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Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable.

Peto M, Kloczkowski A, Honavar V, Jernigan RL - BMC Bioinformatics (2008)

Bottom Line: We classify sequences as folding to either highly- or poorly-designable conformations.We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy -- in some cases exceeding 95%.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov

ABSTRACT

Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.

Results: First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly- or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.

Conclusion: By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy -- in some cases exceeding 95%.

Show MeSH
The most designable conformations for a) the hexagonal and b) the triangular shape. Conformation a) has 54 sequences folding to it and 11 peptide bonds connecting the protein interior with exterior; conformation b) has 423 sequences folding to it and 9 interior-exterior spanning peptide bonds.
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Figure 3: The most designable conformations for a) the hexagonal and b) the triangular shape. Conformation a) has 54 sequences folding to it and 11 peptide bonds connecting the protein interior with exterior; conformation b) has 423 sequences folding to it and 9 interior-exterior spanning peptide bonds.

Mentions: Figure 3 shows the most designable conformations for each shape. The most designable conformation for the hexagonal shape shows features of symmetry that have been found in previous studies [17,24,28,29,33,38]. Both of the conformations contain many peptide bonds between the protein surface and the core, a feature that has been suggested to play an important role in the flexibility of proteins [38].


Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable.

Peto M, Kloczkowski A, Honavar V, Jernigan RL - BMC Bioinformatics (2008)

The most designable conformations for a) the hexagonal and b) the triangular shape. Conformation a) has 54 sequences folding to it and 11 peptide bonds connecting the protein interior with exterior; conformation b) has 423 sequences folding to it and 9 interior-exterior spanning peptide bonds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2655094&req=5

Figure 3: The most designable conformations for a) the hexagonal and b) the triangular shape. Conformation a) has 54 sequences folding to it and 11 peptide bonds connecting the protein interior with exterior; conformation b) has 423 sequences folding to it and 9 interior-exterior spanning peptide bonds.
Mentions: Figure 3 shows the most designable conformations for each shape. The most designable conformation for the hexagonal shape shows features of symmetry that have been found in previous studies [17,24,28,29,33,38]. Both of the conformations contain many peptide bonds between the protein surface and the core, a feature that has been suggested to play an important role in the flexibility of proteins [38].

Bottom Line: We classify sequences as folding to either highly- or poorly-designable conformations.We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy -- in some cases exceeding 95%.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011-3020, USA. myron.peto@ars.usda.gov

ABSTRACT

Background: By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.

Results: First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly- or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.

Conclusion: By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy -- in some cases exceeding 95%.

Show MeSH