Accelerating ab initio phasing with de novo models.

Ab initio phasing is one of the remaining challenges in protein crystallography. Recent progress in computational structure prediction has enabled the generation of de novo models with high enough accuracy to solve the phase problem ab initio. This `ab initio phasing with de novo models' method first generates a huge number of de novo models and then selects some lowest energy models to solve the phase problem using molecular replacement. The amount of CPU time required is huge even for small proteins and this has limited the utility of this method. Here, an approach is described that significantly reduces the computing time required to perform ab initio phasing with de novo models. Instead of performing molecular replacement after the completion of all models, molecular replacement is initiated during the course of each simulation. The approach principally focuses on avoiding the refinement of the best and the worst models and terminating the entire simulation early once suitable models for phasing have been obtained. In a benchmark data set of 20 proteins, this method is over two orders of magnitude faster than the conventional approach. It was observed that in most cases molecular-replacement solutions were determined soon after the coarse-grained models were turned into full-atom representations. It was also found that all-atom refinement was hardly able to change the models sufficiently to enable successful molecular replacement if the coarse-grained models were not very close to the native structure. Therefore, it remains critical to generate good-quality coarse-grained models to enable subsequent all-atom refinement for successful ab initio phasing by molecular replacement.