Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping.

In protein fold recognition, a protein is classified into one of its folds. The recognition of a protein fold can be done by employing feature extraction methods to extract relevant information from protein sequences and then by using a classifier to accurately recognize novel protein sequences. In the past, several feature extraction methods have been developed but with limited recognition accuracy only. Protein sequences of varying lengths share the same fold and therefore they are very similar (in a fold) if aligned properly. To this, we develop an amino acid alignment method to extract important features from protein sequences by computing dissimilarity distances between proteins. This is done by measuring distance between two respective position specific scoring matrices of protein sequences which is used in a support vector machine framework. We demonstrated the effectiveness of the proposed method on several benchmark datasets. The method shows significant improvement in the fold recognition performance which is in the range of 4.3-7.6% compared to several other existing feature extraction methods.