Does this skill perform automated structural alignment?

It provides the methodologies, logic, and comparative patterns required to perform and interpret structural alignments effectively within a coding workflow.

What are the core principles of this structural biology skill?

The skill prioritizes structure quality validation, contextualizing 'bad' regions as potential disorder, and using multiple validation methods for a holistic view.

Can I use this skill for experimental structural data?

Yes, it includes guidance on validation metrics for crystallographic and cryo-EM models, such as R-factors, Ramachandran plots, and clashscores.

How does this skill help with AlphaFold interpretation?

It provides standardized frameworks for interpreting pLDDT scores and understanding the reliability of specific predicted protein regions.

Structural Biology Analysis

Name: Structural Biology Analysis
Author: justaddcoffee

byjustaddcoffee

Data Science & ML

Analyzes and validates protein structures, interprets AlphaFold predictions, and performs comparative molecular modeling.

About

The structural-biology skill provides specialized guidance for complex molecular workflows, enabling researchers to interpret AlphaFold confidence scores (pLDDT), validate experimental models from crystallography or cryo-EM, and perform rigorous structural comparisons. It emphasizes essential quality metrics like R-factors and Ramachandran plots, helping users distinguish between modeling errors and genuine biological disorder to ensure scientific accuracy in downstream analysis and discovery.

Key Features

AlphaFold pLDDT score interpretation and confidence analysis
Validation of crystallographic and cryo-EM models using standard metrics
Assessment of protein disorder and local model quality
Structural alignment and multi-model comparison methodologies
Analysis of structural discrepancies between predicted and experimental data
0 GitHub stars

Use Cases

Comparing a newly solved experimental structure against existing PDB models
Evaluating the reliability and biological relevance of AlphaFold protein predictions
Validating molecular models before submission or downstream simulation

About

Key Features

AlphaFold pLDDT score interpretation and confidence analysis
Validation of crystallographic and cryo-EM models using standard metrics
Assessment of protein disorder and local model quality
Structural alignment and multi-model comparison methodologies
Analysis of structural discrepancies between predicted and experimental data
0 GitHub stars

Use Cases

Comparing a newly solved experimental structure against existing PDB models
Evaluating the reliability and biological relevance of AlphaFold protein predictions
Validating molecular models before submission or downstream simulation