Servalcat

Servalcat refines atomic models and computes weighted Fo-Fc difference maps to support structure refinement and validation in crystallography and single-particle cryo-electron microscopy (Cryo-EM).

Key Features:

• Integration with REFMAC5 (CCP4): Uses REFMAC5 to perform atomic coordinate refinement.
• Weighted Fo-Fc Bayesian maps: Computes weighted Fo-Fc difference maps based on Bayesian statistical principles.
• Real-space map support: Produces maps suitable for manual and automatic model building in real space.
• Hydrogen-density enhancement: Enhances visualization of weak features including hydrogen densities, which can become visible around 1.8 Å resolution.
• Symmetry-constrained refinement: Supports refinement of asymmetric unit models under point group symmetry constraints used during map reconstruction.
• Python implementation: Implemented as a Python package.

Scientific Applications:

• Cryo-EM single-particle analysis: Refinement and validation of atomic models derived from single-particle reconstructions in cryo-electron microscopy.
• Crystallography: Model refinement, Fo-Fc map calculation, and validation for X-ray crystallography.
• Real-space model building: Generation of maps for manual and automated real-space model building workflows.
• Hydrogen detection: Detection and analysis of hydrogen densities in high-resolution maps.
• Symmetry-aware refinement: Refinement of asymmetric unit models consistent with point group symmetries applied during map reconstruction.

Methodology:

Performs atomic coordinate refinement by integrating with REFMAC5 (CCP4), computes weighted Fo-Fc difference maps using Bayesian statistical principles, provides maps for manual and automatic real-space model building, and supports refinement under point group symmetry constraints.