Study On Models Of Protein Structure Prediction

Exponentially exploding bioinformatics data has brought a new multidisciplinary research area-bioinformatics. One of major research issues in bioinformatics is on protein structure prediction based on protein sequence. This interdisciplinary field begs for knowledge of mathematics, computer science, information science, physics, system science, management science as well as biology. Concerning the problem of protein structure prediction, some new models and improved models are given in this dissertation.Graph theory plays a key role in the field of prediction of protein structure. In this dissertation, a method based on the shortest path of a graph is proposed. Three vertices of the graph give a possible secondary structure of a residue, and each edge of the graph is assigned a weight by a function. This path equated the corrected secondary structure. By this method, Several groups of proteins is tested and the result showed that this method was feasible. Finally the selection of parameter is discussed.DNA computing is a new computer model. This dissertation introduces DNA computing in proteins structure prediction. Each possible conformation of a residue in an amino acid sequence is represented using the notion of a node in a graph. Each node is given a weight based on the degree of the interaction between its side-chain atoms and the local main-chain atoms. Proteins structure prediction problem is mapped to find the maximal sets of completely connected nodes (cliques) in a graph and then using DNA computing model can find the maximal cliques.Probabilistic graphic model is an effective protein structure prediction model. By introducing a hidden state variable, a hiden Conditional Random Fields (HCRFs) is builded and used in the problem of protein structure prediction. A method of constructing the model and the algorithms is given to train and decode the model and use the model to predict the second structure of a famous protein dataset (CB513). Finally the results are compared with some other methods.An important problem in protein structure prediction is the correct location of disulfide bonding in proteins. The location of disulfide bonding can strongly reduce the search in the conformational space of protein structure. Therefore the correct prediction of the disulfide bonding starting from the protein residue sequence may also help in predicting its 3D structure. In this paper the LVQ artificial neural network method is applied to predict the disulfide bonding of protein structure. The local sequence arrangement of cysteine is of great significance to the disulfide bonding. Therefore the disulfide bonding can be predicted by its primary structure. This method was used to predict disulfide bonding in protein structure and a fine result was got.HP model is a simplified model of protein structure prediction.20 kinds of protein residues is classed into four groups. A protein sequence is converted to a new sequence including four alphabets. And then by searching the lowest energy of the new sequence we construct a protein structure prediction model. Simulated annealing algorithm is used for this model and the result gets the lower energy than using the HP model. The model can extend in predicting protein structure in 3D.