PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.

Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. C...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jue Wang [verfasserIn] Yejun Wang [verfasserIn] Caiji Gao [verfasserIn] Liwen Jiang [verfasserIn] Dianjing Guo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 12(2017), 1, p e0168912
Übergeordnetes Werk:	volume:12 ; year:2017 ; number:1, p e0168912

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1371/journal.pone.0168912

Katalog-ID:	DOAJ046852557

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author_variant	j w jw y w yw c g cg l j lj d g dg
matchkey_str	article:19326203:2017----::praopttoamdlopatt1yeeoioa
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publishDate	2017
allfields	10.1371/journal.pone.0168912 doi (DE-627)DOAJ046852557 (DE-599)DOAJ85807332b2724bd09f2c528062cbc348 DE-627 ger DE-627 rakwb eng Jue Wang verfasserin aut PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction. 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting. Medicine R Science Q Yejun Wang verfasserin aut Caiji Gao verfasserin aut Liwen Jiang verfasserin aut Dianjing Guo verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 12(2017), 1, p e0168912 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:12 year:2017 number:1, p e0168912 https://doi.org/10.1371/journal.pone.0168912 kostenfrei https://doaj.org/article/85807332b2724bd09f2c528062cbc348 kostenfrei http://europepmc.org/articles/PMC5207514?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2017 1, p e0168912
spelling	10.1371/journal.pone.0168912 doi (DE-627)DOAJ046852557 (DE-599)DOAJ85807332b2724bd09f2c528062cbc348 DE-627 ger DE-627 rakwb eng Jue Wang verfasserin aut PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction. 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting. Medicine R Science Q Yejun Wang verfasserin aut Caiji Gao verfasserin aut Liwen Jiang verfasserin aut Dianjing Guo verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 12(2017), 1, p e0168912 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:12 year:2017 number:1, p e0168912 https://doi.org/10.1371/journal.pone.0168912 kostenfrei https://doaj.org/article/85807332b2724bd09f2c528062cbc348 kostenfrei http://europepmc.org/articles/PMC5207514?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2017 1, p e0168912
allfields_unstemmed	10.1371/journal.pone.0168912 doi (DE-627)DOAJ046852557 (DE-599)DOAJ85807332b2724bd09f2c528062cbc348 DE-627 ger DE-627 rakwb eng Jue Wang verfasserin aut PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction. 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting. Medicine R Science Q Yejun Wang verfasserin aut Caiji Gao verfasserin aut Liwen Jiang verfasserin aut Dianjing Guo verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 12(2017), 1, p e0168912 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:12 year:2017 number:1, p e0168912 https://doi.org/10.1371/journal.pone.0168912 kostenfrei https://doaj.org/article/85807332b2724bd09f2c528062cbc348 kostenfrei http://europepmc.org/articles/PMC5207514?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2017 1, p e0168912
allfieldsGer	10.1371/journal.pone.0168912 doi (DE-627)DOAJ046852557 (DE-599)DOAJ85807332b2724bd09f2c528062cbc348 DE-627 ger DE-627 rakwb eng Jue Wang verfasserin aut PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction. 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting. Medicine R Science Q Yejun Wang verfasserin aut Caiji Gao verfasserin aut Liwen Jiang verfasserin aut Dianjing Guo verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 12(2017), 1, p e0168912 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:12 year:2017 number:1, p e0168912 https://doi.org/10.1371/journal.pone.0168912 kostenfrei https://doaj.org/article/85807332b2724bd09f2c528062cbc348 kostenfrei http://europepmc.org/articles/PMC5207514?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2017 1, p e0168912
allfieldsSound	10.1371/journal.pone.0168912 doi (DE-627)DOAJ046852557 (DE-599)DOAJ85807332b2724bd09f2c528062cbc348 DE-627 ger DE-627 rakwb eng Jue Wang verfasserin aut PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction. 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting. Medicine R Science Q Yejun Wang verfasserin aut Caiji Gao verfasserin aut Liwen Jiang verfasserin aut Dianjing Guo verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 12(2017), 1, p e0168912 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:12 year:2017 number:1, p e0168912 https://doi.org/10.1371/journal.pone.0168912 kostenfrei https://doaj.org/article/85807332b2724bd09f2c528062cbc348 kostenfrei http://europepmc.org/articles/PMC5207514?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2017 1, p e0168912
language	English
source	In PLoS ONE 12(2017), 1, p e0168912 volume:12 year:2017 number:1, p e0168912
sourceStr	In PLoS ONE 12(2017), 1, p e0168912 volume:12 year:2017 number:1, p e0168912
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Medicine R Science Q
isfreeaccess_bool	true
container_title	PLoS ONE
authorswithroles_txt_mv	Jue Wang @@aut@@ Yejun Wang @@aut@@ Caiji Gao @@aut@@ Liwen Jiang @@aut@@ Dianjing Guo @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	523574592
id	DOAJ046852557
language_de	englisch
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author	Jue Wang
spellingShingle	Jue Wang misc Medicine misc R misc Science misc Q PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.
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topic_title	PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction
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title	PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.
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title_sort	ppero, a computational model for plant pts1 type peroxisomal protein prediction
title_auth	PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.
abstract	Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting.
abstractGer	Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting.
abstract_unstemmed	Well-defined motifs often make it easy to investigate protein function and localization. In plants, peroxisomal proteins are guided to peroxisomes mainly by a conserved type 1 (PTS1) or type 2 (PTS2) targeting signal, and the PTS1 motif is commonly used for peroxisome targeting protein prediction. Currently computational prediction of peroxisome targeted PTS1-type proteins are mostly based on the 3 amino acids PTS1 motif and the adjacent sequence which is less than 14 amino acid residue in length. The potential contribution of the adjacent sequences beyond this short region has never been well investigated in plants. In this work, we develop a bi-profile Bayesian SVM method to extract and learn position-based amino acid features for both PTS1 motifs and their extended adjacent sequences in plants. Our proposed model outperformed other implementations with similar applications and achieved the highest accuracy of 93.6% and 92.6% for Arabidosis and other plant species respectively. A large scale analysis for Arabidopsis, Rice, Maize, Potato, Wheat, and Soybean proteome was conducted using the proposed model and a batch of candidate PTS1 proteins were predicted. The DNA segments corresponding to the C-terminal sequences of 9 selected candidates were cloned and transformed into Arabidopsis for experimental validation, and 5 of them demonstrated peroxisome targeting.
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title_short	PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.
url	https://doi.org/10.1371/journal.pone.0168912 https://doaj.org/article/85807332b2724bd09f2c528062cbc348 http://europepmc.org/articles/PMC5207514?pdf=render https://doaj.org/toc/1932-6203
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up_date	2024-07-03T22:53:19.302Z
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PPero, a Computational Model for Plant PTS1 Type Peroxisomal Protein Prediction.

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