Particle swarm optimization based algorithm for protein structure prediction in the 3D HP model. (c2008)

Abstract

Proteins play key roles in many biological functions and represent the building blocks of organisms. They are found in the hair, skin, muscles, blood, etc. ... Proteins are complex organic compounds of which the basic forming unit is the amino acid. They are initially linear chains of amino acids that can vary in length from 50 up to 5000 Amino Acids. Proteins fold , under the int1uence of several chemical and physical factors, into their 3- dimensional structures, which determine their biological functions and properties. Misfolding occurs when the protein folds into a 3D structure that does not represent its correct native structure, which can lead to many diseases such as Alzheimer and several types of cancer. Due to the importance of this problem and since lab techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) are not always feasible, computational methods for characterizing protein structures have been proposed. In this thesis, we present a Particle Swann Optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar 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 minimum or near-minimum energy. To test our algorithm, we use two sets of benchmark sequences of different lengths and compare our results to published results. Our algorithm performs slightly better than previous algorithms by finding lower energy structures with fewer numbers of energy evaluations.