Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F1-ATPase data set and a 4.5 Å resolution SecYEG-SecA complex data set. All of the models were automatically built by the method to Rfree values of between 28.9 and 39.9% and were free from the initial model bias.
Keywords: X-ray crystallography; low resolution; membrane proteins; model bias; multi-protein complexes; multivariate statistics; refinement; single-wavelength anomalous diffraction; structure determination.