Critical assessment of coiled-coil predictions based on protein structure data

Abstract 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Dominic Simm, Klas Hatje, Stephan Waack, Martin Kollmar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/457f24aabd3c4d23a4754297cf759afa
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:457f24aabd3c4d23a4754297cf759afa
record_format	dspace
spelling	oai:doaj.org-article:457f24aabd3c4d23a4754297cf759afa2021-12-02T16:04:36ZCritical assessment of coiled-coil predictions based on protein structure data10.1038/s41598-021-91886-w2045-2322https://doaj.org/article/457f24aabd3c4d23a4754297cf759afa2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91886-whttps://doaj.org/toc/2045-2322Abstract 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.Dominic SimmKlas HatjeStephan WaackMartin KollmarNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dominic Simm Klas Hatje Stephan Waack Martin Kollmar Critical assessment of coiled-coil predictions based on protein structure data
description	Abstract 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	article
author	Dominic Simm Klas Hatje Stephan Waack Martin Kollmar
author_facet	Dominic Simm Klas Hatje Stephan Waack Martin Kollmar
author_sort	Dominic Simm
title	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_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_sort	critical assessment of coiled-coil predictions based on protein structure data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/457f24aabd3c4d23a4754297cf759afa
work_keys_str_mv	AT dominicsimm criticalassessmentofcoiledcoilpredictionsbasedonproteinstructuredata AT klashatje criticalassessmentofcoiledcoilpredictionsbasedonproteinstructuredata AT stephanwaack criticalassessmentofcoiledcoilpredictionsbasedonproteinstructuredata AT martinkollmar criticalassessmentofcoiledcoilpredictionsbasedonproteinstructuredata
_version_	1718385202955288576

Critical assessment of coiled-coil predictions based on protein structure data

Ejemplares similares