Stochastic Local Search for the Optimization of Secondary Structure Packing in Proteins

摘要

We examine the problem of packing secondary structure fragments into low energy conformations via a local search optimization algorithm. We first describe a simplified off-lattice model for the representation of protein conformations and adapt the energy minimization problem behind protein folding into our model. We propose a move set that transforms a protein conformation into another in order to enable the use of local search algorithms for protein folding simulations and conformational search. Special care has been taken so that amino acids in a conformation do not overlap. The constraint of producing an overlap-free conformation can be seen as a objective that conflicts with the energy minimization. Therefore, we approach protein folding as a two-objective problem. We employ a monte carlo-based optimization algorithm in combination to the proposed move set. The algorithm deals with the energy minimization problem while maintaining overlap-free conformations. Initial conformations incorporate experimentally determined secondary structure, which is preserved throughout the execution of local search. Our method produced conformations with a minimum RMSD of alpha-carbon atoms in the range of 3.95A to 5.96A for all benchmarks apart from one for which the value was 7.&A.