In order to understand the function of a protein on a molecular level, the three-dimensional structure of the protein is often essential. X-ray crystallography is the primary method of protein structure determination. Despite recent rapid improvements in the field, the process of de novo structure determination may still take many months or years or may not be successful at all. The research described in this thesis is aimed at the improvement of the computational methods used for X-ray crystallography automated model building and refinement of macromolecular structures. A probabilistic approach is proposed in which a multivariate likelihood function that directly takes into account information from X-ray experiments is derived and shown to improve the process of protein model building and refinement.NWOUBL - phd migration 201
