phenix

Orchestrate protein structure determination and refinement with X-ray crystallography and cryo-EM data.

14|11|Updated Jan 15, 2026

Search Tags:#refinement #protein structure #alphafold #phenix #structural biology #cryo-em #x-ray crystallography

Ready to run

npx skills add https://github.com/kbaseincubator/BERIL-research-observatory --skill phenix-kbaseincubator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: phenix
Source: https://github.com/kbaseincubator/BERIL-research-observatory/tree/main/.claude/skills/phenix
Command: npx skills add https://github.com/kbaseincubator/BERIL-research-observatory --skill phenix-kbaseincubator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the complex and time-consuming process of determining and refining protein structures from experimental data (X-ray crystallography, cryo-EM), leveraging AI predictions like AlphaFold.

Core Features & Use Cases

Automated Workflows: Orchestrates multi-step pipelines for structure determination.
AI Integration: Seamlessly incorporates AlphaFold predictions for molecular replacement and model building.
Refinement & Validation: Manages iterative refinement cycles and provides detailed quality assessments using MolProbity.
Visualization Scripting: Generates scripts for Coot, PyMOL, and ChimeraX to aid in manual model inspection and figure creation.
Use Case: A researcher has collected cryo-EM data and wants to build an atomic model. They can use this Skill to process the data, dock an AlphaFold model, perform real-space refinement, validate the results, and generate visualization scripts for review.

Quick Start

Use the phenix skill to start a new structural biology project.

Frequently Asked Questions about phenix

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I refine protein structures using AlphaFold predictions and cryo-EM data?▼

To refine protein structures, this solution integrates AlphaFold predictions for molecular replacement and manages real-space refinement using your cryo-EM data. It orchestrates iterative refinement cycles to build and validate atomic models.

What is the best way to automate X-ray crystallography structure determination workflows?▼

Automating X-ray crystallography workflows involves orchestrating multi-step pipelines for structure determination and iterative refinement. This approach manages cycles with tools like phenix.refine and performs validation using MolProbity for comprehensive quality assessment.

Can I generate visualization scripts for Coot and PyMOL directly from refined protein structures?▼

Yes, you can generate visualization scripts for Coot, PyMOL, and ChimeraX directly from refined protein structures. These scripts support manual model inspection and streamline the creation of publication-ready figures.

Does this structural biology workflow require manual model validation after real-space refinement?▼

While real-space refinement is automated, manual model validation is supported. The workflow performs comprehensive automated quality assessments using MolProbity and generates visualization scripts for Coot to aid manual inspection.

How does AlphaFold integration help with molecular replacement in cryo-EM?▼

AlphaFold integration helps molecular replacement by providing accurate initial predictions for model building and docking. These predictions are seamlessly incorporated into cryo-EM workflows to establish a starting point for real-space refinement.