An Anytime Local-to-Global Optimization Algorithm for Protein Threading in O(m 2 ñ 2 ) Space

Abstract

This paper describes a novel anytime branch-and-bound or best-first threading search algorithm for gapped block protein sequence-structure alignment with general sequence residue pair interactions. The new algorithm (1) returns a good approximate answer quickly, (2) iteratively improves that answer to the global optimum if allowed more time, (3) eventually produces a proof that the final answer found is indeed the global optimum, and (4) always terminates correctly within a bounded number of steps if allowed sufficient space and time. It runs in polynomial space, which is asymptotically dominated by the O(m²ñ²) space required by the lower bound computation. Using previously published data sets and the Bryant-Lawrence (1993) objective function, the algorithm found the true (proven) global optimum in less than 5 min in all search spaces size 10²⁵ or smaller (sequences to 478 residues), and a putative (not guaranteed) optimum in less than 5 hr in all search spaces size 10⁶⁰ or smaller (sequences to 793 residues, cores to 42 secondary structure segments). The threading in the largest case studied was eventually proven to be globally optimal; the corresponding search speed in that case was the equivalent of 1.5x10⁵⁶ threadings/sec, a speed-up exceeding 10²⁵ over previously published batch branch-and-bound speeds, and exceeding 10⁵⁰ over previously published exhaustive search speeds, using the same objective function and threading paradigm. Implementation-independent measures of search efficiency are defined for equivalent branching factor, depth, and probability of success per draw; empirical data on these measures are given. The general approach should apply to other alignment methodologies and search methods that use a divide-and-conquer strategy.