The characterization of a measure of information discrepancy
Information Sciences—Applications: An International Journal
Finding the consensus shape for a protein family
Proceedings of the eighteenth annual symposium on Computational geometry
Structural alignment of large—size proteins via lagrangian relaxation
Proceedings of the sixth annual international conference on Computational biology
A measure of discrepancy of multiple sequences
Information Sciences: an International Journal
Protein structure alignment by deterministic annealing
Bioinformatics
Supervised classification of protein structures based on convex hull representation
International Journal of Bioinformatics Research and Applications
Prediction of protein structural classes by a new measure of information discrepancy
Computational Biology and Chemistry
A Spectral Approach to Protein Structure Alignment
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
gHull: A GPU algorithm for 3D convex hull
ACM Transactions on Mathematical Software (TOMS)
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To unscramble the relationship between protein function and protein structure, it is essential to assess the protein similarity from different aspects. Although many methods have been proposed for protein structure alignment or comparison, alternative similarity measures are still strongly demanded due to the requirement of fast screening and query in large-scale structure databases. In this paper, we first formulate a novel representation of a protein structure, i.e., Feature Sequence of Surface (FSS). Then, a new score scheme is developed to measure the similarity between two representations. To verify the proposed method, numerical experiments are conducted in four different protein data sets. We also classify SARS coronavirus to verify the effectiveness of the new method. Furthermore, preliminary results of fast classification of the whole CATH v2.5.1 database based on the new macrostructure similarity are given as a pilot study. We demonstrate that the proposed approach to measure the similarities between protein structures is simple to implement, computationally efficient, and surprisingly fast. In addition, the method itself provides a new and quantitative tool to view a protein structure.