Compute per-residue and region-wide density-fit correlations in Coot.
npx skills add https://github.com/pemsley/coot --skill coot-correlations
Please help me install this Agent Skill. Skill: coot-correlations Source: https://github.com/pemsley/coot/tree/main/mcp/docs/skills/correlations Command: npx skills add https://github.com/pemsley/coot --skill coot-correlations
This Skill helps researchers validate structural models by quantifying how well the model fits electron density maps using density-fit correlations within Coot, enabling rapid identification of regions with poor density support.
Load a model and map in Coot. Then run the map_to_model_correlation_stats_per_residue_range_py function with appropriate parameters to obtain per-residue correlation statistics, and inspect the results to identify residues with poor fits. Use the map_to_model_correlation_py and map_to_model_correlation_stats_py functions to further investigate overall and local fit quality, and apply refinements as needed.
High-intent search queries and answers about installing and using this skill.
Model-to-map correlation is computed per-residue in Coot using the map_to_model_correlation_stats_per_residue_range_py function, which returns all-atom and side-chain correlation statistics for each residue across a specified chain and residue range.
Density-fit correlation quantifies how well a molecular model fits electron density maps, enabling rapid identification of residues and regions with poor density support that require further refinement.
Yes, you can assess correlations for specified residue ranges or neighboring regions using map_to_model_correlation_py and map_to_model_correlation_stats_py functions to investigate overall and local fit quality.
Yes, density-fit correlation analysis requires a loaded model and map in Coot, specified by imol and imol_map, along with chain and residue specs, and access to Coot's Python API functions.
Identify poorly fitting regions by computing per-residue density-fit correlations with map_to_model_correlation_stats_per_residue_range_py, then inspect the results to pinpoint residues with low correlation values for targeted refinement.
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