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Critical assessment of coiled-coil predictions based on protein structure data
Coiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil pre...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203680/ https://www.ncbi.nlm.nih.gov/pubmed/34127723 http://dx.doi.org/10.1038/s41598-021-91886-w |
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author | Simm, Dominic Hatje, Klas Waack, Stephan Kollmar, Martin |
author_facet | Simm, Dominic Hatje, Klas Waack, Stephan Kollmar, Martin |
author_sort | Simm, Dominic |
collection | PubMed |
description | Coiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence. |
format | Online Article Text |
id | pubmed-8203680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82036802021-06-15 Critical assessment of coiled-coil predictions based on protein structure data Simm, Dominic Hatje, Klas Waack, Stephan Kollmar, Martin Sci Rep Article Coiled-coil regions were among the first protein motifs described structurally and theoretically. The simplicity of the motif promises that coiled-coil regions can be detected with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Bank, down to each amino acid and its secondary structure. Apart from the 30-fold difference in minimum and maximum number of coiled coils predicted the tools strongly vary in where they predict coiled-coil regions. Accordingly, there is a high number of false predictions and missed, true coiled-coil regions. The evaluation of the binary classification metrics in comparison with naïve coin-flip models and the calculation of the Matthews correlation coefficient, the most reliable performance metric for imbalanced data sets, suggests that the tested tools’ performance is close to random. This implicates that the tools’ predictions have only limited informative value. Coiled-coil predictions are often used to interpret biochemical data and are part of in-silico functional genome annotation. Our results indicate that these predictions should be treated very cautiously and need to be supported and validated by experimental evidence. Nature Publishing Group UK 2021-06-14 /pmc/articles/PMC8203680/ /pubmed/34127723 http://dx.doi.org/10.1038/s41598-021-91886-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Simm, Dominic Hatje, Klas Waack, Stephan Kollmar, Martin Critical assessment of coiled-coil predictions based on protein structure data |
title | Critical assessment of coiled-coil predictions based on protein structure data |
title_full | Critical assessment of coiled-coil predictions based on protein structure data |
title_fullStr | Critical assessment of coiled-coil predictions based on protein structure data |
title_full_unstemmed | Critical assessment of coiled-coil predictions based on protein structure data |
title_short | Critical assessment of coiled-coil predictions based on protein structure data |
title_sort | critical assessment of coiled-coil predictions based on protein structure data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8203680/ https://www.ncbi.nlm.nih.gov/pubmed/34127723 http://dx.doi.org/10.1038/s41598-021-91886-w |
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