Abstract
BackgroundBy 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.ResultsFirst, 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.ConclusionBy 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%.
Highlights
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
Through the use of complete enumerations of H/P sequences and compact lattice conformations it has been found that most protein sequences fold to a relatively small number of so called "highly-designable" conformations, while the remaining conformations have few, or no, sequences that fold to them [24,25,26,27,28,29,30,31,32,33]
The results obtained for lattice proteins suggest that, as for real proteins, designable conformations tend to exhibit structural symmetries. These findings show that a simple lattice model can demonstrate important traits that are mirrored in real proteins
Summary
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. In coarse-grained models of proteins a detailed atomistic description of the structure is replaced by a much simpler view where each amino acid is represented by a single point. Theoretical models of proteins frequently replace the 20-letter amino acid alphabet with a reduced alphabet, up to the limit of a much simpler (page number not for citation purposes). Through the use of complete enumerations of H/P sequences and compact lattice conformations it has been found that most protein sequences fold to a relatively small number of so called "highly-designable" conformations, while the remaining conformations have few, or no, sequences that fold to them [24,25,26,27,28,29,30,31,32,33]. In the present work we use a standard H/P alphabet and a 2D triangular lattice and apply machine learning algorithms to study protein designability for such a reduced model
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