized fluorescence-based thermal shift assays in a microplate format for the high throughput screening of compound libraries. Validation of this process and instrumentation was achieved by investigating ligand binding to more than 100 protein targets. The general applicability of the thermal shift screening strategy was found to be an important advantage because it circumvents the need to design and retool new assays with each new therapeutic target. Moreover, the miniaturized thermal shift assay methodology does not require any prior knowledge of a therapeutic target's function, making it ideally suited for the quantitative high throughput drug screening and evaluation of targets derived from genomics.

scholarly journals Providing the ‘Best’ Lipophilicity Assessment in a Drug Discovery Environment

2021 ◽  
Author(s):  
george chang ◽  
Nathaniel Woody ◽  
Christopher Keefer
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
High Throughput ◽  
In Silico ◽  
Shake Flask ◽  
Chromatographic Method ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
High Throughput Screens

Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as a distribution coefficient (LogD) – a miniaturized shake-flask method (SFLogD) and a chromatographic method (ELogD). The results from these two assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, the ability to predict the LogD value for virtual compounds is equally vital. Here we present an in-silico LogD model, applicable to all chemical spaces, based on the integration of the LogD data from both assays. We developed two approaches towards a single LogD model – a Rule-based and a Machine Learning approach. Ultimately, the Machine Learning LogD model was found to be superior to both internally developed and commercial LogD models.<br>

scholarly journals Multi-Omics Technologies Applied to Tuberculosis Drug Discovery

2020 ◽  
Vol 10 (13) ◽  
pp. 4629 ◽  
Author(s):  
Aaron Goff ◽  
Daire Cantillon ◽  
Leticia Muraro Wildner ◽  
Simon J Waddell
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Discovery ◽  
High Throughput ◽  
A Priori ◽  
Drug Action ◽  
Biological Molecules ◽  
Lead Compound ◽  
Omics Technologies ◽  
Lead Compounds ◽  
High Throughput Screens

Multi-omics strategies are indispensable tools in the search for new anti-tuberculosis drugs. Omics methodologies, where the ensemble of a class of biological molecules are measured and evaluated together, enable drug discovery programs to answer two fundamental questions. Firstly, in a discovery biology approach, to find new targets in druggable pathways for target-based investigation, advancing from target to lead compound. Secondly, in a discovery chemistry approach, to identify the mode of action of lead compounds derived from high-throughput screens, progressing from compound to target. The advantage of multi-omics methodologies in both of these settings is that omics approaches are unsupervised and unbiased to a priori hypotheses, making omics useful tools to confirm drug action, reveal new insights into compound activity, and discover new avenues for inquiry. This review summarizes the application of Mycobacterium tuberculosis omics technologies to the early stages of tuberculosis antimicrobial drug discovery.

scholarly journals A High-Throughput Dose-Response Cellular Thermal Shift Assay for Rapid Screening of Drug Target Engagement in Living Cells, Exemplified Using SMYD3 and IDO1

2017 ◽  
Vol 23 (1) ◽  
pp. 34-46 ◽  
Author(s):  
Dean E. McNulty ◽  
William G. Bonnette ◽  
Hongwei Qi ◽  
Liping Wang ◽  
Thau F. Ho ◽  
...  
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
Drug Target ◽  
Rank Order ◽  
Target Engagement ◽  
Suspension Cells ◽  
Persistent Problem ◽  
Thermal Shift Assay ◽  
Cellular Thermal Shift Assay ◽  
Thermal Shift

A persistent problem in early small-molecule drug discovery is the frequent lack of rank-order correlation between biochemical potencies derived from initial screens using purified proteins and the diminished potency and efficacy observed in subsequent disease-relevant cellular phenotypic assays. The introduction of the cellular thermal shift assay (CETSA) has bridged this gap by enabling assessment of drug target engagement directly in live cells based on ligand-induced changes in protein thermal stability. Initial success in applying CETSA across multiple drug target classes motivated our investigation into replacing the low-throughput, manually intensive Western blot readout with a quantitative, automated higher-throughput assay that would provide sufficient capacity to use CETSA as a primary hit qualification strategy. We introduce a high-throughput dose-response cellular thermal shift assay (HTDR-CETSA), a single-pot homogenous assay adapted for high-density microtiter plate format. The assay features titratable BacMam expression of full-length target proteins fused to the DiscoverX 42 amino acid ePL tag in HeLa suspension cells, facilitating enzyme fragment complementation–based chemiluminescent quantification of ligand-stabilized soluble protein. This simplified format can accommodate determination of full-dose CETSA curves for hundreds of individual compounds/analyst/day in replicates. HTDR-CETSA data generated for substrate site and alternate binding mode inhibitors of the histone-lysine N-methyltransferase SMYD3 in HeLa suspension cells demonstrate excellent correlation with rank-order potencies observed in cellular mechanistic assays and direct translation to target engagement of endogenous Smyd3 in cancer-relevant cell lines. We envision this workflow to be generically applicable to HTDR-CETSA screening spanning a wide variety of soluble intracellular protein target classes.

scholarly journals Interrogating Fragments Using a Protein Thermal Shift Assay

2013 ◽  
Vol 66 (12) ◽  
pp. 1502 ◽  
Author(s):  
Róisín M. McMahon ◽  
Martin J. Scanlon ◽  
Jennifer L. Martin
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Targets ◽  
Protein Protein Interaction ◽  
Small Molecule Libraries ◽  
High Throughput Screens ◽  
Promising Ligand ◽  
Inexpensive Technique ◽  
Thermal Shift ◽  
Fragment Based Drug Discovery

Protein thermal shift is a relatively rapid and inexpensive technique for the identification of low molecular weight compound interactions with protein targets. An increase in the melting temperature of the target protein in the presence of a test ligand is indicative of a promising ligand–protein interaction. Due to its simplicity, protein thermal shift is an attractive method for screening libraries and validating hits in drug discovery programs. The methodology has been used successfully in high throughput screens of small molecule libraries, and its application has been extended to report on protein–drug-like-fragment interactions. Here, we review how protein thermal shift has been employed recently in fragment-based drug discovery (FBDD) efforts, and highlight its application to protein–protein interaction targets. Multiple validation of fragment hits by independent means is paramount to ensure efficient and economical progress in a FBDD campaign. We discuss the applicability of thermal shift assays in this light, and discuss more generally what one does when orthogonal approaches disagree.

scholarly journals AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High Throughput Screens

2021 ◽  
Author(s):  
Kate Stafford ◽  
Brandon M. Anderson ◽  
Jon Sorenson ◽  
Henry van den Bedem
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Conformational Ensemble ◽  
Binding Modes ◽  
Convolutional Network ◽  
In Silico Docking ◽  
High Throughput Screens ◽  
Affinity Score

Structure-based, virtual High Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking to generate an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo, ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here we introduce AtomNet PoseRanker, a graph convolutional network trained to identify, and re-rank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and non-cognate binding modes corresponding to distinct receptor conformations. AtomNet PoseRanker significantly enriched pose quality in docking to cognate and non-cognate receptors of the PDBbind v2019 dataset. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns, and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

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