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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 hexagonal and the triangular shapes used for the designability studies. There are 20,843 different compact conformations unrelated by shape symmetries for this hexagon and 22,104 for this triangle.
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Figure 1: The hexagonal and the triangular shapes used for the designability studies. There are 20,843 different compact conformations unrelated by shape symmetries for this hexagon and 22,104 for this triangle.

Mentions: Our aim is to extend designability studies to various shapes on the 2D triangular lattice and classify sequences folding to highly and poorly designable conformations using machine learning algorithms. The two shapes that are studied here are the triangle and the hexagon, shown in Figure 1. The triangular lattice with the shape of the regular hexagon in Figure 1 has 19 nodes, while the equilateral triangle contains 21 nodes. Therefore there are 219 (≅ 5.2 × 105) and 221 (≅ 2.1 × 106) different H/P sequences for each shape. (Our model has no sequence symmetry because of the difference between the C and the N terminals). Because of relatively small numbers of possible H/P sequences and the numbers of all possible compact (no voids allowed) self-avoiding walks unrelated by shape symmetries for the hexagon (20,843) and the triangle (22,104), we are able to enumerate them completely and perform complete designability computations. Similarly, as in previous studies, we find that certain distinct conformations have many sequences folding to those structures, while other have few or no sequences folding to them.


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 hexagonal and the triangular shapes used for the designability studies. There are 20,843 different compact conformations unrelated by shape symmetries for this hexagon and 22,104 for this triangle.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The hexagonal and the triangular shapes used for the designability studies. There are 20,843 different compact conformations unrelated by shape symmetries for this hexagon and 22,104 for this triangle.
Mentions: Our aim is to extend designability studies to various shapes on the 2D triangular lattice and classify sequences folding to highly and poorly designable conformations using machine learning algorithms. The two shapes that are studied here are the triangle and the hexagon, shown in Figure 1. The triangular lattice with the shape of the regular hexagon in Figure 1 has 19 nodes, while the equilateral triangle contains 21 nodes. Therefore there are 219 (≅ 5.2 × 105) and 221 (≅ 2.1 × 106) different H/P sequences for each shape. (Our model has no sequence symmetry because of the difference between the C and the N terminals). Because of relatively small numbers of possible H/P sequences and the numbers of all possible compact (no voids allowed) self-avoiding walks unrelated by shape symmetries for the hexagon (20,843) and the triangle (22,104), we are able to enumerate them completely and perform complete designability computations. Similarly, as in previous studies, we find that certain distinct conformations have many sequences folding to those structures, while other have few or no sequences folding to them.

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