Crystallographic method development
In our research, we continuously strive to improve crystallographic (refinement) methods to get the most out of our crystals. With increasing automation of synchrotron beamlines, it is now possible to collect more and more datasets from one or many crystals of a protein. We are currently developing methods to take advantage of these repeated observations, improve our models and help to identify new structural features.
We have developed a method for determining biomacromolecular structures from X-ray diffraction data that accounts for the flexibility or dynamics of the molecules. This method, called ensemble refinement, generates an dynamic ensemble model containing multiple structures and is available as part of the PHENIX software suite.
We developed an N-particle method called Conditional Optimization that allows the incorporation of extensive prior geometrical data into protein-structure determination before a reliable model can be constructed. For automated model building this method yielded models of comparable phase quality to those obtained from the commonly used programs ARP/wARP and RESOLVE. For ab initio phasing we demonstrated that the method works successfully on theoretical data, but for real data is currently too slow for practical use.