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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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Distribution of values of exposure of glutamine (Q) in different location class proteins.
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Figure 4: Distribution of values of exposure of glutamine (Q) in different location class proteins.

Mentions: We then compared the distribution of exposure values for the 20 different amino acids in each of the four protein classes and observed variation for particular amino acids and protein locations (Additional file 1: Figures S1-S4). For example, when we compare the distribution of exposure values for glutamine (Q) in different location classes we can see that glutamines in extracellular proteins are more buried than in intracellular proteins (Figure 4). Conversely, cysteines in extracellular proteins have a distinct peak at high exposure values, which is absent from intracellular proteins (Additional file 1: Figures S1-S4). These differences imply that exposure values can be used to predict protein location.


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

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

Distribution of values of exposure of glutamine (Q) in different location class proteins.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Distribution of values of exposure of glutamine (Q) in different location class proteins.
Mentions: We then compared the distribution of exposure values for the 20 different amino acids in each of the four protein classes and observed variation for particular amino acids and protein locations (Additional file 1: Figures S1-S4). For example, when we compare the distribution of exposure values for glutamine (Q) in different location classes we can see that glutamines in extracellular proteins are more buried than in intracellular proteins (Figure 4). Conversely, cysteines in extracellular proteins have a distinct peak at high exposure values, which is absent from intracellular proteins (Additional file 1: Figures S1-S4). These differences imply that exposure values can be used to predict protein location.

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