ArticleActa crystallographica. Section D, Structural biology2017
Estimation of the protein-ligand interaction energy for model building and validation.
Article in Acta crystallographica. Section D, Structural biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Labeled dataset of X-ray protein ligand images in 3D point cloud and validated deep learning models.Scientific data · 2025Article
- Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies.Computational and structural biotechnology journal · 2022Article
- Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality.Structure (London, England : 1993) · 2021Article
- 'International journal of molecular sciences · 2021Article
- Validation of ligands in macromolecular structures determined by X-ray crystallography.Acta crystallographica. Section D, Structural biology · 2018Article
- New developments in crystallography: exploring its technology, methods and scope in the molecular biosciences.Bioscience reports · 2017Review
- Twilight reloaded: the peptide experience.Acta crystallographica. Section D, Structural biology · 2017Article
- Ligand fitting with CCP4.Acta crystallographica. Section D, Structural biology · 2017Article
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3 authors.
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Abstract
Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein-ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site with ARP/wARP. The applicability of the method to the validation of protein-ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.
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