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Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique topredict the native structure of proteins. However, NMR machines are only ableto report approximate and partial distances between pair of atoms. To build theprotein structure one has to solve the Euclidean distance geometry problemgiven the incomplete interval distance data produced by NMR machines. In thispaper, we propose a new genetic algorithm for solving the Euclidean distancegeometry problem for protein structure prediction given sparse NMR data. Ourgenetic algorithm uses a greedy mutation operator to intensify the search, atwin removal technique for diversification in the population and a randomrestart method to recover stagnation. On a standard set of benchmark dataset,our algorithm significantly outperforms standard genetic algorithms.

GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures