Molecular Replacement Using Genetic Algorithms
Abstract
A new molecular replacement (MR) strategy is introduced which features a continuous transform and a genetic algorithm (GA) for search optimization. This strategy uses a GA to simultaneously search the rotational and translational parameters of a test model while maximizing the correlation coefficient between the observed and calculated diffraction data. This has distinct advantages over conventional MR strategies which require a cross-rotation signal. An important feature of this method is its capability to simultaneously search the overall rotation/translation of the test model in the unit cell while refining the relative orientation/position of internal subdomains. This identifies molecular replacement solutions which would otherwise be completely missed using just a static model, and greatly improve the signal-to-noise contrast.