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CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this...

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Autores principales: Del Conte, Alessio, Bouhraoua, Adel, Mehdiabadi, Mahta, Clementel, Damiano, Monzon, Alexander Miguel, Tosatto, Silvio C E, Piovesan, Damiano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320102/
https://www.ncbi.nlm.nih.gov/pubmed/37246642
http://dx.doi.org/10.1093/nar/gkad430
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author Del Conte, Alessio
Bouhraoua, Adel
Mehdiabadi, Mahta
Clementel, Damiano
Monzon, Alexander Miguel
Tosatto, Silvio C E
Piovesan, Damiano
author_facet Del Conte, Alessio
Bouhraoua, Adel
Mehdiabadi, Mahta
Clementel, Damiano
Monzon, Alexander Miguel
Tosatto, Silvio C E
Piovesan, Damiano
author_sort Del Conte, Alessio
collection PubMed
description Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org.
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spelling pubmed-103201022023-07-06 CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins Del Conte, Alessio Bouhraoua, Adel Mehdiabadi, Mahta Clementel, Damiano Monzon, Alexander Miguel Tosatto, Silvio C E Piovesan, Damiano Nucleic Acids Res Web Server Issue Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org. Oxford University Press 2023-05-29 /pmc/articles/PMC10320102/ /pubmed/37246642 http://dx.doi.org/10.1093/nar/gkad430 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Del Conte, Alessio
Bouhraoua, Adel
Mehdiabadi, Mahta
Clementel, Damiano
Monzon, Alexander Miguel
Tosatto, Silvio C E
Piovesan, Damiano
CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title_full CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title_fullStr CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title_full_unstemmed CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title_short CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
title_sort caid prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320102/
https://www.ncbi.nlm.nih.gov/pubmed/37246642
http://dx.doi.org/10.1093/nar/gkad430
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