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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...

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Autores principales: Suo, Sheng-Bao, Qiu, Jian-Ding, Shi, Shao-Ping, Sun, Xing-Yu, Huang, Shu-Yun, Chen, Xiang, Liang, Ru-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
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.
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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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