Issue No. 02 - March/April (2012 vol. 9)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.133
A. Poleksic , Dept. of Comput. Sci., Univ. of Northern Iowa, Cedar Falls, IA, USA
We study the well-known Largest Common Point-set (LCP) under Bottleneck Distance Problem. Given two proteins o and 6 (as sequences of points in three-dimensional space) and a distance cutoff σ, 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 σ from each other. The best to date algorithms for approximate and exact solution to this problem run in time O(n8) and O(n32), 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(n7log n) and O(n14 log n), respectively. For nonsequential alignments, corresponding running times are O(n7.5) and O(n14.5).
Proteins, Approximation algorithms, Bioinformatics, Approximation methods, Computational biology, Algorithm design and analysis, Space exploration
A. Poleksic, "On Complexity of Protein Structure Alignment Problem under Distance Constraint," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 511-516, 2012.