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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 t...

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Autores principales: Samuel, Errol L. G., Holmes, Secondra L., Young, Damian W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666039/
https://www.ncbi.nlm.nih.gov/pubmed/34895330
http://dx.doi.org/10.1186/s13321-021-00577-1
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author Samuel, Errol L. G.
Holmes, Secondra L.
Young, Damian W.
author_facet Samuel, Errol L. G.
Holmes, Secondra L.
Young, Damian W.
author_sort Samuel, Errol L. G.
collection PubMed
description 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. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00577-1.
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spelling pubmed-86660392021-12-13 Processing binding data using an open-source workflow Samuel, Errol L. G. Holmes, Secondra L. Young, Damian W. J Cheminform Educational 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. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-021-00577-1. Springer International Publishing 2021-12-11 /pmc/articles/PMC8666039/ /pubmed/34895330 http://dx.doi.org/10.1186/s13321-021-00577-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Educational
Samuel, Errol L. G.
Holmes, Secondra L.
Young, Damian W.
Processing binding data using an open-source workflow
title Processing binding data using an open-source workflow
title_full Processing binding data using an open-source workflow
title_fullStr Processing binding data using an open-source workflow
title_full_unstemmed Processing binding data using an open-source workflow
title_short Processing binding data using an open-source workflow
title_sort processing binding data using an open-source workflow
topic Educational
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8666039/
https://www.ncbi.nlm.nih.gov/pubmed/34895330
http://dx.doi.org/10.1186/s13321-021-00577-1
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