Parallel processing of chemical information in a local area network--III. Using genetic algorithms for conformational analysis of biomacromolecules.

Multi-dimensional nuclear magnetic resonance experiments are an excellent means of revealing the three-dimensional structure of biomacromolecules in solution. However, the search space in the conformational analysis of biomacromolecules, using multi-dimensional NMR data, is huge and complex. This calls for global optimization techniques with good sampling properties. This paper describes a genetic algorithm that optimizes the fit between (simulated) experimental two-dimensional Nuclear Overhauser Effect spectra and the corresponding calculated spectra for trial structures. This is a very computational intensive procedure. Speed-up of performance is achieved by parallelizing the algorithm, i.e. creating small subpopulations of trial structures, each of which can be processed on different processors. Good sampling behavior is obtained by initializing each subpopulation with its own random seed and the introduction of a migration operator. The latter replaces the best performing individual from one subpopulation with the worst performing individual from another subpopulation after a predetermined number of generations. A parallel genetic algorithm for the conformational analysis of nucleic acids is developed using the software package HYDRA. It is demonstrated that, for the data sets used in the study, a considerable reduction in computation time is obtained for the parallel genetic algorithm as compared to a sequential implementation, while the same optimal solutions are found.