PredβTM: A Novel β-Transmembrane Region Prediction Algorithm.
Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β-transmembrane region predictors are still limited in terms of prediction accuracy, especially in pr...
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145564&type=printable |
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| author | Amrita Roy Choudhury Marjana Novič |
| author_facet | Amrita Roy Choudhury Marjana Novič |
| author_sort | Amrita Roy Choudhury |
| collection | DOAJ |
| description | Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β-transmembrane region predictors are still limited in terms of prediction accuracy, especially in precision. Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. Position-specific amino acid preference data is incorporated in the final prediction. The predictor does not incorporate evolutionary profile information explicitly, but is based on sequence patterns generated implicitly by encoding the protein segments using amino acid adjacency matrix. With a benchmark set of 35 β-transmembrane proteins, PredβTM shows a sensitivity and precision of 83.71% and 72.98%, respectively. The segment overlap score is 82.19%. In comparison with other state-of-art methods, PredβTM provides a higher precision and segment overlap without compromising with sensitivity. Further, we applied PredβTM to analyze the β-barrel membrane proteins without defined transmembrane regions and the uncharacterized protein sequences in eight bacterial genomes and predict possible β-transmembrane proteins. PredβTM can be freely accessed on the web at http://transpred.ki.si/. |
| format | Article |
| id | doaj-art-bd9be8dbe1904b618aa993c616a7e6aa |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-bd9be8dbe1904b618aa993c616a7e6aa2025-08-20T02:15:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011012e014556410.1371/journal.pone.0145564PredβTM: A Novel β-Transmembrane Region Prediction Algorithm.Amrita Roy ChoudhuryMarjana NovičPredicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite significant efforts, currently available β-transmembrane region predictors are still limited in terms of prediction accuracy, especially in precision. Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. Position-specific amino acid preference data is incorporated in the final prediction. The predictor does not incorporate evolutionary profile information explicitly, but is based on sequence patterns generated implicitly by encoding the protein segments using amino acid adjacency matrix. With a benchmark set of 35 β-transmembrane proteins, PredβTM shows a sensitivity and precision of 83.71% and 72.98%, respectively. The segment overlap score is 82.19%. In comparison with other state-of-art methods, PredβTM provides a higher precision and segment overlap without compromising with sensitivity. Further, we applied PredβTM to analyze the β-barrel membrane proteins without defined transmembrane regions and the uncharacterized protein sequences in eight bacterial genomes and predict possible β-transmembrane proteins. PredβTM can be freely accessed on the web at http://transpred.ki.si/.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145564&type=printable |
| spellingShingle | Amrita Roy Choudhury Marjana Novič PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. PLoS ONE |
| title | PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. |
| title_full | PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. |
| title_fullStr | PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. |
| title_full_unstemmed | PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. |
| title_short | PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. |
| title_sort | predβtm a novel β transmembrane region prediction algorithm |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0145564&type=printable |
| work_keys_str_mv | AT amritaroychoudhury predbtmanovelbtransmembraneregionpredictionalgorithm AT marjananovic predbtmanovelbtransmembraneregionpredictionalgorithm |