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A novel approach for protein subcellular location prediction using amino acid exposure.

Mer AS, Andrade-Navarro MA - BMC Bioinformatics (2013)

Bottom Line: For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations.In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one.Our algorithm uses a novel approach to address the multiclass classification problem.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology and Data Mining, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str, 10, Berlin 13125, Germany. miguel.andrade@mdc-berlin.de.

ABSTRACT

Background: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure.

Results: Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity.

Conclusions: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE' and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.

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Venn diagram of eukaryotic proteins exclusively found in three localization categories (selected from UniProt; see Methods for details). A significant number of proteins are found both in the cytoplasm and in the nucleus.
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Figure 1: Venn diagram of eukaryotic proteins exclusively found in three localization categories (selected from UniProt; see Methods for details). A significant number of proteins are found both in the cytoplasm and in the nucleus.

Mentions: To generate a training dataset we first selected proteins annotated to occur in three major locations: nuclear (N), cytoplasmic (C) and extracellular (E), and not in other locations (see Methods for details; Figure 1). Given the significant amount of proteins that shuttle between nucleus and cytoplasm (approximately one in three nuclear proteins) we considered an extra category (nucleocytoplasmic, Y). To obtain reliable information on amino acid exposure, we then selected proteins of known structure for each of these four categories (see Methods for details; Table 1). We obtained values of residue accessibility for all amino acids of the sequences in this dataset that were covered by 3D-structural information (see Methods).


A novel approach for protein subcellular location prediction using amino acid exposure.

Mer AS, Andrade-Navarro MA - BMC Bioinformatics (2013)

Venn diagram of eukaryotic proteins exclusively found in three localization categories (selected from UniProt; see Methods for details). A significant number of proteins are found both in the cytoplasm and in the nucleus.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219330&req=5

Figure 1: Venn diagram of eukaryotic proteins exclusively found in three localization categories (selected from UniProt; see Methods for details). A significant number of proteins are found both in the cytoplasm and in the nucleus.
Mentions: To generate a training dataset we first selected proteins annotated to occur in three major locations: nuclear (N), cytoplasmic (C) and extracellular (E), and not in other locations (see Methods for details; Figure 1). Given the significant amount of proteins that shuttle between nucleus and cytoplasm (approximately one in three nuclear proteins) we considered an extra category (nucleocytoplasmic, Y). To obtain reliable information on amino acid exposure, we then selected proteins of known structure for each of these four categories (see Methods for details; Table 1). We obtained values of residue accessibility for all amino acids of the sequences in this dataset that were covered by 3D-structural information (see Methods).

Bottom Line: For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations.In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one.Our algorithm uses a novel approach to address the multiclass classification problem.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Biology and Data Mining, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str, 10, Berlin 13125, Germany. miguel.andrade@mdc-berlin.de.

ABSTRACT

Background: Proteins perform their functions in associated cellular locations. Therefore, the study of protein function can be facilitated by predictions of protein location. Protein location can be predicted either from the sequence of a protein alone by identification of targeting peptide sequences and motifs, or by homology to proteins of known location. A third approach, which is complementary, exploits the differences in amino acid composition of proteins associated to different cellular locations, and can be useful if motif and homology information are missing. Here we expand this approach taking into account amino acid composition at different levels of amino acid exposure.

Results: Our method has two stages. For stage one, we trained multiple Support Vector Machines (SVMs) to score eukaryotic protein sequences for membership to each of three categories: nuclear, cytoplasmic and extracellular, plus extra category nucleocytoplasmic, accounting for the fact that a large number of proteins shuttles between those two locations. In stage two we use an artificial neural network (ANN) to propose a category from the scores given to the four locations in stage one. The method reaches an accuracy of 68% when using as input 3D-derived values of amino acid exposure. Calibration of the method using predicted values of amino acid exposure allows classifying proteins without 3D-information with an accuracy of 62% and discerning proteins in different locations even if they shared high levels of identity.

Conclusions: In this study we explored the relationship between residue exposure and protein subcellular location. We developed a new algorithm for subcellular location prediction that uses residue exposure signatures. Our algorithm uses a novel approach to address the multiclass classification problem. The algorithm is implemented as web server 'NYCE' and can be accessed at http://cbdm.mdc-berlin.de/~amer/nyce.

Show MeSH
Related in: MedlinePlus