Protein Science· 2023Q1
<scp>UCSF ChimeraX</scp>: Tools for structure building and analysis
- 4,277citations
- Q1SCImago
- 2023year
Short summary
UCSF ChimeraX has introduced new computational tools that leverage machine learning for atomic model building of large molecular assemblies, improving accuracy at resolutions of 3-4 Å.
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Key points
- New UCSF ChimeraX features integrate machine-learning for atomic model building.
- Tools offer likelihood-based fitting and per-residue scoring to identify modeling errors.
- Enhanced analysis capabilities for mutations, post-translational modifications, and ligand interactions.
- Improved accuracy for molecular assemblies at 3-4 Å resolution from electron microscopy.
AI-generated from the title and abstract; the full text is not read.
Abstract
Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.
The authors' abstract, as published at the source. Protein Science, 2023 · DOI ↗
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Field: Software
SoftwareComputer Science