ArticleJournal of computer-aided molecular design2015
Models of protein-ligand crystal structures: trust, but verify.
Article in Journal of computer-aided molecular design, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Avoiding pitfalls when modelling ligands in macromolecular crystallography.Acta crystallographica. Section D, Structural biology · 2026Article
- Unfreezing structural biology for drug discovery.Nature chemical biology · 2026Review
- Crystal structure of Methanococcus jannaschii dihydroorotase with substrate bound.Acta crystallographica. Section F, Structural biology communications · 2026Article
- Gluebodies Offer a Route To Improve Crystal Reliability and Diversity through Transferable Nanobody Mutations That Introduce Constitutive Close Contacts.ACS central science · 2025Article
- AlphaFold 3 accurately models natural variants ofMicrobiology spectrum · 2025Article
- A high-resolution data set of fatty acid-binding protein structures. III. Unexpectedly high occurrence of wrong ligands.Acta crystallographica. Section D, Structural biology · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Virtual screening: hope, hype, and the fine line in between.Expert opinion on drug discovery · 2025Review
- Recent computational advances in the identification of cryptic binding sites for drug discovery.Bioinformatics advances · 2025Review
- 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
- Article
- The "Doorstop Pocket" In Thioredoxin Reductases─An Unexpected Druggable Regulator of the Catalytic Machinery.Journal of medicinal chemistry · 2024Review
- Controlling the incorporation of fluorinated amino acids in human cells and its structural impact.Protein science : a publication of the Protein Society · 2024Article
- Plant-Based Bioactive Phthalates Derived fromACS omega · 2023Article
- Mining the Protein Data Bank to inspire fragment library design.Frontiers in chemistry · 2023Article
- Serial femtosecond crystallography.Nature reviews. Methods primers · 2022Article
- Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel.International journal of molecular sciences · 2022Article
- The FUSION protein crystallization screen.Journal of applied crystallography · 2022Article
- Simplified quality assessment for small-molecule ligands in the Protein Data Bank.Structure (London, England : 1993) · 2022Article
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2 authors.
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Abstract
X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.
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