PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins
Prediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal prote...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2012.00194/full |
| _version_ | 1811230356743913472 |
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| author | Sigrun eReumann Daniela eBuchwald Thomas eLingner |
| author_facet | Sigrun eReumann Daniela eBuchwald Thomas eLingner |
| author_sort | Sigrun eReumann |
| collection | DOAJ |
| description | Prediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e. novel PTS1 tripeptides and tripeptide residues. Here we describe the first plant-specific web server PredPlantPTS1 for the prediction of plant PTS1 proteins using the above-mentioned underlying models. The server allows the submission of protein sequences from diverse spermatophytes and also performs well for mosses and algae. The easy-to-use web interface provides detailed output in terms of (i) the peroxisomal targeting probability of the given sequence, (ii) information whether a particular non-canonical PTS1 tripeptide has already been experimentally verified, and (iii) the prediction scores for the single C-terminal 14 amino acid residues. The latter allows identification of predicted residues that inhibit peroxisome targeting and which can be optimized using site-directed mutagenesis to raise the peroxisome targeting efficiency. The prediction server will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants. PredPlantPTS1 is freely accessible at ppp.gobics.de. |
| first_indexed | 2024-04-12T10:29:10Z |
| format | Article |
| id | doaj.art-40f22c4a090241e4b55c7de7a487d8f4 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T10:29:10Z |
| publishDate | 2012-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-40f22c4a090241e4b55c7de7a487d8f42022-12-22T03:36:54ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2012-08-01310.3389/fpls.2012.0019430102PredPlantPTS1: a web server for the prediction of plant peroxisomal proteinsSigrun eReumann0Daniela eBuchwald1Thomas eLingner2University of StavangerUniversity of GöttingenUniversity of GöttingenPrediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e. novel PTS1 tripeptides and tripeptide residues. Here we describe the first plant-specific web server PredPlantPTS1 for the prediction of plant PTS1 proteins using the above-mentioned underlying models. The server allows the submission of protein sequences from diverse spermatophytes and also performs well for mosses and algae. The easy-to-use web interface provides detailed output in terms of (i) the peroxisomal targeting probability of the given sequence, (ii) information whether a particular non-canonical PTS1 tripeptide has already been experimentally verified, and (iii) the prediction scores for the single C-terminal 14 amino acid residues. The latter allows identification of predicted residues that inhibit peroxisome targeting and which can be optimized using site-directed mutagenesis to raise the peroxisome targeting efficiency. The prediction server will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants. PredPlantPTS1 is freely accessible at ppp.gobics.de.http://journal.frontiersin.org/Journal/10.3389/fpls.2012.00194/fullArabidopsismachine learningperoxisomeSubcellular localizationPTS1 |
| spellingShingle | Sigrun eReumann Daniela eBuchwald Thomas eLingner PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins Frontiers in Plant Science Arabidopsis machine learning peroxisome Subcellular localization PTS1 |
| title | PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins |
| title_full | PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins |
| title_fullStr | PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins |
| title_full_unstemmed | PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins |
| title_short | PredPlantPTS1: a web server for the prediction of plant peroxisomal proteins |
| title_sort | predplantpts1 a web server for the prediction of plant peroxisomal proteins |
| topic | Arabidopsis machine learning peroxisome Subcellular localization PTS1 |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2012.00194/full |
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