Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p...

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Autores principales:	Tao Huang, Shen Niu, Zhongping Xu, Yun Huang, Xiangyin Kong, Yu-Dong Cai, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b0939d94abdd4b6782539087cdd4a629
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spelling	oai:doaj.org-article:b0939d94abdd4b6782539087cdd4a6292021-11-18T06:48:29ZPredicting transcriptional activity of multiple site p53 mutants based on hybrid properties.1932-620310.1371/journal.pone.0022940https://doaj.org/article/b0939d94abdd4b6782539087cdd4a6292011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21857971/?tool=EBIhttps://doaj.org/toc/1932-6203As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.Tao HuangShen NiuZhongping XuYun HuangXiangyin KongYu-Dong CaiKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 8, p e22940 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tao Huang Shen Niu Zhongping Xu Yun Huang Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
description	As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.
format	article
author	Tao Huang Shen Niu Zhongping Xu Yun Huang Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou
author_facet	Tao Huang Shen Niu Zhongping Xu Yun Huang Xiangyin Kong Yu-Dong Cai Kuo-Chen Chou
author_sort	Tao Huang
title	Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
title_short	Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
title_full	Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
title_fullStr	Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
title_full_unstemmed	Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
title_sort	predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/b0939d94abdd4b6782539087cdd4a629
work_keys_str_mv	AT taohuang predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT shenniu predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT zhongpingxu predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT yunhuang predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT xiangyinkong predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT yudongcai predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties AT kuochenchou predictingtranscriptionalactivityofmultiplesitep53mutantsbasedonhybridproperties
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Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

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