<scp>UCSF ChimeraX</scp>: Tools for structure building and analysis

TLDR

Advances in computational tools for atomic model building are enabling accurate models of large molecular assemblies seen in electron microscopy, often at challenging 3‑4 Å resolutions. The paper introduces UCSF ChimeraX methods that leverage machine‑learning predictions, likelihood‑based map fitting, per‑residue error scoring, and community extensions for model building. These methods incorporate machine‑learning structure predictions, likelihood‑based fitting, per‑residue scoring, mutation and post‑translational modification analysis, ligand interaction tools, and community‑developed extensions. ChimeraX is freely available for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.

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.