The primary structure of proteins consists of a linear chain of amino acids that can vary in length. Proteins fold, under the influence of several chemical and physical factors, into their 3D structures, which determine their biological functions and properties. Misfolding occurs when the protein folds into a 3D structure that does not represent its native structure, which can lead to diseases. Due to the importance of this problem and since laboratory techniques are not always feasible, computational methods for characterizing protein structures have been proposed. In this paper, we present a particle swarm optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar model. Starting from a small set of candidate solutions, our algorithm efficiently explores the search space and returns 3D protein structures with minimal energy. To test our algorithm, we used two sets of benchmark sequences of different lengths and compared our results to published results. Our algorithm performs better than previous algorithms by finding lower energy structures or by performing fewer numbers of energy evaluations.

Proteins are initially linear chains of amino acids that fold, under the influence of several chemical and physical factors, into their 3-dimensional structures. Due to the importance of this problem and since laboratory techniques are not always feasible, computational methods for characterizing protein structures have been proposed. In this paper, we present a Particle Swarm Optimization (PSO) based algorithm for predicting protein structures in the 3D HP model. Starting from a small set of potential solutions, our algorithm efficiently explores the search space of candidate solutions and returns 3D protein structures with minimal energy. To test our algorithm, we use two sets of benchmark sequences of different lengths. It is found that the results of the PSO algorithm are better than those of previous algorithms. I.978-1-4244-3806-8/09/$25.00

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