Increasing the radius of convergence of molecular replacement by density- and energy-guided optimization
The crystallographic phase problem refers to the fact that when X-ray diffraction data is collected, additional data -- the "phases" -- are needed to construct a map of the protein's density. Molecular replacement (MR) is a method in which a previously solved protein structure (the "template") is used to fill in this missing experimental information for a target protein. The method generally works, assuming template and target are reasonably similar. However, when the template and target have less than 30% sequence identity, molecular replacement often will fail.
My manuscript shows that the crystallographic phase problem can be solved using distant evolutionary relationships by combining algorithms for protein structure modelling with those developed for crystallographic structure determination. Integrating Rosetta structure modelling with Autobuild chain tracing yielded high-resolution structures for 8 of 13 X-ray diffraction data sets that could not be solved in the laboratories of expert crystallographers, and that remained unsolved after application of an extensive array of alternative approaches. The method shows a 50% success rate in cases where templates with 16-30% sequence identity and 70%+ coverage are available.