On complexity of protein structure alignment problem under distance constraint
alignment algorithms, Protein structure, structural alignment, structural similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics
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 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(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 7log n) and O(n 14log n), respectively. For nonsequential alignments, corresponding running times are O(n 7.5) and O(n 14.5). © 2012 IEEE.
Original Publication Date
DOI of published version
Poleksic, Aleksandar, "On complexity of protein structure alignment problem under distance constraint" (2012). Faculty Publications. 1813.