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flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions
Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We...
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/PMC8295265/ https://www.ncbi.nlm.nih.gov/pubmed/34290238 http://dx.doi.org/10.1038/s41467-021-24773-7 |
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author | Hu, Gang Katuwawala, Akila Wang, Kui Wu, Zhonghua Ghadermarzi, Sina Gao, Jianzhao Kurgan, Lukasz |
author_facet | Hu, Gang Katuwawala, Akila Wang, Kui Wu, Zhonghua Ghadermarzi, Sina Gao, Jianzhao Kurgan, Lukasz |
author_sort | Hu, Gang |
collection | PubMed |
description | Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/ |
format | Online Article Text |
id | pubmed-8295265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-82952652021-08-12 flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions Hu, Gang Katuwawala, Akila Wang, Kui Wu, Zhonghua Ghadermarzi, Sina Gao, Jianzhao Kurgan, Lukasz Nat Commun Article Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/ Nature Publishing Group UK 2021-07-21 /pmc/articles/PMC8295265/ /pubmed/34290238 http://dx.doi.org/10.1038/s41467-021-24773-7 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hu, Gang Katuwawala, Akila Wang, Kui Wu, Zhonghua Ghadermarzi, Sina Gao, Jianzhao Kurgan, Lukasz flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title | flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title_full | flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title_fullStr | flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title_full_unstemmed | flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title_short | flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions |
title_sort | fldpnn: accurate intrinsic disorder prediction with putative propensities of disorder functions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295265/ https://www.ncbi.nlm.nih.gov/pubmed/34290238 http://dx.doi.org/10.1038/s41467-021-24773-7 |
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