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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 average number of sequences folding to conformations having the specified number of covalent bonds connecting protein interior with exterior for a) hexagonal and b) triangular shapes.
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Figure 5: The average number of sequences folding to conformations having the specified number of covalent bonds connecting protein interior with exterior for a) hexagonal and b) triangular shapes.

Mentions: It has been suggested that the number of peptide bonds spanning between the protein interior and exterior is related to designability, by increasing amount of protein secondary structure and allowing for easier unfolding and folding of the sequence [38]. Previous studies using lattice models have found such a relationship between the number of covalent bonds between the interior and exterior and the protein designability [17,38]. We have computed the average number of sequences folding to conformations having a specified number of peptide bonds between the protein interior and exterior. The results are given in Figure 5 for both the hexagonal (Fig. 5a) and the triangular (Fig. 5b) shapes. Both plots show a strong dependence between the increase in the number of covalent bonds connecting protein interior with exterior and the increase in designability, confirming earlier results in References [17,38].


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 average number of sequences folding to conformations having the specified number of covalent bonds connecting protein interior with exterior for a) hexagonal and b) triangular shapes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: The average number of sequences folding to conformations having the specified number of covalent bonds connecting protein interior with exterior for a) hexagonal and b) triangular shapes.
Mentions: It has been suggested that the number of peptide bonds spanning between the protein interior and exterior is related to designability, by increasing amount of protein secondary structure and allowing for easier unfolding and folding of the sequence [38]. Previous studies using lattice models have found such a relationship between the number of covalent bonds between the interior and exterior and the protein designability [17,38]. We have computed the average number of sequences folding to conformations having a specified number of peptide bonds between the protein interior and exterior. The results are given in Figure 5 for both the hexagonal (Fig. 5a) and the triangular (Fig. 5b) shapes. Both plots show a strong dependence between the increase in the number of covalent bonds connecting protein interior with exterior and the increase in designability, confirming earlier results in References [17,38].

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