Motivation: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting. Results: Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes. Availability and implementation: Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: Here we report on recent developments of the map2mod server. It has been designed for validation of protein models created by X-ray data interpretation. It can also be used during the refinement process since it is able to indicate problem regions in the model. Apart from evaluation of model quality, it has an option to remove atoms of side chains, which are not consistent with the maps as well as improperly placed water molecules. There are two additional options: checking the B-factors of atoms in the provided model and comparison of R and Rfree values obtained as the result of refinement with the averages characteristic for the data resolution shell. Availability: The map2mod server is freely available for all user community (). Contact: olga,[email protected]