FrustraPy is an attempt at a parallelized Python implementation of the frustratometeR package (See publications by Parra et al. , Rausch et al. Jenik et al.), designed for analyzing energetic frustration in protein structures. This implementation offers significant enhancements including interactive visualizations, parallel processing capabilities, and various performance optimizations.
- Multiple Frustration Analysis Modes:
- Configurational frustration
- Mutational frustration
- Single residue frustration
- Enhanced Mutation Analysis:
- Parallel execution of mutations
- Customizable mutation schemes
- Parallel Processing:
- Multi-core support for mutation analysis
- Optimized memory usage
- Progress Tracking:
- Real-time progress bars using tqdm
- Execution time logging
- Memory usage monitoring
- Interactive Plots:
- Frustration density plots
- Mutation analysis visualizations
# Basic installation
pip install frustrapy
# Development installation
git clone https://github.com/engelberger/frustrapy.git
cd frustrapy
pip install -r requirements.txt
pip install -e ".[dev]"import frustrapy
# Simple configurational frustration analysis
pdb, plots = frustrapy.calculate_frustration(
pdb_file="protein.pdb",
mode="configurational",
results_dir="results"
)
# Access and display results
plots.show_contact_map()
plots.save_all_figures("output_directory")from frustrapy import Profiler
# Initialize profiler
profiler = Profiler()
profiler.start_section("Total Analysis")
# Configure analysis parameters
residues_to_analyze = {"A": [144, 146]}
results_dir = "results_example"
# Analyze multiple PDBs in a directory
profiler.start_section("Directory Analysis")
plots_dir_dict = frustrapy.dir_frustration(
pdbs_dir="pdbs_directory",
mode="singleresidue",
results_dir=results_dir,
debug=True,
chain="A",
residues=residues_to_analyze
)
profiler.end_section("Directory Analysis")
# Single PDB detailed analysis
profiler.start_section("Single PDB Analysis")
pdb, plots = frustrapy.calculate_frustration(
pdb_file="example.pdb",
mode="singleresidue",
results_dir=results_dir,
chain="A",
residues=residues_to_analyze
)
profiler.end_section("Single PDB Analysis")
# Print performance report
profiler.end_section("Total Analysis")
profiler.print_report()import pickle
import os
# Load and analyze results
results_dir = "results_example"
for root, dirs, files in os.walk(results_dir):
for file in files:
if file.endswith("_single_residue_data.pkl"):
with open(os.path.join(root, file), "rb") as f:
data = pickle.load(f)
# Access chain A data
if "A" in data:
for res_num in [144, 146]:
if res_num in data["A"]:
res_data = data["A"][res_num]
mutations = res_data.mutations
# Find extreme mutations
most_frustrated = min(mutations.items(), key=lambda x: x[1])
least_frustrated = max(mutations.items(), key=lambda x: x[1])
print(f"\nPosition {res_num} Analysis:")
print(f"Most frustrated: {most_frustrated}")
print(f"Least frustrated: {least_frustrated}")frustrapy/
├── __init__.py # Main package entry point
├── core/ # Core functionality
│ ├── __init__.py
│ ├── pdb.py # PDB structure handling
│ └── dynamic.py # Dynamic analysis core
├── analysis/ # Analysis modules
│ ├── __init__.py
│ ├── frustration.py # Frustration calculations
│ ├── mutations.py # Mutation analysis
│ └── clustering.py # Clustering algorithms
├── visualization/ # Visualization tools
│ ├── __init__.py
│ ├── plots.py # Plotting functions
│ └── structure.py # PyMOL visualization
├── utils/ # Utility functions
│ ├── __init__.py
│ ├── decorators.py # Performance decorators
│ └── helpers.py # Helper functions
└── scripts/ # External tools integration
├── __init__.py
├── pdb_to_lammps.py
└── visualization_tools.py
classDiagram
%% Main Package Entry
class frustrapy {
Imports and re-exports all public components
}
%% Core Classes Module
class core {
class Pdb
class Dynamic
}
%% Analysis Module
class analysis {
+calculate_frustration()
+dir_frustration()
+dynamic_frustration()
+get_frustration()
+mutate_res()
+detect_dynamic_clusters()
}
%% Visualization Module
class visualization {
+plot_contact_map()
+plot_5andens()
+plot_5adens_proportions()
+plot_delta_frus()
+view_frustration_pymol()
}
%% Utils Module
class utils {
+log_execution_time()
+log_memory_usage()
+get_os()
}
%% Scripts Module
class scripts {
+pdb_to_lammps
+generate_visualizations
+renum_files
+generate_charge_file
}
%% Relationships
frustrapy --> core: exports
frustrapy --> analysis: exports
frustrapy --> visualization: exports
frustrapy --> utils: internal
analysis --> core: uses
visualization --> core: uses
analysis --> scripts: uses
analysis --> utils: uses
Contributions are welcome! Please read our Contributing Guidelines for details on our code of conduct and the process for submitting pull requests.
If you use FrustraPy in your research, please cite the following papers:
@article{parra2016protein,
title={Protein Frustratometer 2: a tool to localize energetic frustration in protein molecules, now with electrostatics},
author={Parra, R Gonzalo and Schafer, Nicholas P and Radusky, Leandro G and Tsai, Min-Yeh and Guzovsky, A Brenda and Wolynes, Peter G and Ferreiro, Diego U},
journal={Nucleic acids research},
volume={44},
number={W1},
pages={W356--W360},
year={2016},
publisher={Oxford University Press}
}
@article{jenik2012protein,
title={Protein frustratometer: a tool to localize energetic frustration in protein molecules},
author={Jenik, Michael and Parra, R Gonzalo and Radusky, Leandro G and Turjanski, Adrian and Wolynes, Peter G and Ferreiro, Diego U},
journal={Nucleic acids research},
volume={40},
number={W1},
pages={W348--W351},
year={2012},
publisher={Oxford University Press}
}
@article{rausch2021frustratometer,
title={FrustratometeR: an R-package to compute local frustration in protein structures, point mutants and MD simulations},
author={Rausch, Atilio O and Freiberger, Maria I and Leonetti, Cesar O and Luna, Diego M and Radusky, Leandro G and Wolynes, Peter G and Ferreiro, Diego U and Parra, R Gonzalo},
journal={Bioinformatics},
volume={37},
number={18},
pages={3038--3040},
year={2021},
publisher={Oxford University Press}
}This project is licensed under the MIT License - see the LICENSE file for details.
- Original Frustratometer developers!