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On Complexity of Protein Structure Alignment Problem under Distance Constraint
March/April 2012 (vol. 9 no. 2)
pp. 511-516
Aleksandar Poleksic, University of Northern Iowa, Cedar Falls
We study the well-known Largest Common Point-set (LCP) under Bottleneck Distance Problem. Given two proteins a and b (as sequences of points in three-dimensional space) and a distance cutoff \sigma, the goal is to find a spatial superposition and an alignment that maximizes the number of pairs of points from a and b that can be fit under the distance \sigma from each other. The best to date algorithms for approximate and exact solution to this problem run in time O(n^8 ) and O(n^{32} ), respectively, where n represents protein length. This work improves runtime of the approximation algorithm and the expected runtime of the algorithm for absolute optimum for both order-dependent and order-independent alignments. More specifically, our algorithms for near-optimal and optimal sequential alignments run in time O(n^7 \log n) and O(n^{14} \log n), respectively. For nonsequential alignments, corresponding running times are O(n^{7.5} ) and O(n^{14.5} ).

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Index Terms:
Protein structure, structural alignment, structural similarity, alignment algorithms.
Citation:
Aleksandar Poleksic, "On Complexity of Protein Structure Alignment Problem under Distance Constraint," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 511-516, March-April 2012, doi:10.1109/TCBB.2011.133
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