UCSF ChimeraX : Tools for structure building and analysis

UCSF ChimeraX: инструменты для построения и анализа структур
Elaine C. Meng, Thomas D. Goddard, Eric F. Pettersen, John H. Morris, Thomas E. Ferrin, Greg S. Couch, Zachary Pearson
2023-09-29

UCSF ChimeraXlikelihood-based fitting in mapsmachine-learning structure predictionsmodel-building for electron microscopyper-residue scores
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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ChimeraX computes per-residue scores to identify modeling errors in atomic models.
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ChimeraX includes new methods that leverage machine-learning structure predictions to aid atomic model building.
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ChimeraX is freely available for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.
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ChimeraX now integrates several community-developed extensions to expand model-building capabilities.
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ChimeraX offers model-building tools for analyzing mutations, post-translational modifications, and ligand interactions.
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ChimeraX provides likelihood-based fitting of models into electron microscopy maps, improving fit at challenging 3–4 Å resolutions.

UCSF ChimeraX molecular modeling package (model-building and analysis tools)

New model-building and analysis methods including ML-based structure prediction integration, likelihood-based map fitting, per-residue error scoring, and tools for mutations, PTMs, and ligand interactions

Publication Details
Publication Date
2023-09-29
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Authors
Elaine C. Meng
Thomas D. Goddard
Eric F. Pettersen
John H. Morris
Thomas E. Ferrin
Greg S. Couch
Zachary Pearson
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