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Inflect: Optimizing Computational Workflows for Thermal Proteome Profiling Data Analysis
[Image: see text] The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug dis...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022325/ https://www.ncbi.nlm.nih.gov/pubmed/33660510 http://dx.doi.org/10.1021/acs.jproteome.0c00872 |
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author | McCracken, Neil A. Peck Justice, Sarah A. Wijeratne, Aruna B. Mosley, Amber L. |
author_facet | McCracken, Neil A. Peck Justice, Sarah A. Wijeratne, Aruna B. Mosley, Amber L. |
author_sort | McCracken, Neil A. |
collection | PubMed |
description | [Image: see text] The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (T(m)) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications. |
format | Online Article Text |
id | pubmed-8022325 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-80223252021-04-06 Inflect: Optimizing Computational Workflows for Thermal Proteome Profiling Data Analysis McCracken, Neil A. Peck Justice, Sarah A. Wijeratne, Aruna B. Mosley, Amber L. J Proteome Res [Image: see text] The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (T(m)) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications. American Chemical Society 2021-03-04 2021-04-02 /pmc/articles/PMC8022325/ /pubmed/33660510 http://dx.doi.org/10.1021/acs.jproteome.0c00872 Text en © 2021 The Authors. Published by American Chemical Society Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | McCracken, Neil A. Peck Justice, Sarah A. Wijeratne, Aruna B. Mosley, Amber L. Inflect: Optimizing Computational Workflows for Thermal Proteome Profiling Data Analysis |
title | Inflect: Optimizing
Computational Workflows for Thermal
Proteome Profiling Data Analysis |
title_full | Inflect: Optimizing
Computational Workflows for Thermal
Proteome Profiling Data Analysis |
title_fullStr | Inflect: Optimizing
Computational Workflows for Thermal
Proteome Profiling Data Analysis |
title_full_unstemmed | Inflect: Optimizing
Computational Workflows for Thermal
Proteome Profiling Data Analysis |
title_short | Inflect: Optimizing
Computational Workflows for Thermal
Proteome Profiling Data Analysis |
title_sort | inflect: optimizing
computational workflows for thermal
proteome profiling data analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022325/ https://www.ncbi.nlm.nih.gov/pubmed/33660510 http://dx.doi.org/10.1021/acs.jproteome.0c00872 |
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