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LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers

BACKGROUND: Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those...

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Autores principales: Hou, Ting, Zheng, Guangyong, Zhang, Pingyu, Jia, Jia, Li, Jing, Xie, Lu, Wei, Chaochun, Li, Yixue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930742/
https://www.ncbi.nlm.nih.gov/pubmed/24586884
http://dx.doi.org/10.1371/journal.pone.0089575
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author Hou, Ting
Zheng, Guangyong
Zhang, Pingyu
Jia, Jia
Li, Jing
Xie, Lu
Wei, Chaochun
Li, Yixue
author_facet Hou, Ting
Zheng, Guangyong
Zhang, Pingyu
Jia, Jia
Li, Jing
Xie, Lu
Wei, Chaochun
Li, Yixue
author_sort Hou, Ting
collection PubMed
description BACKGROUND: Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding. RESULT: In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets. CONCLUSION: LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.
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spelling pubmed-39307422014-02-25 LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers Hou, Ting Zheng, Guangyong Zhang, Pingyu Jia, Jia Li, Jing Xie, Lu Wei, Chaochun Li, Yixue PLoS One Research Article BACKGROUND: Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding. RESULT: In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets. CONCLUSION: LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/. Public Library of Science 2014-02-20 /pmc/articles/PMC3930742/ /pubmed/24586884 http://dx.doi.org/10.1371/journal.pone.0089575 Text en © 2014 Hou 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
Hou, Ting
Zheng, Guangyong
Zhang, Pingyu
Jia, Jia
Li, Jing
Xie, Lu
Wei, Chaochun
Li, Yixue
LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title_full LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title_fullStr LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title_full_unstemmed LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title_short LAceP: Lysine Acetylation Site Prediction Using Logistic Regression Classifiers
title_sort lacep: lysine acetylation site prediction using logistic regression classifiers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930742/
https://www.ncbi.nlm.nih.gov/pubmed/24586884
http://dx.doi.org/10.1371/journal.pone.0089575
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