Prediction of function changes associated with single-point protein mutations using support vector machines (SVMs)

Computational methods can be used to predict the effects of single amino acid substitutions (single-point mutations). In contrast to previous methods that need many protein sequence and structural features, we applied support vector machines (SVMs) to predict protein function changes associated with amino acid substitutions using only sequence information, and cross-validated them on a large dataset extracted from the Protein Mutant Database (PMD). By three SVM classifiers, we investigated three local sequence features of proteins (residue composition, hydrophobic interaction, and evolutionary property), and examined their effects on the prediction accuracy. As a main result, a novel SVM named substitution-matrix-based kernel SVM was constructed to make speedy and accurate prediction, and its value was shown in an application case. Furthermore, our findings confirmed results from other studies.