DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks

Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of r...
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Autor*in:	Cheng, Jianlin [verfasserIn] Sweredoski, Michael J. Baldi, Pierre

Format:	Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	protein structure prediction domain recursive neural networks

Anmerkung:	© Springer Science + Business Media, LLC 2006

Übergeordnetes Werk:	Enthalten in: Data mining and knowledge discovery - Springer US, 1997, 13(2006), 1 vom: 11. Mai, Seite 1-10
Übergeordnetes Werk:	volume:13 ; year:2006 ; number:1 ; day:11 ; month:05 ; pages:1-10

Links:	Volltext

DOI / URN:	10.1007/s10618-005-0023-5

Katalog-ID:	OLC2027057471

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520			\|a Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html.
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allfields	10.1007/s10618-005-0023-5 doi (DE-627)OLC2027057471 (DE-He213)s10618-005-0023-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Cheng, Jianlin verfasserin aut DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, LLC 2006 Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. protein structure prediction domain recursive neural networks Sweredoski, Michael J. aut Baldi, Pierre aut Enthalten in Data mining and knowledge discovery Springer US, 1997 13(2006), 1 vom: 11. Mai, Seite 1-10 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:13 year:2006 number:1 day:11 month:05 pages:1-10 https://doi.org/10.1007/s10618-005-0023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4305 AR 13 2006 1 11 05 1-10
spelling	10.1007/s10618-005-0023-5 doi (DE-627)OLC2027057471 (DE-He213)s10618-005-0023-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Cheng, Jianlin verfasserin aut DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, LLC 2006 Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. protein structure prediction domain recursive neural networks Sweredoski, Michael J. aut Baldi, Pierre aut Enthalten in Data mining and knowledge discovery Springer US, 1997 13(2006), 1 vom: 11. Mai, Seite 1-10 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:13 year:2006 number:1 day:11 month:05 pages:1-10 https://doi.org/10.1007/s10618-005-0023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4305 AR 13 2006 1 11 05 1-10
allfields_unstemmed	10.1007/s10618-005-0023-5 doi (DE-627)OLC2027057471 (DE-He213)s10618-005-0023-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Cheng, Jianlin verfasserin aut DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, LLC 2006 Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. protein structure prediction domain recursive neural networks Sweredoski, Michael J. aut Baldi, Pierre aut Enthalten in Data mining and knowledge discovery Springer US, 1997 13(2006), 1 vom: 11. Mai, Seite 1-10 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:13 year:2006 number:1 day:11 month:05 pages:1-10 https://doi.org/10.1007/s10618-005-0023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4305 AR 13 2006 1 11 05 1-10
allfieldsGer	10.1007/s10618-005-0023-5 doi (DE-627)OLC2027057471 (DE-He213)s10618-005-0023-5-p DE-627 ger DE-627 rakwb eng 400 070 004 VZ 24,1 ssgn LING DE-30 fid Cheng, Jianlin verfasserin aut DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science + Business Media, LLC 2006 Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. protein structure prediction domain recursive neural networks Sweredoski, Michael J. aut Baldi, Pierre aut Enthalten in Data mining and knowledge discovery Springer US, 1997 13(2006), 1 vom: 11. Mai, Seite 1-10 (DE-627)230491774 (DE-600)1386325-3 (DE-576)067290434 1384-5810 nnns volume:13 year:2006 number:1 day:11 month:05 pages:1-10 https://doi.org/10.1007/s10618-005-0023-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI SSG-OPC-ANG GBV_ILN_40 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2005 GBV_ILN_4305 AR 13 2006 1 11 05 1-10
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title_sort	dompro: protein domain prediction using profiles, secondary structure, relative solvent accessibility, and recursive neural networks
title_auth	DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks
abstract	Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. © Springer Science + Business Media, LLC 2006
abstractGer	Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. © Springer Science + Business Media, LLC 2006
abstract_unstemmed	Abstract Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html. © Springer Science + Business Media, LLC 2006
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container_issue	1
title_short	DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks
url	https://doi.org/10.1007/s10618-005-0023-5
remote_bool	false
author2	Sweredoski, Michael J. Baldi, Pierre
author2Str	Sweredoski, Michael J. Baldi, Pierre
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doi_str	10.1007/s10618-005-0023-5
up_date	2024-07-03T13:36:33.914Z
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