Protein structure similarity based on multi-view images generated from 3D molecular visualization

Comparing the structures of proteins is one of the most challenging problems in structural biology. Root Mean Square Distance (RMSD) has become a standard measurement to calculate the similarity between two protein structures. However, to get the best result one has to align and superpose the two protein structures, which raises issues related to finding the best alignment technique. In this paper, we propose a new approach to protein structure comparison using canonical angles between two subspaces generated from multiple views of the protein structure visualization. The main advantage of our approach is that no protein alignment is required. Moreover, since we also consider the various visualization types of the 3D protein structures (backbone, ribbons, and rockets), our protein descriptors contain more elaborate structures and characteristics of the protein, which possibly cannot be represented by only a single visualization geometry. The validity of our proposed method is shown by experiments on classifications of four classes of protein in which our approach exhibited better performance than the two well-known methods of combinatorial extension alignment and the Gauss integral tuning.