iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.

Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-gen...

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Autores principales:	Yan Xu, Jun Ding, Ling-Yun Wu, Kuo-Chen Chou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5befbbfbeb4f43929192a4d1685c0f33
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spelling	oai:doaj.org-article:5befbbfbeb4f43929192a4d1685c0f332021-11-18T07:58:18ZiSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.1932-620310.1371/journal.pone.0055844https://doaj.org/article/5befbbfbeb4f43929192a4d1685c0f332013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23409062/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.Yan XuJun DingLing-Yun WuKuo-Chen ChouPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 2, p e55844 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yan Xu Jun Ding Ling-Yun Wu Kuo-Chen Chou iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
description	Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had [Formula: see text] pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
format	article
author	Yan Xu Jun Ding Ling-Yun Wu Kuo-Chen Chou
author_facet	Yan Xu Jun Ding Ling-Yun Wu Kuo-Chen Chou
author_sort	Yan Xu
title	iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
title_short	iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
title_full	iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
title_fullStr	iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
title_full_unstemmed	iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
title_sort	isno-pseaac: predict cysteine s-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/5befbbfbeb4f43929192a4d1685c0f33
work_keys_str_mv	AT yanxu isnopseaacpredictcysteinesnitrosylationsitesinproteinsbyincorporatingpositionspecificaminoacidpropensityintopseudoaminoacidcomposition AT junding isnopseaacpredictcysteinesnitrosylationsitesinproteinsbyincorporatingpositionspecificaminoacidpropensityintopseudoaminoacidcomposition AT lingyunwu isnopseaacpredictcysteinesnitrosylationsitesinproteinsbyincorporatingpositionspecificaminoacidpropensityintopseudoaminoacidcomposition AT kuochenchou isnopseaacpredictcysteinesnitrosylationsitesinproteinsbyincorporatingpositionspecificaminoacidpropensityintopseudoaminoacidcomposition
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iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.

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