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PreDisorder: ab initio sequence-based prediction of protein disordered regions
BACKGROUND: Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure predict...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087350/ https://www.ncbi.nlm.nih.gov/pubmed/20025768 http://dx.doi.org/10.1186/1471-2105-10-436 |
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author | Deng, Xin Eickholt, Jesse Cheng, Jianlin |
author_facet | Deng, Xin Eickholt, Jesse Cheng, Jianlin |
author_sort | Deng, Xin |
collection | PubMed |
description | BACKGROUND: Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation. RESULTS: This paper presents our protein disorder prediction server, PreDisorder. It is based on our ab initio prediction method (MULTICOM-CMFR) which, along with our meta (or consensus) prediction method (MULTICOM), was recently ranked among the top disorder predictors in the eighth edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP8). We systematically benchmarked PreDisorder along with 26 other protein disorder predictors on the CASP8 data set and assessed its accuracy using a number of measures. The results show that it compared favourably with other ab initio methods and its performance is comparable to that of the best meta and clustering methods. CONCLUSION: PreDisorder is a fast and reliable server which can be used to predict protein disordered regions on genomic scale. It is available at http://casp.rnet.missouri.edu/predisorder.html. |
format | Text |
id | pubmed-3087350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30873502011-05-05 PreDisorder: ab initio sequence-based prediction of protein disordered regions Deng, Xin Eickholt, Jesse Cheng, Jianlin BMC Bioinformatics Software BACKGROUND: Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation. RESULTS: This paper presents our protein disorder prediction server, PreDisorder. It is based on our ab initio prediction method (MULTICOM-CMFR) which, along with our meta (or consensus) prediction method (MULTICOM), was recently ranked among the top disorder predictors in the eighth edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP8). We systematically benchmarked PreDisorder along with 26 other protein disorder predictors on the CASP8 data set and assessed its accuracy using a number of measures. The results show that it compared favourably with other ab initio methods and its performance is comparable to that of the best meta and clustering methods. CONCLUSION: PreDisorder is a fast and reliable server which can be used to predict protein disordered regions on genomic scale. It is available at http://casp.rnet.missouri.edu/predisorder.html. BioMed Central 2009-12-21 /pmc/articles/PMC3087350/ /pubmed/20025768 http://dx.doi.org/10.1186/1471-2105-10-436 Text en Copyright ©2009 Deng et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Deng, Xin Eickholt, Jesse Cheng, Jianlin PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title | PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title_full | PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title_fullStr | PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title_full_unstemmed | PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title_short | PreDisorder: ab initio sequence-based prediction of protein disordered regions |
title_sort | predisorder: ab initio sequence-based prediction of protein disordered regions |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087350/ https://www.ncbi.nlm.nih.gov/pubmed/20025768 http://dx.doi.org/10.1186/1471-2105-10-436 |
work_keys_str_mv | AT dengxin predisorderabinitiosequencebasedpredictionofproteindisorderedregions AT eickholtjesse predisorderabinitiosequencebasedpredictionofproteindisorderedregions AT chengjianlin predisorderabinitiosequencebasedpredictionofproteindisorderedregions |