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ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors
MOTIVATION: ADP-ribosylation is a post-translational modification (PTM) implicated in several crucial cellular processes, ranging from regulation of DNA repair and chromatin structure to cell metabolism and stress responses. To date, a complete understanding of ADP-ribosylation targets and their mod...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061869/ https://www.ncbi.nlm.nih.gov/pubmed/29554239 http://dx.doi.org/10.1093/bioinformatics/bty159 |
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author | Lo Monte, Matteo Manelfi, Candida Gemei, Marica Corda, Daniela Beccari, Andrea Rosario |
author_facet | Lo Monte, Matteo Manelfi, Candida Gemei, Marica Corda, Daniela Beccari, Andrea Rosario |
author_sort | Lo Monte, Matteo |
collection | PubMed |
description | MOTIVATION: ADP-ribosylation is a post-translational modification (PTM) implicated in several crucial cellular processes, ranging from regulation of DNA repair and chromatin structure to cell metabolism and stress responses. To date, a complete understanding of ADP-ribosylation targets and their modification sites in different tissues and disease states is still lacking. Identification of ADP-ribosylation sites is required to discern the molecular mechanisms regulated by this modification. This motivated us to develop a computational tool for the prediction of ADP-ribosylated sites. RESULTS: Here, we present ADPredict, the first dedicated computational tool for the prediction of ADP-ribosylated aspartic and glutamic acids. This predictive algorithm is based on (i) physicochemical properties, (ii) in-house designed secondary structure-related descriptors and (iii) three-dimensional features of a set of human ADP-ribosylated proteins that have been reported in the literature. ADPredict was developed using principal component analysis and machine learning techniques; its performance was evaluated both internally via intensive bootstrapping and in predicting two external experimental datasets. It outperformed the only other available ADP-ribosylation prediction tool, ModPred. Moreover, a novel secondary structure descriptor, HM-ratio, was introduced and successfully contributed to the model development, thus representing a promising tool for bioinformatics studies, such as PTM prediction. AVAILABILITY AND IMPLEMENTATION: ADPredict is freely available at www.ADPredict.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6061869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-60618692018-08-07 ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors Lo Monte, Matteo Manelfi, Candida Gemei, Marica Corda, Daniela Beccari, Andrea Rosario Bioinformatics Original Papers MOTIVATION: ADP-ribosylation is a post-translational modification (PTM) implicated in several crucial cellular processes, ranging from regulation of DNA repair and chromatin structure to cell metabolism and stress responses. To date, a complete understanding of ADP-ribosylation targets and their modification sites in different tissues and disease states is still lacking. Identification of ADP-ribosylation sites is required to discern the molecular mechanisms regulated by this modification. This motivated us to develop a computational tool for the prediction of ADP-ribosylated sites. RESULTS: Here, we present ADPredict, the first dedicated computational tool for the prediction of ADP-ribosylated aspartic and glutamic acids. This predictive algorithm is based on (i) physicochemical properties, (ii) in-house designed secondary structure-related descriptors and (iii) three-dimensional features of a set of human ADP-ribosylated proteins that have been reported in the literature. ADPredict was developed using principal component analysis and machine learning techniques; its performance was evaluated both internally via intensive bootstrapping and in predicting two external experimental datasets. It outperformed the only other available ADP-ribosylation prediction tool, ModPred. Moreover, a novel secondary structure descriptor, HM-ratio, was introduced and successfully contributed to the model development, thus representing a promising tool for bioinformatics studies, such as PTM prediction. AVAILABILITY AND IMPLEMENTATION: ADPredict is freely available at www.ADPredict.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-08-01 2018-03-15 /pmc/articles/PMC6061869/ /pubmed/29554239 http://dx.doi.org/10.1093/bioinformatics/bty159 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Lo Monte, Matteo Manelfi, Candida Gemei, Marica Corda, Daniela Beccari, Andrea Rosario ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title | ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title_full | ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title_fullStr | ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title_full_unstemmed | ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title_short | ADPredict: ADP-ribosylation site prediction based on physicochemical and structural descriptors |
title_sort | adpredict: adp-ribosylation site prediction based on physicochemical and structural descriptors |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061869/ https://www.ncbi.nlm.nih.gov/pubmed/29554239 http://dx.doi.org/10.1093/bioinformatics/bty159 |
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