Measuring the similarity of protein structures using image local feature descriptors SIFT and SURF

Understanding of protein structures is important to find their functions. Many methods such as structural alignment, alignment-free similarity, and use of structural fragments have been developed for finding similar protein structures. In our previous study, we transformed protein structures into images each pixel of which represents the distance between the corresponding Cα atoms, and proposed similarity measures between two protein structures based on Kolmogorov complexity using image compression algorithms. In this paper, we examine efficient and effective image recognition techniques, SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which are invariant to image scaling, translation, and rotation, and partially invariant to affine or three-dimensional projection. We propose similarity based on SIFT and SURF, and apply it to classification of several protein structures. The results suggest that the similarity based on SURF outperforms several existing similarity measures including the compression-based similarity measures in our previous study, and that SIFT and SURF are useful for recognizing protein structures as well as objects in images.