coot

Best Practices for Model-Building Tools and Refinement

Best practices for protein structure refinement and validation in Coot. Use when performing (1) Residue refinement operations, (2) Model building and fitting, (3) Rotamer fixing, (4) Scripted/automated refinement workflows, (5) Validation and correlation checking.

Comprehensive structure validation combining model-to-map analysis and unmodeled density detection

API documentation to be loaded at startup - when starting a Coot session, immediately call get_function_descriptions() with the functions listed in this skill.

Best Practices for using Coot MCP

Using Density-Fit Correlations in Coot

Best practices for creating publication-quality molecular graphics figures in Coot using user-defined colors, ribbons, and molecular representations

Query the PDBe (Protein Data Bank in Europe) REST API and Solr search API from within Coot to access structure metadata, validation data, revision history, search capabilities, and download coordinate files

Create interactive inline Chart.js graphs directly in the chat from live Coot data. Use this skill whenever the user asks to plot, graph, chart, or visualise any per-residue data from Coot — B-factors, density correlations, Ramachandran probabilities, rotamer scores, or any other per-residue metric. Also use when the user asks to overlay secondary structure on a graph, or to compare metrics across chains. Prefer this approach over any file-based graphing (e.g. Pygal) — it is faster, interactive, and renders inline in the conversation.

How to handle Unmodelled Density Blobs

NCS Reference Guidance

RDKit molecular manipulation and visualization within Coot's Python environment. Use when working with Coot and need to (1) Create RDKit molecules from Coot monomers, (2) Modify molecular structures (e.g., atom substitution), (3) Generate 2D chemical structure diagrams, (4) Perform cheminformatics operations on ligands or small molecules loaded in Coot.