Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Finding the consensus shape for a protein family
Proceedings of the eighteenth annual symposium on Computational geometry
Multiple Structural Alignment and Core Detection by Geometric Hashing
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
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An algorithm is presented to compute a multiple structure alignment for a set of proteins and to generate a consensus structure which captures common substructures present in the given proteins. The algorithm is a heuristic in that it computes an approximation to the optimal alignment that minimizes the sum of the pairwise distances between the consensus and the transformed proteins. A distinguishing feature of the algorithm is that it works directly with the coordinate representation in three dimensions with no loss of spatial information, unlike some other multiple structure alignment algorithms that operate on sets of backbone vectors translated to the origin; hence, the algorithm is able to generate true alignments. Experimental studies on several protein datasets show that the algorithm is quite competitive with a well-known algorithm called CE-MC. A web-based tool has also been developed to facilitate remote access to the algorithm over the Internet.