Algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Protein Threading
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
Parallel evolution strategy on grids for the protein threading problem
Journal of Parallel and Distributed Computing
Efficient non-coding RNA gene searches through classical and evolutionary methods
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Protein homology search using hidden Markov model parameters and genetic algorithms
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
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The biological function of proteins is dependent, to a large extent, on their native three dimensional conformation. Thus, it is important to know the structure of as many proteins as possible. Since experimental methods for structure determination are very tedious, there is a significant effort to calculate the structure of a protein from its linear sequence. Direct methods of calculating structure from sequence are not available yet. Thus, an indirect approach to predict the conformation of protein, called threading, is discussed. In this approach, known structures are used as constraints, to restrict the search for the native conformation. Threading requires finding good alignments between a sequence and a structure, which is a major computational challenge and a practical bottleneck in applying threading procedures. The Genetic Algorithm paradigm, an efficient search method that is based on evolutionary ideas, is used to perform sequence to structure alignments. A proper representation is discussed in which genetic operators can be effectively implemented. The algorithm performance is tested for a set of six sequence/structure pairs. The effects of changing operators and parameters are explored and analyzed.