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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Published inPloS one Vol. 10; no. 12; p. e0145564
Main Authors Roy Choudhury, Amrita, Novič, Marjana
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 22.12.2015
Public Library of Science (PLoS)
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ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0145564

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Abstract 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/.
AbstractList 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/.
Author Novič, Marjana
Roy Choudhury, Amrita
AuthorAffiliation Laboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia
Russian Academy of Sciences, Institute for Biological Instrumentation, RUSSIAN FEDERATION
AuthorAffiliation_xml – name: Laboratory of Chemometrics, National Institute of Chemistry, Ljubljana, Slovenia
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ContentType Journal Article
Copyright 2015 Roy Choudhury, Novič. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2015 Roy Choudhury, Novič 2015 Roy Choudhury, Novič
Copyright_xml – notice: 2015 Roy Choudhury, Novič. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: ARC MN. Performed the experiments: ARC. Analyzed the data: ARC. Wrote the paper: ARC MN.
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  article-title: Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor
  publication-title: Protein Sci
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  start-page: 2964
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  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/bth340
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Snippet Predicting the transmembrane regions is an important aspect of understanding the structures and architecture of different β-barrel membrane proteins. Despite...
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StartPage e0145564
SubjectTerms Acids
Algorithms
Amino acid sequence
Amino acids
Bacteria
Bioinformatics
Gene sequencing
Genomes
Gram-positive bacteria
Laboratories
Membrane proteins
Membrane Proteins - chemistry
Membrane Proteins - genetics
Predictions
Predictive Value of Tests
Protein Structure, Secondary
Protein Structure, Tertiary
Proteins
Segments
Sensitivity
Sensitivity analysis
Sequence Analysis, Protein - methods
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Title PredβTM: A Novel β-Transmembrane Region Prediction Algorithm
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