The road not taken: retreat and diverge in local search for simplified protein structure prediction.
BACKGROUND
Given a protein's amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy.


RESULTS
In this paper, we present an efficient local search algorithm that deals with these two problems. During search, we select the best candidate at each iteration, but store the unexplored second best candidates in a set of elite conformations, and explore them whenever the search faces stagnation. Moreover, we propose a new non-isomorphic encoding for the protein conformations to store the conformations and to check similarity when applied with a memory based search. This new encoding helps eliminate conformations that are equivalent under rotation and translation, and thus results in better prevention of re-visitation.


CONCLUSIONS
On standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice.
Institute of Intelligent and Integrated Systems, Griffith University, Queensland, Australia. swakkhar.shatabda@nicta.com.au
Swakkhar
S
Shatabda
M A Hakim
MA
Newton
Mahmood A
MA
Rashid
Duc Nghia
DN
Pham
Abdul
A
Sattar
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D008962:Models, Theoretical; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
D011506:Proteins