Optimisation of metric matrix embedding by genetic algorithms

To improve the convergence properties of 'embedding' distance geometry, a new approach was developed by combining the distance-geometry methodology with a genetic algorithm. This new approach is called DG-OMEGA (DG omega, optimised metric matrix embedding by genetic algorithms). The genetic algorithm was used to combine well-defined parts of individual structures generated by the distance-geometry program, and to identify new lower and upper distance bounds within the original experimental restraints in order to restrict the sampling of the metrisation algorithm to promising regions of the conformational space. The algorithm was tested on cyclosporin A, which is notorious for its intrinsic difficult sampling properties. A set of 58 distance restraints was employed. It was shown that DG omega resulted in an improvement of convergence behaviour as well as sampling properties with respect to the standard distance-geometry protocol