An approach to automate protein-ligand crystallography is presented, with the aim of increasing the number of structures available to structure-based drug design. The methods we propose deal with the automatic interpretation of diffraction data for targets with known protein structures, and provide easy access to the results. Central to the system is a novel procedure that fully automates the placement of ligands into electron density maps. Automation provides an objective way to structure solution, whereas manual placement can be rather subjective, especially for data of low to medium resolution. Ligands are placed by docking into electron density, whilst taking care of protein-ligand interactions. The ligand fitting procedure has been validated on both public domain and in-house examples. Some of the latter deal with cocktails of low-molecular weight compounds, as used in fragment-based drug discovery by crystallography. For such library-screening experiments we show that the method can automatically identify which of the compounds from a cocktail is bound.
The validation of structural models obtained by macromolecular X-ray crystallography against experimental diffraction data, whether before deposition into the PDB or after, is typically carried out exclusively against the merged data that are eventually archived along with the atomic coordinates. It is shown here that the availability of unmerged reflection data enables valuable additional analyses to be performed that yield improvements in the final models, and tools are presented to implement them, together with examples of the results to which they give access. The first example is the automatic identification and removal of image ranges affected by loss of crystal centering or by excessive decay of the diffraction pattern as a result of radiation damage. The second example is the `reflection-auditing' process, whereby individual merged data items showing especially poor agreement with model predictions during refinement are investigated thanks to the specific metadata (such as image number and detector position) that are available for the corresponding unmerged data, potentially revealing previously undiagnosed instrumental, experimental or processing problems. The third example is the calculation of so-called F(early) - F(late) maps from carefully selected subsets of unmerged amplitude data, which can not only highlight the location and extent of radiation damage but can also provide guidance towards suitable fine-grained parametrizations to model the localized effects of such damage.
Neurofibromatosis type 2 (NF2) is a tumor-forming disease of the nervous system caused by deletion or by loss-of-function mutations in NF2, encoding the tumor suppressing protein neurofibromin 2 (also known as schwannomin or merlin). Neurofibromin 2 is a member of the ezrin, radixin, moesin (ERM) family of proteins regulating the cytoskeleton and cell signaling. The correlation of the tumor-suppressive function and conformation (open or closed) of neurofibromin 2 has been subject to much speculation, often based on extrapolation from other ERM proteins, and controversy. Here we show that lipid binding results in the open conformation of neurofibromin 2 and that lipid binding is necessary for inhibiting cell proliferation. Collectively, our results provide a mechanism in which the open conformation is unambiguously correlated with lipid binding and localization to the membrane, which are critical for the tumor-suppressive function of neurofibromin 2, thus finally reconciling the long-standing conformation and function debate.
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