Protein katlanma probleminin çözümü için kaba-taneli kafes ve kafes-dışı modelleri kullanan yapay zeka tabanlı yöntemler

The protein folding problem is one of the most widely studied problem within the bioinformatics community. Computational methods proposed for the solution of this problem can be categorized into two main groups: Comparative modeling, and ab initio methods. Comparative modeling utilizes existing databases of experimentally determined protein structures to determine the three-dimensional structure of proteins. However, in ab initio methods three-dimensional structure of proteins are determined from solely their amino acid sequences. In the ab initio methods, a number of potential energy functions with different resolutions (including the simple coarse-grained methods and the detailed all-atom models) are proposed to model the interactions that occur among the amino acid molecules of the proteins. A search method is then used to thoroughly explore the energy landscape of the defined potential energy function to find the optimum fold of a protein. In this thesis, new possibilities are searched to find an effective way of improving the search abilities for ab initio methods. Within this scope, both the coarse-grained and all-atom models are studied to determine the protein structures. Coarse-grained methods studied in this thesis include the simplified lattice and off-lattice models. For the hydrophobic polar (HP) lattice model, a new state-space representation of the protein folding problem is proposed for the use of reinforcement learning methods. The proposed state-space representation reduces the dependency of the size of the state-action space to the amino acid sequence length. The proposed method also introduces the concept of "learning" for the protein folding problem in two-dimensional HP model. Thus, at the end of a learning process optimum fold of any sequence of a particular length can be found which is not the case in the existing methods. Moreover, by utilizing a swarm based reinforcement method (Ant-Q algorithm) the optimal fold is found rapidly when compared to the most widely used reinforcement learning algorithm, the Q-learning algorithm. For the off-lattice AB model, a new optimization algorithm, the Vortex Search (VS) algorithm, is proposed to minimize the energy function of this model. The proposed VS algorithm tested on a benchmark numerical function set and it is shown that it performs quite well when compared to the well known optimization algorithms. Another contribution of the thesis presented for the off-lattice AB model deals with the energy function of this model. The energy landscape of the off-lattice AB model leads the algorithms to easily trap into local minimum points. In literature, to escape from local minimum points, usually a combination of the well known optimization algorithms or some extensions of these algorithms are proposed. However, in this thesis rather than an algorithmic improvement, a more smoothed energy landscape is provided for the algorithms by modifying the energy function of the off-lattice AB model. The all-atom model studied in the thesis is based on the ECEPP force field which is combined to the VS algorithm in conjuction with the SMMP software package. A number of proteins are selected from the PDB database to evaluate the performance of the proposed method results of which indicate that the proposed method is comparable to the existing methods.