SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks

Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of pro...
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Autor*in:	Adelfio, Alessandro [verfasserIn] Volpato, Viola [verfasserIn] Pollastri, Gianluca [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Generalize Correlation Protein Subcellular Localization Protein Function Prediction Relative Solvent Accessibility Good Template

Übergeordnetes Werk:	Enthalten in: SpringerPlus - London : Biomed Central, 2012, 2(2013), 1 vom: 03. Okt.
Übergeordnetes Werk:	volume:2 ; year:2013 ; number:1 ; day:03 ; month:10

Links:	Volltext

DOI / URN:	10.1186/2193-1801-2-502

Katalog-ID:	SPR032752520

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520			\|a Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission.
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allfields	10.1186/2193-1801-2-502 doi (DE-627)SPR032752520 (SPR)2193-1801-2-502-e DE-627 ger DE-627 rakwb eng 600 ASE Adelfio, Alessandro verfasserin aut SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission. Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213 Volpato, Viola verfasserin aut Pollastri, Gianluca verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 2(2013), 1 vom: 03. Okt. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:2 year:2013 number:1 day:03 month:10 https://dx.doi.org/10.1186/2193-1801-2-502 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 2 2013 1 03 10
spelling	10.1186/2193-1801-2-502 doi (DE-627)SPR032752520 (SPR)2193-1801-2-502-e DE-627 ger DE-627 rakwb eng 600 ASE Adelfio, Alessandro verfasserin aut SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission. Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213 Volpato, Viola verfasserin aut Pollastri, Gianluca verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 2(2013), 1 vom: 03. Okt. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:2 year:2013 number:1 day:03 month:10 https://dx.doi.org/10.1186/2193-1801-2-502 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 2 2013 1 03 10
allfields_unstemmed	10.1186/2193-1801-2-502 doi (DE-627)SPR032752520 (SPR)2193-1801-2-502-e DE-627 ger DE-627 rakwb eng 600 ASE Adelfio, Alessandro verfasserin aut SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission. Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213 Volpato, Viola verfasserin aut Pollastri, Gianluca verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 2(2013), 1 vom: 03. Okt. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:2 year:2013 number:1 day:03 month:10 https://dx.doi.org/10.1186/2193-1801-2-502 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 2 2013 1 03 10
allfieldsGer	10.1186/2193-1801-2-502 doi (DE-627)SPR032752520 (SPR)2193-1801-2-502-e DE-627 ger DE-627 rakwb eng 600 ASE Adelfio, Alessandro verfasserin aut SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission. Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213 Volpato, Viola verfasserin aut Pollastri, Gianluca verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 2(2013), 1 vom: 03. Okt. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:2 year:2013 number:1 day:03 month:10 https://dx.doi.org/10.1186/2193-1801-2-502 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 2 2013 1 03 10
allfieldsSound	10.1186/2193-1801-2-502 doi (DE-627)SPR032752520 (SPR)2193-1801-2-502-e DE-627 ger DE-627 rakwb eng 600 ASE Adelfio, Alessandro verfasserin aut SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission. Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213 Volpato, Viola verfasserin aut Pollastri, Gianluca verfasserin aut Enthalten in SpringerPlus London : Biomed Central, 2012 2(2013), 1 vom: 03. Okt. (DE-627)718615298 (DE-600)2661116-8 2193-1801 nnns volume:2 year:2013 number:1 day:03 month:10 https://dx.doi.org/10.1186/2193-1801-2-502 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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 2 2013 1 03 10
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author	Adelfio, Alessandro
spellingShingle	Adelfio, Alessandro ddc 600 misc Generalize Correlation misc Protein Subcellular Localization misc Protein Function Prediction misc Relative Solvent Accessibility misc Good Template SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks
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topic_title	600 ASE SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks Generalize Correlation (dpeaa)DE-He213 Protein Subcellular Localization (dpeaa)DE-He213 Protein Function Prediction (dpeaa)DE-He213 Relative Solvent Accessibility (dpeaa)DE-He213 Good Template (dpeaa)DE-He213
topic	ddc 600 misc Generalize Correlation misc Protein Subcellular Localization misc Protein Function Prediction misc Relative Solvent Accessibility misc Good Template
topic_unstemmed	ddc 600 misc Generalize Correlation misc Protein Subcellular Localization misc Protein Function Prediction misc Relative Solvent Accessibility misc Good Template
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title	SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks
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title_full	SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks
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author_browse	Adelfio, Alessandro Volpato, Viola Pollastri, Gianluca
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title_sort	sclpredt: ab initio and homology-based prediction of subcellular localization by n-to-1 neural networks
title_auth	SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks
abstract	Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission.
abstractGer	Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission.
abstract_unstemmed	Abstract The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure. SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission.
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title_short	SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks
url	https://dx.doi.org/10.1186/2193-1801-2-502
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author2	Volpato, Viola Pollastri, Gianluca
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up_date	2024-07-03T14:35:37.223Z
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SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks

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