Bringing validation information closer to the user
|Year of publication
|Appeared in Conference without Proceedings
|MU Faculty or unit
|Universal availability of biomacromolecular structural data has gradually changed life sciences. Various databases, the most prominent being the Protein Data Bank (PDB), enable access to plethora of published structures. Unfortunately, questions regarding quality of structure models have increased in importance in recent years. Therefore, all new structures are validated at the time of their submission to PDB. Here, we show how values of available validation metrics can be combined into an overall score that enables ranking of macromolecular structures and their domains in search results. This solution brings validation information closer to the general scientific community. A big challenge of crystallographic studies is how to correctly interpret electron density that is present in the binding site. It either represents an expected ligand, or just solvent molecules. As a result, ligand model quality in the PDB database was and still remains a concerning matter. That is why several ligand validation methods have been integrated into the PDB validation pipeline. Here, we describe these methods, along with our finding that the currently used LLDF metric can give misleading results.