- Published
- 2023-09-29
- Venue
- Protein Science, vol. 32, no. 11, pp. e4792
- Cited by
- 4,486
- Open access
- Yes, hybrid
- DOI
- 10.1002/pro.4792
- PubMed
- 37774136
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.
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