ArticleActa crystallographica. Section D, Biological crystallography2014
Automated identification of crystallographic ligands using sparse-density representations.
Article in Acta crystallographica. Section D, Biological crystallography, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Denoising and iterative phase recovery reveal low-occupancy populations in protein crystals.Communications biology · 2025Article
- Labeled dataset of X-ray protein ligand images in 3D point cloud and validated deep learning models.Scientific data · 2025Article
- Ligand identification in CryoEM and X-ray maps using deep learning.Bioinformatics (Oxford, England) · 2024Article
- Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Crystal polymorphism in fragment-based lead discovery of ligands of the catalytic domain of UGGT, the glycoprotein folding quality control checkpoint.Frontiers in molecular biosciences · 2022Article
- Recognizing and validating ligands with CheckMyBlob.Nucleic acids research · 2021Article
- Crystal structures ofIUCrJ · 2021Article
- Automatic recognition of ligands in electron density by machine learning.Bioinformatics (Oxford, England) · 2019Article
- Crystal structures of the extracellular domains of the CrRLK1L receptor-like kinases ANXUR1 and ANXUR2.Protein science : a publication of the Protein Society · 2018Article
- A distance geometry-based description and validation of protein main-chain conformation.IUCrJ · 2017Article
- Estimation of the protein-ligand interaction energy for model building and validation.Acta crystallographica. Section D, Structural biology · 2017Article
- Models of protein-ligand crystal structures: trust, but verify.Journal of computer-aided molecular design · 2015Article
- The solvent component of macromolecular crystals.Acta crystallographica. Section D, Biological crystallography · 2015Review
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Authors and funding
2 authors.
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Abstract
A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination.
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