A Memetic Algorithm for 3-D Protein Structure Prediction Problem
| Autor: | Leonardo de Lima Corrêa, Mario Inostroza-Ponta, Bruno Borguesan, Márcio Dorn, Camilo Farfan |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Mathematical optimization business.industry Computer science Applied Mathematics Crossover Evolutionary algorithm Protein structure prediction 03 medical and health sciences 030104 developmental biology Problem domain Simulated annealing Genetics Memetic algorithm Local search (optimization) business Metaheuristic Biotechnology |
| Zdroj: | IEEE/ACM transactions on computational biology and bioinformatics. 15(3) |
| ISSN: | 1557-9964 |
| Popis: | Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem. It takes advantage of structural knowledge stored in the Protein Data Bank , by using an Angle Probability List that helps to reduce the search space and to guide the search strategy. The proposed algorithm was tested on 19 protein sequences of amino acid residues, and the results show the ability of the algorithm to find native-like protein structures. Experimental results have revealed that the proposed algorithm can find good solutions regarding root-mean-square deviation and global distance total score test in comparison with the experimental protein structures. We also show that our results are comparable in terms of folding organization with state-of-the-art prediction methods, corroborating the effectiveness of our proposal. |
| Databáze: | OpenAIRE |
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