Constraint-based evolutionary local search for protein structures with secondary motifs
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On-lattice protein structure prediction with empirical energy minimisation has drawn significant research effort. However, energy minimisation with free-modelling not necessarily leads to structures that are similar to the native structure of the given protein. In this paper, we show that energy minimisation has a positive correlation with structural similarity measures if we consider secondary motifs. We then present a constraint-based evolutionary local search framework for on-lattice protein structure prediction using secondary structural information. We approximate secondary motifs such as a-helix and ߭strands on the lattice and propose a set of neighbourhood generation operators that respect those motifs. Our experimental results show significant improvement over the state-of-the-art methods in terms of similarity with the native structures determined by laboratory methods.
13th Pacific Rim International Conference on Artificial Intelligence Proceedings
Artificial Intelligence and Image Processing not elsewhere classified