Structure-based computational protein design (CPD) refers to the problem
of finding a sequence of amino acids which folds into a specific desired
protein structure, and possibly fulfills some targeted biochemical
properties. Recent studies point out the particularly rugged CPD energy
landscape, suggesting that local search optimization methods should be
designed and tuned to easily escape local minima attraction basins. In
this paper, we analyze the performance and search dynamics of an
iterated local search (ILS) algorithm enhanced with partition crossover.
Our algorithm, PILS, quickly finds local minima and escapes their basins
of attraction by solution perturbation. Additionally, the partition
crossover operator exploits the structure of the residue interaction
graph in order to efficiently mix solutions and find new unexplored
basins. Our results on a benchmark of 30 proteins of various topology
and size show that PILS consistently finds lower energy solutions
compared to Rosetta fixbb and a classic ILS, and that the corresponding
sequences are mostly closer to the native.
Iterated Local Search with Partition Crossover for Computational Protein Design
