The present invention concerns the determination and evaluation of the crystal structure of autolysin E (AtlE) of Staphylococcus aureus (S. aureus), or a crystallizable fragment of AtlE, a method for producing a crystal of AtlE and the respective crystallization kit, and its use in a method for screening an inhibitor of the N-acetylglucosaminidase activity of AtlE, for obtaining atomic spatial relationship data, and for identifying a binding compound of AtlE, e.g. by in silico screening.
F.05 Ability to launch new technological development cycle
In the early 90s in the absence of rigorous geometric restrains the structure validation was first introduced in the reciprocal space with R-free. Nowadays, however, over fitting can be controlled in real space by the rigorous use of geometric restraints and validation tools. In refinement the practice was established that the deviations from ideal geometry are defined as a target used to scale crystallographic energy terms. Hence, over fitting of models which leads to severe deviations from ideal geometry is not really possible anymore. Hand in hand with the progress of tools delivering better models also the amount of data used for the TEST set was gradually decreasing from the initial 10% and more to 5% and less. Its portion is now practically limited by the request for statistical reliability of the Maximum Likelihood (ML) Cross Validation parameters. The use of the TEST set concept has its limitations: it does not allow the use of all data in refinement and map calculations, the presence of NCS makes it impossible to decouple the independence of TEST set reflections from the rest of the data, and the exchange of the TEST set can result in a considerably different gap between R-work and R-free. To overcome the limitations of the R-free concept we developed an approach that uses the WORK set to calculate the phase error estimates in the ML refinement from simulating the model errors via the random displacement of atomic coordinates. We call it ML Free Kick refinement as it uses the ML formulation of target function and is based on the idea to free the model from the model bias imposed by the chemical energy restrains used in refinement. This approach of calculation of error estimates is superior to the cross validation approach: it reduces the phase error and increases the accuracy of molecular models, is more robust, provides clearer maps, and may use a smaller portion of data for the TEST set for calculation of the Rfree or leave it out completely. Praznikar, J. & Turk, D. (2014) Free kick instead of cross-validation in maximum-likelihood refinement of macromolecular crystal structures. Acta Cryst. D70, 3124-3134.
B.04 Guest lecture