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Related Experiment Video

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Processing binding data using an open-source workflow.

Errol L G Samuel1,2, Secondra L Holmes3,4, Damian W Young5,6,7

  • 1Center for Drug Discovery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA. errol.samuel@bcm.edu.

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|December 13, 2021
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Summary
This summary is machine-generated.

The thermal shift assay (TSA) identifies protein-ligand interactions. A new, code-free KNIME workflow streamlines data analysis for large TSA screens, saving time and resources.

Keywords:
Data analysisData processingDifferential scanning fluorimetryEducationFlow-based programmingFragment-based drug discoveryFragment-based ligand discoveryKNIMEThermal shift assayWorkflows

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Area of Science:

  • Biophysics
  • Structural Biology
  • Drug Discovery

Background:

  • The thermal shift assay (TSA), also known as differential scanning fluorimetry (DSF), is a popular biophysical method for detecting protein-ligand interactions.
  • TSA is valuable in fragment-based ligand discovery (FBLD) due to its ease of use, low protein consumption, and compatibility with real-time PCR instruments.
  • Analyzing large TSA screening datasets (96- or 384-well plates) can be time-consuming and complex, often requiring specialized software or programming skills.

Purpose of the Study:

  • To develop a streamlined, user-friendly data analysis workflow for thermal shift assays (TSA).
  • To address the challenges of processing large-scale TSA screening data, particularly from 384-well plates.
  • To provide a free, open-source, and code-free solution for TSA data analysis.

Main Methods:

  • Development of a data analysis workflow using the Konstanz Information Miner (KNIME) platform.
  • Implementation of a code-free approach to process TSA data from multiple plates.
  • Focus on streamlining the data processing timeline for high-throughput TSA screening.

Main Results:

  • The developed KNIME workflow significantly reduced the time and effort required for TSA data processing.
  • The workflow is code-free, making it accessible to researchers without programming expertise.
  • The analysis pipeline is adaptable to various instrument output files and experimental setups.

Conclusions:

  • The KNIME-based workflow offers an efficient and accessible solution for analyzing large-scale thermal shift assay data.
  • This open-source tool democratizes TSA data analysis, supporting fragment-based ligand discovery and other research areas.
  • The workflow's flexibility allows for customization, enhancing its utility across different laboratories and experimental designs.