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SECISearch3 and Seblastian: new tools for prediction of SECIS elements and selenoproteins.

Mariotti M, Lobanov AV, Guigo R, Gladyshev VN - Nucleic Acids Res. (2013)

Bottom Line: Seblastian is able to both identify known selenoproteins and predict new selenoproteins.By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues.An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.

View Article: PubMed Central - PubMed

Affiliation: Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, 02115, Boston, MA, USA and Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain.

ABSTRACT
Selenoproteins are proteins containing an uncommon amino acid selenocysteine (Sec). Sec is inserted by a specific translational machinery that recognizes a stem-loop structure, the SECIS element, at the 3' UTR of selenoprotein genes and recodes a UGA codon within the coding sequence. As UGA is normally a translational stop signal, selenoproteins are generally misannotated and designated tools have to be developed for this class of proteins. Here, we present two new computational methods for selenoprotein identification and analysis, which we provide publicly through the web servers at http://gladyshevlab.org/SelenoproteinPredictionServer or http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins. By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues. An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.

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Example of SECISearch3 generated image: SECIS type I of human SelN. The core and the unpaired conserved nucleotides of the SECIS element are highlighted in green, and mismatches in red. SECISearch3 uses internally RNAplot.
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gkt550-F2: Example of SECISearch3 generated image: SECIS type I of human SelN. The core and the unpaired conserved nucleotides of the SECIS element are highlighted in green, and mismatches in red. SECISearch3 uses internally RNAplot.

Mentions: Lastly, the remaining candidates are assigned a grade (A, B or C). We included this procedure after inspecting and grading manually hundreds of SECIS elements trying to incorporate our extensive experience with these structures. The grade depends on several characteristics: the presence of conserved unpaired nucleotides in the apical loop, the bending coefficient for helix2, the Covels score, the presence of mismatches or insertion in key positions (just before or just after the core, or in any two consecutive positions along helix2). For details, see the SECIS grading section in Supplementary Material S4. SECISearch3 may generate graphical output of publication quality: the program RNAplot from the RNAfold package is used with custom settings to highlight the key SECIS features (see Figure 2). We designed SECISearch3 to be as flexible as possible. Any combination of the prediction methods (or any single method) can be run. This allows balancing the trade-off between sensitivity and speed. For example, Covels should be avoided for large databases but may be used to find unusual candidate SECIS elements in relatively small databases. As default settings, we recommend to use the Infernal model with a score threshold of 10, prioritizing sensitivity.Figure 2.


SECISearch3 and Seblastian: new tools for prediction of SECIS elements and selenoproteins.

Mariotti M, Lobanov AV, Guigo R, Gladyshev VN - Nucleic Acids Res. (2013)

Example of SECISearch3 generated image: SECIS type I of human SelN. The core and the unpaired conserved nucleotides of the SECIS element are highlighted in green, and mismatches in red. SECISearch3 uses internally RNAplot.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753652&req=5

gkt550-F2: Example of SECISearch3 generated image: SECIS type I of human SelN. The core and the unpaired conserved nucleotides of the SECIS element are highlighted in green, and mismatches in red. SECISearch3 uses internally RNAplot.
Mentions: Lastly, the remaining candidates are assigned a grade (A, B or C). We included this procedure after inspecting and grading manually hundreds of SECIS elements trying to incorporate our extensive experience with these structures. The grade depends on several characteristics: the presence of conserved unpaired nucleotides in the apical loop, the bending coefficient for helix2, the Covels score, the presence of mismatches or insertion in key positions (just before or just after the core, or in any two consecutive positions along helix2). For details, see the SECIS grading section in Supplementary Material S4. SECISearch3 may generate graphical output of publication quality: the program RNAplot from the RNAfold package is used with custom settings to highlight the key SECIS features (see Figure 2). We designed SECISearch3 to be as flexible as possible. Any combination of the prediction methods (or any single method) can be run. This allows balancing the trade-off between sensitivity and speed. For example, Covels should be avoided for large databases but may be used to find unusual candidate SECIS elements in relatively small databases. As default settings, we recommend to use the Infernal model with a score threshold of 10, prioritizing sensitivity.Figure 2.

Bottom Line: Seblastian is able to both identify known selenoproteins and predict new selenoproteins.By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues.An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.

View Article: PubMed Central - PubMed

Affiliation: Division of Genetics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, 77 Avenue Louis Pasteur, 02115, Boston, MA, USA and Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain and Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain.

ABSTRACT
Selenoproteins are proteins containing an uncommon amino acid selenocysteine (Sec). Sec is inserted by a specific translational machinery that recognizes a stem-loop structure, the SECIS element, at the 3' UTR of selenoprotein genes and recodes a UGA codon within the coding sequence. As UGA is normally a translational stop signal, selenoproteins are generally misannotated and designated tools have to be developed for this class of proteins. Here, we present two new computational methods for selenoprotein identification and analysis, which we provide publicly through the web servers at http://gladyshevlab.org/SelenoproteinPredictionServer or http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for selenoprotein gene detection that uses SECISearch3 and then predicts selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able to both identify known selenoproteins and predict new selenoproteins. By applying these tools to diverse eukaryotic genomes, we provide a ranked list of newly predicted selenoproteins together with their annotated cysteine-containing homologues. An analysis of a representative candidate belonging to the AhpC family shows how the use of Sec in this protein evolved in bacterial and eukaryotic lineages.

Show MeSH