Research on Metaheuristic Approach to De Novo Prediction of Protein Structure
|Course||Applied Computer Technology|
|Keywords||Protein Structure De nove Prediction Metaheuristic Parallel|
Proteins with certain structure are executants of the material life function. Pre-diction of protein structure is quite significant in the context of Gene data explosionbut structure parsing with high cost and low e?ciency. De novo prediction of pro-tein structure with no structure template is a significant research content with hightechnical di?culty and great practical significance.Prediction of protein structure is essentially a combination optimization problemin computer view. And this problem with an enormous search space and complicatedconstraining mechanism is a major challenge in computer field. We summarized denovo prediction of protein structure and parallel metaheuristics in this dissertation.And the main content of this dissertation includes search space, search scheme, andclustering scheme.1. The research on search space of structure prediction. The fragment structureand its building method for both backbone and side chains are summarized in this dis-sertation. A four-level model for building rotamer library based on Dynamic BayesianNetworks is proposed. The relation of backbone with four side chain torsion anglesis considered in this model, so it shows an obvious ratiocination hierarchy, and thismodel is in accord with the biology characteristic of protein molecule. It holds onlyone unknown parameter in every level, so the complexity of this model is reduced,and the problem caused by parse data is solved to a certain extent for the same scaleof training data. Experiment results show that this model obtain models with highquality. Moreover, assessment of rotamer library with ultra conformation and randomconformation is proposed. Experiment on CASP9 FM targets shows that this methodis effective.2. The research on parallel metaheuristics. A parallel metaheuristic strategywhich main characteristics are task parsing and experience feedback is proposed basedon metaheuristics such as ACO. And a parallel metaheuristic search frame with fusingdi?erent energy functions or search strategies is proposed for solving the problems of optimization target is hard to quantify and solution structure is extraordinary complex.And the task distribute strategy is designed for prediction of GPCR. Further more,algorithms of prediction of backbone and side chain are designed. The search schemein one ant colony, the solution construction, the local search, and the parallel distributestrategy in prediction of backbone are implemented. Experiments on data sets of 16small proteins provided by a paper on Science and FM targets in CASP8 show thatthe method proposed in our dissertation had got a considerable e?ect.3. The research on protein structures clustering. It includes two aspects. First,an exemplar selection algorithm for clustering protein structures is proposed basedon the widely-used quality threshold and a?nity propagation algorithms in proteinstructure prediction. The ability to find the best conformation is enhanced based onstatistical information, and the algorithm does not depend on experience parameter.Second, a scheme of optimizing the similarity matrix based on energy is proposed. Itcan form good basis of clustering. Experiments on authoritative data sets show thatthe methods proposed in our dissertation can enhance the performance of clustering,and find the closer decoys to native structure.The major contribution of this dissertation includes: the proposal of the four-levelmodel for building rotamer library, the relation of backbone with four torsion anglesis considered, and the problem caused by parse data is solved to a certain extent; theproposal of the parallel metaheuristic search strategy fitting for de novo prediction ofprotein structure, it has got a considerable e?ect in backbone prediction; the proposalof an exemplar selection algorithm for clustering protein structures and the schemefor optimization of similarity distribution, they can improve the correctness of optimalstructure selection. Experiments show that this work will exert positive e?ects on denovo prediction of protein structure, and exhibits a great reference value to the futurecorrelative research.