Summary
Finding the native structure of a protein starting from its amino acid sequence remains one of the most challenging open problems in bioinformatics and molecular biology. The Protein Structure Prediction (PSP) problem has been tackled from many different directions. The common approach is to cast it in the form of a global single-objective optimization problem using energy functions to evaluate the physical state of the conformations. In this work we reformulate the PSP as a multiobjective optimization problem motivated by the fact that the folded state of a protein is a small ensemble of conformational structures. A 2-objective decomposition of the CHARMM energy function is proposed based on local and nonlocal interactions between atoms, supported by experimental evidence that these objectives are in fact conflicting. A new MOEA algorithm is used to search for (observed) Pareto-optimal sets of conformations with respect to the 2-objective formulation and tested on a large set of medium-size proteins (26-70 residues), with results demonstrating the effectiveness of this approach and providing different measures of protein complexity. Results also point to instances in which the CHARMM energy model suffers from low accuracy owing to the required trade-off between differing objectives in finding “good” conformations.
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Cutello, V., Narzisi, G., Nicosia, G. (2008). Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_5
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