The road not taken: retreat and diverge in local search for simplified protein structure prediction

Abstract

BackgroundGiven 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.ResultsIn 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.ConclusionOn standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice.

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

“…There are a large number of existing search algorithms that attempt to solve the PSP problem by exploring feasible structures called conformations. For the low resolution hydrophobic-polar (HP) energy model, a memory based local search algorithm [8,9], a population-based genetic algorithm [10], and a hydrophobic core directed local search method [11] reportedly produced the state-of-the-art results on the face-centred-cubic (FCC) lattice. For the high resolution Berrera 20 × 20 energy matrix (henceforth referred to as BM energy model) [12][13][14] produces the state-of-the-art results.…”

An efficient encoding for simplified protein structure prediction using genetic algorithms

“…Shatabda et al [35] proposed a memory based approach on top of the algorithm proposed by Dotu et al [34] and improved the results on the FCC lattice and HP energy model. These results were further improved in a subsequent work [36]. Other methods (such as Ant Colony Optimization (ACO) [37], and Extremal Optimization [38]) are also found in the literature.…”

“…Many researchers have also used relative encodings [3], [35]. Two other non-isomorphic encodings are also found to be used [36], [40].…”

Exact methods for lattice protein models

“…As exhaustive structure enumeration approaches [20] are restricted to very short sequence lengths, usually heuristic methods are applied. They apply various techniques, e.g., simulated annealing [21,22], quantum annealing [23], ant colonization optimization [24], evolutionary algorithms [25], energy landscape paving [26], large neighborhood search [27,28], constraint programming [29][30][31], or dedicated search heuristics [32][33][34][35]. They enable performance-guaranteed approximations close to the optimum for both backbone-only [36,37] and sidechain models [38,39].…”