Chimera visualisation plugin

The Chimera visualization plugin is an outlier detection model for validating protein structures determined by cryo-electron microscopy (cryo-EM). The model aims to identify outliers introduced during the modeling process due to the low quality of cryo-EM density maps. The current protein model validation system needs more specific features for cryo-EM proteins, making it insufficient for identifying outliers in these structures.

The proposed model uses a high-resolution X-ray dataset (<1.5 Å) as a reference. It collects and saves the residues' distal block distance, side-chain length, phi, psi, and first chi angle in the reference dataset as a histogram-based outlier score (HBOS) database. The HBOS values of residues in target cryo-EM proteins can be read from this database, and residues with an HBOS value greater than ten are labeled as outliers by default.

The model was tested on four datasets containing proteins derived from cryo-EM density maps. Based on the proposed model, a visualization assistant tool, a protein visualization platform, was designed for Chimera.

Topic

Imaging;Machine learning;Protein folding, stability and design

Detail

  • Operation: Protein geometry calculation;Protein structure validation;Chimera detection

  • Software interface: Command-line user interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: NIH.

  • Input: -

  • Output: -

  • Contact: Lin Chen lichen@valdosta.edu

  • Collection: -

  • Maturity: -

Publications

  • A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform.
  • Chen L, et al. A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform. A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform. 2019; 6:(unknown pages). doi: 10.3390/medicines6030086
  • https://doi.org/10.3390/MEDICINES6030086
  • PMID: 31390767
  • PMC: PMC6789601

Download and documentation


< Back to DB search