ArticleActa crystallographica. Section D, Structural biology2020
Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix.
Article in Acta crystallographica. Section D, Structural biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 43 citations in OpenAlex.
- KNexPHENIX: A PHENIX-Based Workflow for Improving Cryo-EM and Crystallographic Structural Models.Journal of chemical information and modeling · 2026Article
- Validated ligand geometries for macromolecular refinement restraints and molecular-mechanics force fields.Acta crystallographica. Section D, Structural biology · 2026Article
- The Untangle Challenge for accurate ensemble models.bioRxiv : the preprint server for biology · 2026Article
- New targets and procedures for validating the valence geometry of nucleic acid structures.Nucleic acids research · 2026Article
- AQuaRef: machine learning accelerated quantum refinement of protein structures.Nature communications · 2025Article
- Semi-automated modeling of reaction states in time-resolved serial femtosecond crystallography using molecular dynamics sampled conformations.Structural dynamics (Melville, N.Y.) · 2025Article
- Validated ligand geometries for macromolecular refinement restraints and molecular mechanics force fields.bioRxiv : the preprint server for biology · 2025Article
- Deciphering the structural and dynamic effects of SHP2-E76 mutations: mechanistic insights into oncogenic activation.BMC chemistry · 2025Article
- StructureNet: Physics-Informed Hybridized Deep Learning Framework for Protein-Ligand Binding Affinity Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- If you cannot see it, is it still there?Journal of applied crystallography · 2025Article
- SFCalculator: connecting deep generative models and crystallography.bioRxiv : the preprint server for biology · 2025Article
- The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.Acta crystallographica. Section D, Structural biology · 2023Article
- Chemical features and machine learning assisted predictions of protein-ligand short hydrogen bonds.Scientific reports · 2023Article
- Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds.Research square · 2023Article
- In situ ligand restraints from quantum-mechanical methods.Acta crystallographica. Section D, Structural biology · 2023Article
- Chromogranin B (CHGB) is dimorphic and responsible for dominant anion channels delivered to cell surface via regulated secretion.Frontiers in molecular neuroscience · 2023Article
- Cryo-EM structure of ex vivo fibrils associated with extreme AA amyloidosis prevalence in a cat shelter.Nature communications · 2022Article
- Article
- Effective prediction of short hydrogen bonds in proteins via machine learning method.Scientific reports · 2022Article
- Integral equation models for solvent in macromolecular crystals.The Journal of chemical physics · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing.
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