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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%.

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ROC curve for the Decision Tree (J48) classifier. Tripeptide segments are used to classify binary sequences folding to highly- and poorly-designable conformations for both the hexagonal and triangular shapes. The line x = y, expected for the random case is shown for comparison.
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Figure 7: ROC curve for the Decision Tree (J48) classifier. Tripeptide segments are used to classify binary sequences folding to highly- and poorly-designable conformations for both the hexagonal and triangular shapes. The line x = y, expected for the random case is shown for comparison.

Mentions: In figure 7 we show a receiver operating characteristic (ROC) curve for the decision tree (J48) classifier on tripeptide sequences in both the triangular and hexagonal shape. In this case our classifier performs worse than in the case of single sequences (hexagonal) but is still significantly better than random guesses. This suggests there is some important signal from the tripeptide segments of binary sequences folding to both shapes.


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)

ROC curve for the Decision Tree (J48) classifier. Tripeptide segments are used to classify binary sequences folding to highly- and poorly-designable conformations for both the hexagonal and triangular shapes. The line x = y, expected for the random case is shown for comparison.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: ROC curve for the Decision Tree (J48) classifier. Tripeptide segments are used to classify binary sequences folding to highly- and poorly-designable conformations for both the hexagonal and triangular shapes. The line x = y, expected for the random case is shown for comparison.
Mentions: In figure 7 we show a receiver operating characteristic (ROC) curve for the decision tree (J48) classifier on tripeptide sequences in both the triangular and hexagonal shape. In this case our classifier performs worse than in the case of single sequences (hexagonal) but is still significantly better than random guesses. This suggests there is some important signal from the tripeptide segments of binary sequences folding to both shapes.

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