Improved scatter search algorithm for predicting all-atoms protein structures using charmm22 energy model. (c2010)

Abstract

Proteins are organic compounds made up of chains of amino acids. Chemical and physical properties determine the 3-dimensional structure and folding of a protein. A protein needs to be folded into its proper 3D structure for its function to remain intact. The protein structure prediction problem has real-world implication, since the 3D structure of a protein gives important clues regarding its function, localization, and interactions. Wet laboratory techniques are costly in terms of time and effort, consequently having a right protein structure prediction model reduces cost and time by eliminating some of the initial wet lab work. Consequently, we need to study methods that predict protein structures. In this thesis, we present an improved scatter search (SS) algorithm for predicting all-atoms protein structures using the CHARMM22 energy model. Our algorithm produces a 3D structure of the whole protein by minimizing the energy function linked to protein folding. This is based on a sequence of amino acids as well as on data collected from known protein structures for comparative purposes. Defined as an evolutionary algorithm, SS relies on a population of candidate solutions. Candidate solutions, over a number of iterations, experience evolutionary operations which combine intense search and diversification. Our algorithm is evaluated on few proteins, whose structure is defined in a Protein Data Bank (PDB). The results generated by the improved SS algorithm are compared with those of other energy models. Our results showed that our algorithm produces 3D structures with good and promising root mean square deviations from the reference proteins. This study also demonstrates the advantage of the CHARMM22 energy model.