OPTIMIZATION 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 meth odology 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 indivi dual structures generated by the distance-geometry program, and to ide ntify new lower and upper distance bounds within the original experime ntal restraints in order to restrict the sampling of the metrisation a lgorithm to promising regions of the conformational space. The algorit hm was tested on cyclosporin A, which is notorious for its intrinsic d ifficult sampling properties. A set of 58 distance restraints was empl oyed. It was shown that DG Omega resulted in an improvement of converg ence behaviour as well as sampling properties with respect to the stan dard distance-geometry protocol.