Relating metrics, lines and variables defined on graphs to problems in medicinal chemistry
Graph theory with applications to algorithms and computer science
A branch and bound algorithm for the maximum clique problem
Computers and Operations Research
Detection of three-dimensional patterns of atoms in chemical structures
Communications of the ACM
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Protein function prediction via graph kernels
Bioinformatics
Protein Secondary Structure Prediction Based on Ramachandran Maps
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
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It is known that the function of a protein is determined by its structure. Thus, structural similarity between proteins plays an important role as a good predictor of functional similarity. Many methods focus on solving the protein structure alignment problem. In this paper, we propose a graph-based approach to measure the similarity of two proteins. We first transfer a protein into a labeled graph according to its secondary structures, chemical properties, and 3D topology. For two graphs, we then find their maximum common edge subgraph for measuring the structural similarity of the corresponding proteins. By using a practical technique, the maximum common edge subgraph of two graphs can be found efficiently. Finally, by a backtracking, we can find the common substructure of the given proteins. Experimental results show that our method outperforms the RMSD method. This graph-based approach provides a practical direction for measuring protein structural similarity.