Abstract
The prediction of a protein’s structure from its amino-acid sequence is one of the most important problems in computational biology. In the current work, we focus on a widely studied abstraction of this problem, the 2-dimensional hydrophobic-polar (2D HP) protein folding problem. We present an improvedv ersion of our recently proposed Ant Colony Optimisation (ACO) algorithm for this NP-hardcom binatorial problem and demonstrate its ability to solve standard benchmark instances substantially better than the original algorithm; the performance of our new algorithm is comparable with state-of-the-art Evolutionary and Mon te Carlo algorithms for this problem. The improvements over our previous ACO algorithm include long range moves that allows us to perform modification of the protein at high densities, the use of improving ants, ands elective local search. Overall, the results presentedhere establish our new ACO algorithm for 2D HP protein folding as a state-of-the-art methodf or this highly relevant problem from bioinformatics.
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Shmygelska, A., Hoos, H.H. (2003). An Improved Ant Colony Optimisation Algorithm for the 2D HP Protein Folding Problem. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_30
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DOI: https://doi.org/10.1007/3-540-44886-1_30
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