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CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability
The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252748/ https://www.ncbi.nlm.nih.gov/pubmed/35580052 http://dx.doi.org/10.1093/nar/gkac378 |
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author | Kunka, Antonin Lacko, David Stourac, Jan Damborsky, Jiri Prokop, Zbynek Mazurenko, Stanislav |
author_facet | Kunka, Antonin Lacko, David Stourac, Jan Damborsky, Jiri Prokop, Zbynek Mazurenko, Stanislav |
author_sort | Kunka, Antonin |
collection | PubMed |
description | The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter. |
format | Online Article Text |
id | pubmed-9252748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92527482022-07-05 CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability Kunka, Antonin Lacko, David Stourac, Jan Damborsky, Jiri Prokop, Zbynek Mazurenko, Stanislav Nucleic Acids Res Web Server Issue The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter. Oxford University Press 2022-05-17 /pmc/articles/PMC9252748/ /pubmed/35580052 http://dx.doi.org/10.1093/nar/gkac378 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Kunka, Antonin Lacko, David Stourac, Jan Damborsky, Jiri Prokop, Zbynek Mazurenko, Stanislav CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title | CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title_full | CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title_fullStr | CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title_full_unstemmed | CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title_short | CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability |
title_sort | calfitter 2.0: leveraging the power of singular value decomposition to analyse protein thermostability |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252748/ https://www.ncbi.nlm.nih.gov/pubmed/35580052 http://dx.doi.org/10.1093/nar/gkac378 |
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