The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
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The right-handed single-stranded β-helix proteins are characterized as virulence factors, allergens, toxins that are threat to human health. Identification of these proteins from amino acid sequence is of great importance as these proteins are potential targets for anti-bacterial and fungal agents. In this paper, support vector machine (SVM) has been used to predict the presence of β-helix fold in protein sequences using dipeptide composition. An input vector of 400 dimensions for dipeptide compositions is used to search for the presence of putative rungs or coils, the conserved secondary structure, found in β-helix proteins. An average accuracy of 89.2% with Matthew's correlation coefficient of 0.75 is obtained in a 5-fold cross-validation technique. In addition, a PSSM was also used to score the query sequence of proteins identified as β-helices by SVM. The method recognizes right-handed β-helices with 100% sensitivity and 99.6% specificity on test set of known protein structures.