A comprehensive Python application for mapping and visualizing molecular interactions at protein/nucleic acid interfaces. A tool for mapping protein-protein, protein-nucleic acid, and antigen-antibody interactions.
PandaProt can detect and analyze a comprehensive set of molecular interactions that occur in protein/DNA structures:
Standard Interactions:
- Hydrogen Bonds: Between hydrogen donors and acceptors (N-H···O, O-H···O, etc.)
- Ionic Interactions: Between oppositely charged residues (Arg/Lys/His with Asp/Glu)
- Hydrophobic Interactions: Between non-polar side chains
- Pi-Pi Stacking: Between aromatic rings (parallel, T-shaped, offset-stacked)
- Pi-Cation Interactions: Between aromatic rings and cationic groups
- Salt Bridges: Close-range ionic interactions forming "bridges"
Enhanced Interactions:
- Cation-Pi Interactions: Between cations and aromatic rings
- CH-Pi Interactions: Between CH groups and aromatic rings
- Disulfide Bridges: Covalent bonds between cysteine residues
- Sulfur-Aromatic Interactions: Between sulfur atoms and aromatic rings
- Water-Mediated Interactions: Hydrogen bonds bridged by water molecules
- Metal-Coordinated Bonds: Interactions mediated by metal ions
- Halogen Bonds: Between halogen atoms and Lewis bases
- Amide-Aromatic Interactions: Between amide groups and aromatic rings
- Van der Waals Interactions: Non-specific attractive forces
- Amide-Amide Hydrogen Bonds: Between backbone or side chain amide groups
- Interactive 3D Visualization: Color-coded display of interactions in the 3D structure
- Network Analysis: Graph representation of interaction networks
- Detailed Reports: Comprehensive interaction statistics and measurements
pip install pandaprotpandaprot -h
usage: pandaprot [-h] [--chains CHAINS [CHAINS ...]] [--3d-plot] [--export-vis] [--report] [--network] [--output OUTPUT]
[--distance-cutoff DISTANCE_CUTOFF] [--include-intrachain] [--all-interactions] [--standard-only]
[--hydrogen-bonds] [--ionic] [--hydrophobic] [--pi-stacking] [--pi-cation] [--salt-bridges] [--cation-pi]
[--ch-pi] [--disulfide] [--sulfur-aromatic] [--water-mediated] [--metal-coordination] [--halogen-bonds]
[--amide-aromatic] [--van-der-waals] [--amide-amide] [--statistics] [--residue-summary]
pdb_file
PandaProt: Comprehensive Protein Interaction Mapper
positional arguments:
pdb_file Path to PDB file
options:
-h, --help show this help message and exit
--chains CHAINS [CHAINS ...]
Chains to analyze (e.g., A B)
--3d-plot Generate 3D visualization
--export-vis Export visualization files for PyMOL, Chimera, VMD, and Molstar
--report Generate detailed interaction report
--network Generate interaction network visualization
--output, -o OUTPUT Output file prefix
--distance-cutoff DISTANCE_CUTOFF
Distance cutoff for interaction detection (default: 4.5Å)
--include-intrachain Include intra-chain interactions
Interaction Types:
--all-interactions Map all interaction types (default)
--standard-only Map only standard interactions (H-bonds, ionic, hydrophobic, pi-stacking, salt bridges)
--hydrogen-bonds Map hydrogen bonds
--ionic Map ionic interactions
--hydrophobic Map hydrophobic interactions
--pi-stacking Map pi-pi stacking interactions
--pi-cation Map pi-cation interactions
--salt-bridges Map salt bridges
--cation-pi Map cation-pi interactions
--ch-pi Map CH-pi interactions
--disulfide Map disulfide bridges
--sulfur-aromatic Map sulfur-aromatic interactions
--water-mediated Map water-mediated interactions
--metal-coordination Map metal-coordinated bonds
--halogen-bonds Map halogen bonds
--amide-aromatic Map amide-aromatic interactions
--van-der-waals Map van der Waals interactions
--amide-amide Map amide-amide hydrogen bonds
Analysis Options:
--statistics Generate interaction statistics
--residue-summary Generate residue interaction summary
pandaprot 4kpy.pdb --export-vis --3d-plot --report --network --statistics --residue-summary --chains A B C D E F --output protein-dna-ligandpandaprot 1BJ1.pdb --3d-plot --export-vis --report --network --statistics --residue-summary --chains L H V W J K --output antigen_antibody# Basic usage - map all interactions
pandaprot example.pdb
# Analyze specific chains
pandaprot example.pdb --chains A B
# Generate 3D visualization
pandaprot example.pdb --3d-plot
# Generate detailed report
pandaprot example.pdb --report
# Generate interaction network
pandaprot example.pdb --network
# Map specific interaction types
pandaprot example.pdb --hydrogen-bonds --ionic --salt-bridges
# Map only standard interactions
pandaprot example.pdb --standard-only
# Include water-mediated and metal-coordinated interactions
pandaprot example.pdb --water-mediated --metal-coordination
# Generate interaction statistics
pandaprot example.pdb --statistics
# Specify output file prefix
pandaprot example.pdb --3d-plot --report --network --output myresults
# Include intra-chain interactions
pandaprot example.pdb --include-intrachainfrom pandaprot import PandaProt
# Initialize with PDB file
analyzer = PandaProt("example.pdb", chains=["A", "B"])
# Map all interactions
interactions = analyzer.map_interactions()
# Map with custom parameters
interactions = analyzer.map_interactions(
distance_cutoff=4.5,
include_intrachain=False
)
# Filter interactions
filtered = analyzer.filter_interactions(
interaction_types=["hydrogen_bonds", "salt_bridges"],
chains=["A"],
distance_range=(2.5, 3.5)
)
# Generate 3D visualization
analyzer.visualize_3d("visualization.html")
# Visualize specific interaction types
analyzer.visualize_3d(
"hbonds_visualization.html",
interaction_types=["hydrogen_bonds", "water_mediated"]
)
# Generate detailed report
report = analyzer.generate_report("report.csv")
# Generate report for specific interaction types
report = analyzer.generate_report(
"ionic_report.csv",
interaction_types=["ionic_interactions", "salt_bridges"]
)
# Create interaction network
graph, fig = analyzer.create_interaction_network("network.png")
# Get interaction statistics
stats = analyzer.get_interaction_statistics()
print(f"Total interactions: {stats['total_interactions']}")
print(f"Most interactive residue: {list(stats['residue_frequencies'].keys())[0]}")# Analyze an antibody-antigen complex
analyzer = PandaProt("antibody_complex.pdb", chains=["H", "L", "A"])
# Map all interactions
interactions = analyzer.map_interactions()
# Find epitope residues (antigen residues interacting with antibody)
epitope_residues = []
for interaction_type, interactions_list in interactions.items():
for interaction in interactions_list:
# Extract chain information from interaction
chain1 = interaction.get('chain1', interaction.get('donor_chain', ''))
chain2 = interaction.get('chain2', interaction.get('acceptor_chain', ''))
# Extract residue information
res1 = interaction.get('residue1', interaction.get('donor_residue', ''))
res2 = interaction.get('residue2', interaction.get('acceptor_residue', ''))
# Check if antigen (chain A) is interacting with antibody (chains H or L)
if chain1 == 'A' and chain2 in ['H', 'L']:
epitope_residues.append(f"A:{res1}")
elif chain2 == 'A' and chain1 in ['H', 'L']:
epitope_residues.append(f"A:{res2}")
# Get unique epitope residues
epitope_residues = list(set(epitope_residues))
print(f"Epitope residues: {', '.join(epitope_residues)}")# Analyze a protein-protein complex
analyzer = PandaProt("protein_complex.pdb", chains=["A", "B"])
# Map all interactions
interactions = analyzer.map_interactions()
# Generate statistics
stats = analyzer.get_interaction_statistics()
# Find hotspot residues (highly interactive residues)
hotspots = []
for residue, count in list(stats['residue_frequencies'].items())[:5]:
hotspots.append((residue, count))
print("Interface hotspot residues:")
for residue, count in hotspots:
print(f" - {residue}: {count} interactions")
# Analyze interaction types at the interface
print("\nInteraction composition at the interface:")
total = stats['total_interactions']
for interaction_type, count in stats['by_type'].items():
if count > 0:
percentage = (count / total) * 100
print(f" - {interaction_type}: {count} ({percentage:.1f}%)")# Analyze a protein-ligand complex
analyzer = PandaProt("protein_ligand.pdb", chains=["A", "L"])
# Map interactions with focus on drug binding
interactions = analyzer.map_interactions()
# Create a pharmacophore model based on interactions
pharmacophore_features = {
'hydrogen_bond_donors': [],
'hydrogen_bond_acceptors': [],
'ionic_positive': [],
'ionic_negative': [],
'hydrophobic': [],
'aromatic': []
}
# Extract pharmacophore features from interactions
for interaction_type, interactions_list in interactions.items():
for interaction in interactions_list:
# Focus on protein-ligand interactions
if interaction_type == 'hydrogen_bonds':
if 'donor_chain' in interaction and interaction['donor_chain'] == 'A':
pharmacophore_features['hydrogen_bond_acceptors'].append(interaction['donor_residue'])
elif 'acceptor_chain' in interaction and interaction['acceptor_chain'] == 'A':
pharmacophore_features['hydrogen_bond_donors'].append(interaction['acceptor_residue'])
elif interaction_type == 'ionic_interactions':
if 'positive_chain' in interaction and interaction['positive_chain'] == 'A':
pharmacophore_features['ionic_negative'].append(interaction['positive_residue'])
elif 'negative_chain' in interaction and interaction['negative_chain'] == 'A':
pharmacophore_features['ionic_positive'].append(interaction['negative_residue'])
elif interaction_type == 'hydrophobic_interactions':
if 'chain1' in interaction and interaction['chain1'] == 'A':
pharmacophore_features['hydrophobic'].append(interaction['residue1'])
elif 'chain2' in interaction and interaction['chain2'] == 'A':
pharmacophore_features['hydrophobic'].append(interaction['residue2'])
elif interaction_type in ['pi_stacking', 'pi_cation', 'cation_pi']:
if 'aromatic_chain' in interaction and interaction['aromatic_chain'] == 'A':
pharmacophore_features['aromatic'].append(interaction['aromatic_residue'])
print("Pharmacophore features for drug design:")
for feature_type, residues in pharmacophore_features.items():
if residues:
print(f" - {feature_type}: {', '.join(set(residues))}")PandaProt detects and analyzes the following interaction types:
- Definition: Non-covalent interaction between a hydrogen atom covalently bonded to an electronegative donor atom (N, O) and another electronegative acceptor atom
- Strength: 2-10 kcal/mol
- Distance Criteria: Donor-acceptor distance < 3.5Å
- Angle Criteria: Donor-H-acceptor angle > 120°
- Definition: Electrostatic attraction between oppositely charged groups
- Strength: 10-20 kcal/mol
- Distance Criteria: < 6.0Å between charged groups
- Residues Involved: Positive (Arg, Lys, His) and negative (Asp, Glu)
- Definition: Clustering of non-polar side chains to exclude water
- Strength: 1-2 kcal/mol per methylene group
- Distance Criteria: < 5.0Å between carbon atoms
- Residues Involved: Ala, Val, Leu, Ile, Met, Phe, Trp, Pro, Tyr
- Definition: Interaction between aromatic rings
- Types: Parallel, T-shaped, offset stacked
- Strength: 2-3 kcal/mol
- Distance Criteria: < 7.0Å between ring centers
- Angle Criteria: Dependent on stacking type
- Residues Involved: Phe, Tyr, Trp, His
- Definition: Attraction between a cation and the face of an aromatic ring
- Strength: 3-5 kcal/mol
- Distance Criteria: < 6.0Å between cation and ring center
- Residues Involved: Cation (Arg, Lys) and aromatic (Phe, Tyr, Trp)
- Definition: Close-range ionic interaction with partial covalent character
- Strength: 10-20 kcal/mol
- Distance Criteria: < 4.0Å between charged groups
- Residues Involved: Acidic (Asp, Glu) and basic (Arg, Lys, His)
- Definition: Electrostatic attraction between a cation and π-electron system
- Strength: 2-5 kcal/mol
- Distance Criteria: < 6.0Å between cation and ring center
- Residues Involved: Cation (Arg, Lys, metal ions) and aromatic (Phe, Tyr, Trp)
- Definition: Weak hydrogen bond between a CH group and an aromatic ring
- Strength: 0.5-1.0 kcal/mol
- Distance Criteria: < 4.0Å between CH and ring center
- Angle Criteria: CH vector pointing toward ring plane
- Definition: Covalent bond between sulfur atoms of cysteine residues
- Strength: 60-70 kcal/mol (covalent)
- Distance Criteria: 2.0-2.2Å between sulfur atoms
- Residues Involved: Cys
- Definition: Interaction between sulfur atoms and aromatic rings
- Strength: 1-2 kcal/mol
- Distance Criteria: < 5.5Å between sulfur and ring center
- Residues Involved: Sulfur (Met, Cys) and aromatic (Phe, Tyr, Trp)
- Definition: Hydrogen bonds bridged by water molecules
- Strength: Similar to regular hydrogen bonds
- Distance Criteria: < 3.5Å between polar atom and water
- Types: Water bridges, water networks
- Definition: Coordination between metal ions and electron-rich atoms
- Strength: 20-40 kcal/mol
- Distance Criteria: Depends on metal (typically 1.8-2.5Å)
- Coordination: Tetrahedral, octahedral, square planar
- Metals: Zn²⁺, Ca²⁺, Mg²⁺, Fe²⁺/³⁺, etc.
- Definition: Interaction between halogen atom (Lewis acid) and Lewis base
- Strength: 1-5 kcal/mol
- Distance Criteria: < 4.0Å between halogen and acceptor
- Angle Criteria: C-X···Y angle close to 180°
- Atoms Involved: F, Cl, Br, I as donors; O, N, S as acceptors
- Definition: Interaction between amide groups and aromatic rings
- Strength: 1-3 kcal/mol
- Distance Criteria: < 4.5Å between amide and ring center
- Residues Involved: Amide (Asn, Gln, backbone) and aromatic (Phe, Tyr, Trp)
- Definition: Weak non-specific attractive forces between atoms
- Strength: 0.1-1.0 kcal/mol per atom pair
- Distance Criteria: Sum of van der Waals radii + 40%
- Involves: All atom types
- Definition: Hydrogen bonds between amide groups
- Strength: Similar to regular hydrogen bonds
- Distance Criteria: < 3.5Å between amide groups
- Residues Involved: Asn, Gln, backbone amides
PandaProt uses carefully calibrated distance criteria for each interaction type:
| Interaction Type | Default Distance Cutoff (Å) |
|---|---|
| Hydrogen Bonds | 3.5 |
| Ionic Interactions | 6.0 |
| Hydrophobic Interactions | 5.0 |
| Pi-Pi Stacking | 7.0 |
| Pi-Cation | 6.0 |
| Salt Bridges | 4.0 |
| Cation-Pi | 6.0 |
| CH-Pi | 4.0 |
| Disulfide Bridges | 2.2 |
| Sulfur-Aromatic | 5.5 |
| Water-Mediated | 3.5 (per hydrogen bond) |
| Metal-Coordination | 3.0 |
| Halogen Bonds | 4.0 |
| Amide-Aromatic | 4.5 |
| Van der Waals | vdW radii sum × 1.4 |
| Amide-Amide | 3.5 |
These distance cutoffs can be customized using the distance_cutoff parameter.
- Python 3.7+
- BioPython
- NumPy
- Pandas
- Matplotlib
- py3Dmol
- NetworkX
If you use PandaProt in your research, please cite:
Pritam Kumar Panda. (2025).
A comprehensive Python application for mapping and visualizing molecular interactions at protein/nucleic acid interfaces and tool for mapping protein-protein, protein-nucleic acid, and antigen-antibody interactions. GitHub repository https://github.com/pritampanda15/PandaProt
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.