Processing binding data using an open-source workflow

The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and T m shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a ta...

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Bibliographic Details
Published in:Journal of cheminformatics Vol. 13; no. 1; pp. 99 - 11
Main Authors: Samuel, Errol L. G., Holmes, Secondra L., Young, Damian W.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 11.12.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Binders
Biomedical Data Analyses Facilitated by Open Cheminformatics Workflows
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computer Applications in Chemistry
Data analysis
Data processing
Differential scanning fluorimetry
Documentation and Information in Chemistry
Drug discovery
Education
Educational
Fluorimetry
Fragment-based drug discovery
Fragment-based ligand discovery
Freeware
Information management
KNIME
Ligands
Open data
Plates
Polymerase chain reaction
Proteins
Source code
Theoretical and Computational Chemistry
Thermal shift assay
Thermal stability
Workflow
Data analysis
Education
Workflows
Fragment-based drug discovery
Fragment-based ligand discovery
Data processing
Flow-based programming
KNIME
Differential scanning fluorimetry
Thermal shift assay
ISSN:1758-2946, 1758-2946
Online Access:Get full text
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Summary:The thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and T m shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows. Graphical Abstract
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-021-00577-1