Introduction to algorithms
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Multiple Structural Alignment and Core Detection by Geometric Hashing
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Exact computation of protein structure similarity
Proceedings of the twenty-second annual symposium on Computational geometry
Finding compact structural motifs
Theoretical Computer Science
Evaluating Protein Similarity from Coarse Structures
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A fuzzy sets based generalization of contact maps for the overlap of protein structures
Fuzzy Sets and Systems
Multiple structure alignment and consensus identification for proteins
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Finding compact structural motifs
CPM'07 Proceedings of the 18th annual conference on Combinatorial Pattern Matching
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We define and prove properties of the consensus shape for a family of proteins, a protein-like structure that provides a compact summary of the significant structural information for a protein family. If all members of a protein family exhibit a geometric relationship between corresponding alpha carbons then that relationship is preserved in the consensus shape. In particular, distances and angles that are consistent across family members are preserved. For the consensus shape, the spacing between successive alpha carbons is variable, with small distances in regions where the members of the protein family exhibit significant variation and large distances (up to the standard spacing of about 4\AA) in regions where the family members agree. Despite this non-protein-like characteristic, the consensus shape preserves and highlights important structural information. We describe an iterative algorithm for computing the consensus shape and prove that the algorithm converges. We also present the results of experiments in which we build consensus shapes for several known protein families.