# Welcome to SOAP’s documentation!¶

## Installation¶

- Install required packages defined in envConfig.py (
`detailed instructions`

). - Copy envConfig.py to env.py and modify it to work with your setup.

## Basic workflow to generate a statistical potential¶

- Preprocess PDB if necessary and generate sequence files.
- Prepare decoys.
- Run SOAP script to select models.
- Run SOAP script to calculate the optimal statistical potential using the best model.
- Write out the optimal potential in hdf5 or lib format for use in Modeller, IMP or other packages.

Examples:

Additonal undocumented examples can be found at SOAP/examples.

## Pre-calculated SOAP tables¶

Pre-calculated SOAP statistical potentials for ranking peptides, loops, proteins, protein interfaces and ligands can be found at : salilab.org/SOAP

## Modules:¶

- Environmental variables
- Job scheduler (local and on SGE cluster)
- Select the best model and generate statistical potentials
- Collection of sequences for known structures or decoys
- Decoys
- Definition of MDT features
- Calculate statistics from a set of structures
- Recovery Functions
- Structures/Decoys scores
- Structure Refinement using SOAP
- Benchmark SOAP’s performance using a set of criteria
- Combined scores from multiple scoring terms
- Optimize and sample the parameters in SOAP
- Cross Validation of a model
- Loop modeling
- Utility classes and functions

## Please cite:¶

Dong GQ, Fan H, Schneidman-Duhovny D, Webb B, Sali A. Optimized atomic statistical potentials: assessment of protein interfaces and loops. Bioinformatics. 2013 Dec 15; 29(24):3158-66.