Abstract

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 .

Keywords

Computer scienceChemistryComputational biologyBiology

MeSH Terms

SoftwareCryoelectron MicroscopyLikelihood FunctionsModelsMolecularMicroscopyElectronProtein Conformation

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Publication Info

Year
2023
Type
article
Volume
32
Issue
11
Pages
e4792-e4792
Citations
2914
Access
Closed

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Cite This

Elaine C. Meng, Thomas D. Goddard, Eric F. Pettersen et al. (2023). <scp>UCSF ChimeraX</scp>: Tools for structure building and analysis. Protein Science , 32 (11) , e4792-e4792. https://doi.org/10.1002/pro.4792

Identifiers

DOI
10.1002/pro.4792
PMID
37774136
PMCID
PMC10588335

Data Quality

Data completeness: 90%