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Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features
Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an init...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500252/ https://www.ncbi.nlm.nih.gov/pubmed/23173045 http://dx.doi.org/10.1371/journal.pone.0049108 |
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author | Suo, Sheng-Bao Qiu, Jian-Ding Shi, Shao-Ping Sun, Xing-Yu Huang, Shu-Yun Chen, Xiang Liang, Ru-Ping |
author_facet | Suo, Sheng-Bao Qiu, Jian-Ding Shi, Shao-Ping Sun, Xing-Yu Huang, Shu-Yun Chen, Xiang Liang, Ru-Ping |
author_sort | Suo, Sheng-Bao |
collection | PubMed |
description | Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation. |
format | Online Article Text |
id | pubmed-3500252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35002522012-11-21 Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features Suo, Sheng-Bao Qiu, Jian-Ding Shi, Shao-Ping Sun, Xing-Yu Huang, Shu-Yun Chen, Xiang Liang, Ru-Ping PLoS One Research Article Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation. Public Library of Science 2012-11-16 /pmc/articles/PMC3500252/ /pubmed/23173045 http://dx.doi.org/10.1371/journal.pone.0049108 Text en © 2012 Suo et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Suo, Sheng-Bao Qiu, Jian-Ding Shi, Shao-Ping Sun, Xing-Yu Huang, Shu-Yun Chen, Xiang Liang, Ru-Ping Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title | Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title_full | Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title_fullStr | Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title_full_unstemmed | Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title_short | Position-Specific Analysis and Prediction for Protein Lysine Acetylation Based on Multiple Features |
title_sort | position-specific analysis and prediction for protein lysine acetylation based on multiple features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500252/ https://www.ncbi.nlm.nih.gov/pubmed/23173045 http://dx.doi.org/10.1371/journal.pone.0049108 |
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