Structural highlights
Publication Abstract from PubMed
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: Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information: Supplementary data are available at Bioinformatics online.
Automatic Recognition of Ligands in Electron Density by Machine Learning.,Kowiel M, Brzezinski D, Porebski PJ, Shabalin IG, Jaskolski M, Minor W Bioinformatics. 2018 Jul 17. pii: 5055122. doi: 10.1093/bioinformatics/bty626. PMID:30016407[1]
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.
References
- ↑ Kowiel M, Brzezinski D, Porebski PJ, Shabalin IG, Jaskolski M, Minor W. Automatic Recognition of Ligands in Electron Density by Machine Learning. Bioinformatics. 2018 Jul 17. pii: 5055122. doi: 10.1093/bioinformatics/bty626. PMID:30016407 doi:https://dx.doi.org/10.1093/bioinformatics/bty626
