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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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Accuracy of one-vs.-rest SVM classifications for nuclear (N), nucleocytoplasmic (Y), cytoplasmic (C) and extracellular (E) proteins using residues in different ranges of exposure (1–6, from buried to exposed; see text and Methods for details).
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Figure 5: Accuracy of one-vs.-rest SVM classifications for nuclear (N), nucleocytoplasmic (Y), cytoplasmic (C) and extracellular (E) proteins using residues in different ranges of exposure (1–6, from buried to exposed; see text and Methods for details).

Mentions: We then trained an SVM on such amino acid composition vectors for proteins from each of the four localization categories (see Methods for details). The accuracy of the classifier was distinctively better for extracellular proteins and worst for nucleocytoplasmic proteins (Figure 5). Interestingly, for nuclear proteins, and less so for nucleocytoplasmic and cytoplasmic proteins, the middle ranges of exposure (3 and 4) seem to contain less signal about the localization of the protein. For extracellular proteins, buried residues contain more information on the localization of the protein than exposed residues. In any case, the complete protein amino acid composition (full range: 1 2 3 4 5 6) was a better predictor than each of the six individual ranges, with composition from multiple ranges, e.g. (1 2), (3 4 5 6), close.


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

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

Accuracy of one-vs.-rest SVM classifications for nuclear (N), nucleocytoplasmic (Y), cytoplasmic (C) and extracellular (E) proteins using residues in different ranges of exposure (1–6, from buried to exposed; see text and Methods for details).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Accuracy of one-vs.-rest SVM classifications for nuclear (N), nucleocytoplasmic (Y), cytoplasmic (C) and extracellular (E) proteins using residues in different ranges of exposure (1–6, from buried to exposed; see text and Methods for details).
Mentions: We then trained an SVM on such amino acid composition vectors for proteins from each of the four localization categories (see Methods for details). The accuracy of the classifier was distinctively better for extracellular proteins and worst for nucleocytoplasmic proteins (Figure 5). Interestingly, for nuclear proteins, and less so for nucleocytoplasmic and cytoplasmic proteins, the middle ranges of exposure (3 and 4) seem to contain less signal about the localization of the protein. For extracellular proteins, buried residues contain more information on the localization of the protein than exposed residues. In any case, the complete protein amino acid composition (full range: 1 2 3 4 5 6) was a better predictor than each of the six individual ranges, with composition from multiple ranges, e.g. (1 2), (3 4 5 6), close.

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