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UCSF ChimeraX: Tools for structure building and analysis.

Elaine C Meng¹, Thomas D Goddard¹, Eric F Pettersen¹

¹Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.

Protein Science : a Publication of the Protein Society

|September 29, 2023

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New UCSF ChimeraX software features enhance atomic model building for large molecular assemblies using machine learning. These tools improve accuracy in electron microscopy data analysis and identify potential modeling errors.

Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Accurate atomic models of large molecular assemblies are crucial for understanding biological function.
Electron microscopy (EM) provides high-resolution structural data, but model building can be challenging.

Purpose of the Study:

To introduce new computational tools within UCSF ChimeraX for improved atomic model building in EM.
To enhance the accuracy and reliability of molecular models derived from EM data.

Main Methods:

Utilizing machine-learning structure predictions for atomic model generation.
Implementing likelihood-based fitting of models into experimental electron microscopy maps.
Developing per-residue scoring functions to detect and quantify modeling errors.

Keywords:

AlphaFold ChimeraX atomic model building cryo-electron microscopy protein structure prediction refinement

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Main Results:

Demonstrated enhanced capabilities in building accurate atomic models for large molecular assemblies.
Successfully identified and analyzed potential modeling errors using new scoring methods.
Integrated tools for analyzing mutations, post-translational modifications, and ligand interactions.

Conclusions:

UCSF ChimeraX offers advanced, integrated tools for high-resolution atomic model building from EM data.
The new methods facilitate more reliable interpretation of structural biology data.
These advancements aid in understanding complex biological systems at the molecular level.