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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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Residue exposure frequency distributions (from buried to exposed) for each of the 20 amino acids in the proteins of known structure and experimentally verified location used to train the algorithm (Table 1).
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Figure 2: Residue exposure frequency distributions (from buried to exposed) for each of the 20 amino acids in the proteins of known structure and experimentally verified location used to train the algorithm (Table 1).

Mentions: We then studied the distribution of exposure values for the 20 different amino acids. We observed that residues with side chains belonging to the same physicochemical property group show similar frequency distributions (Figure 2). For example the hydrophobic residues isoleucine (I), valine (V), leucine (L) and alanine (A) show very similar distributions with a very high frequency in the low accessibility region and fewer residues in the high relative accessibility region. Principal component analysis (PCA) of these data shows this more prominently (Figure 3).


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

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

Residue exposure frequency distributions (from buried to exposed) for each of the 20 amino acids in the proteins of known structure and experimentally verified location used to train the algorithm (Table 1).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Residue exposure frequency distributions (from buried to exposed) for each of the 20 amino acids in the proteins of known structure and experimentally verified location used to train the algorithm (Table 1).
Mentions: We then studied the distribution of exposure values for the 20 different amino acids. We observed that residues with side chains belonging to the same physicochemical property group show similar frequency distributions (Figure 2). For example the hydrophobic residues isoleucine (I), valine (V), leucine (L) and alanine (A) show very similar distributions with a very high frequency in the low accessibility region and fewer residues in the high relative accessibility region. Principal component analysis (PCA) of these data shows this more prominently (Figure 3).

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