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Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends

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Computational Intelligence in Biomedicine and Bioinformatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 151))

Abstract

Proteins are complex macromolecules that perform vital functions in all living beings. They are composed of a chain of amino acids. The biological function of a protein is determined by the way it is folded into a specific tri-dimensional structure, known as native conformation. Understanding how proteins fold is of great importance to Biology, Biochemistry and Medicine. Considering the full analytic atomic model of a protein, it is still not possible to determine the exact tri-dimensional structure of real-world proteins, even with the most powerful computational resources. To reduce the computational complexity of the analytic model, many simplified models have been proposed. Even the simplest one, the bi-dimensional Hydrophobic-Polar (2D-HP) model (see Sect. 12.2.2), was proved to be intractable due to its NP-completeness. The current approach for studying the structure of proteins is the use of heuristic methods that, however, do not guarantee the optimal solution. Evolutionary computation techniques have been proved to be efficient for many engineering and computer science problems. This is also the case of unveiling the structure of proteins using simple lattice models.

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Lopes, H.S. (2008). Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_12

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  • DOI: https://doi.org/10.1007/978-3-540-70778-3_12

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