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MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles

Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of...

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Autores principales: Mizianty, Marcin J., Peng, Zhenling, Kurgan, Lukasz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424793/
https://www.ncbi.nlm.nih.gov/pubmed/28516009
http://dx.doi.org/10.4161/idp.24428
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author Mizianty, Marcin J.
Peng, Zhenling
Kurgan, Lukasz
author_facet Mizianty, Marcin J.
Peng, Zhenling
Kurgan, Lukasz
author_sort Mizianty, Marcin J.
collection PubMed
description Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/.
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spelling pubmed-54247932017-05-17 MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles Mizianty, Marcin J. Peng, Zhenling Kurgan, Lukasz Intrinsically Disord Proteins Technical Paper Intrinsically disordered proteins (IDPs) are either entirely disordered or contain disordered regions in their native state. IDPs were found to be abundant in complex organisms and implicated in numerous cellular processes. Experimental annotation of disorder lags behind the rapidly growing sizes of the protein databases, and thus computational methods are used to close this gap and to investigate the disorder. MFDp2 is a novel content-rich and user-friendly web server for sequence-based prediction of protein disorder that builds upon our residue-level disorder predictor MFDp and chain-level disorder content predictor DisCon. It applies novel post-processing filters and uses sequence alignment to improve predictive quality. Using a new benchmark data set, which has reduced sequence identity to corresponding training data sets, MFDp2 is shown to provide competitive predictive quality when compared with MFDp and a comprehensive set of 13 other state-of-the-art predictors, including publicly available versions of the top predictors from CASP9. Our server obtains the highest Mathews Correlation Coefficient (MCC) and the second best Area Under the receiver operating characteristic Curve (AUC). In addition to the disorder predictions, our server also outputs well-described sequence-derived information that allows profiling the predicted disorder. We conveniently visualize sequence conservation, predicted secondary structure, relative solvent accessibility and alignments to chains with annotated disorder. We allow predictions for multiple proteins at the same time and each prediction can be downloaded as text-based (parsable) file. The web server, which includes help pages and tutorial, is freely available at biomine.ece.ualberta.ca/MFDp2/. Taylor & Francis 2013-04-01 /pmc/articles/PMC5424793/ /pubmed/28516009 http://dx.doi.org/10.4161/idp.24428 Text en Copyright © 2013 Landes Bioscience http://creativecommons.org/licenses/by-nc/3.0/ This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited.
spellingShingle Technical Paper
Mizianty, Marcin J.
Peng, Zhenling
Kurgan, Lukasz
MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title_full MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title_fullStr MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title_full_unstemmed MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title_short MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
title_sort mfdp2: accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles
topic Technical Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424793/
https://www.ncbi.nlm.nih.gov/pubmed/28516009
http://dx.doi.org/10.4161/idp.24428
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