Rosetta Integration

Access Rosetta's powerful molecular modeling capabilities directly within your Omic workflows for protein structure prediction, design, and docking.

Overview

Rosetta is one of the most widely used molecular modeling suites in structural biology and drug discovery. The Omic-Rosetta integration brings Rosetta's capabilities into your AI-powered drug discovery workflows without complex setup.

Run protein structure prediction, protein-protein docking, ligand docking, and protein design calculations through Omic's unified API. Results are automatically integrated with downstream analyses.

Capabilities

Ab Initio Structure Prediction

Predict protein structures from sequence using Rosetta's fragment assembly and energy minimization protocols.

Protein-Protein Docking

Model protein-protein interactions with RosettaDock for studying binding interfaces and designing inhibitors.

Ligand Docking (RosettaLigand)

Flexible receptor-ligand docking with full side-chain flexibility and accurate binding energy prediction.

Protein Design

Design novel proteins or optimize existing sequences for stability, binding, and catalytic activity.

Loop Modeling

Model missing loops and flexible regions in crystal structures with high accuracy.

Mutation Analysis

Predict effects of point mutations on protein stability and binding affinity (ddG calculations).

Usage Example

from omic import Omic
from omic.integrations import Rosetta

client = Omic(api_key="your_api_key")

# Initialize Rosetta integration
rosetta = Rosetta()

# Predict structure from sequence
sequence = "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH"
structure = rosetta.predict_structure(
    sequence=sequence,
    method="abinitio",
    n_models=5,
    refine=True
)

# Get best model
best_model = structure.best_model
print(f"Best model score: {best_model.score:.2f} REU")
print(f"Model confidence: {best_model.confidence:.2f}")

# Run ligand docking
from omic.chemistry import Molecule

ligand = Molecule.from_smiles("CC(=O)Oc1ccccc1C(=O)O")  # Aspirin

docking_results = rosetta.dock_ligand(
    protein=best_model,
    ligand=ligand,
    binding_site="auto",  # Auto-detect binding site
    n_poses=100,
    flexibility="full"  # Full receptor flexibility
)

# Analyze results
for pose in docking_results.top_poses(5):
    print(f"Pose {pose.rank}: score={pose.score:.2f}, "
          f"interface_energy={pose.interface_energy:.2f}")

# Calculate binding energy
binding_energy = rosetta.calculate_binding_energy(
    complex=docking_results.best_pose,
    method="flex_ddg"
)
print(f"Predicted binding energy: {binding_energy:.2f} kcal/mol")

Protein Design Example

# Design protein to bind target epitope
design_job = rosetta.design_binder(
    target_structure=target_pdb,
    target_epitope=["A:52-68"],  # Residue range to target
    scaffold_library="miniproteins",
    n_designs=1000,
    optimize_for=["binding_energy", "stability", "expression"]
)

# Wait for results
designs = design_job.wait()

# Get top designs
for design in designs.top(10):
    print(f"Design {design.id}:")
    print(f"  Sequence: {design.sequence}")
    print(f"  Predicted Kd: {design.predicted_kd:.2f} nM")
    print(f"  Stability (ddG): {design.stability:.2f} kcal/mol")
    print(f"  Expression score: {design.expression_score:.2f}")

# Export designs for experimental validation
designs.top(10).to_fasta("top_designs.fasta")
designs.top(10).to_pdb("top_designs/")

Technical Details

Rosetta Version	Rosetta 2024.1 (weekly updates)
Supported Protocols	AbinitioRelax, RosettaDock, RosettaLigand, FastRelax, Backrub, FlexPepDock
Compute Infrastructure	Distributed across cloud GPU clusters for parallel execution
Input Formats	PDB, mmCIF, FASTA, SMILES, SDF
Output Formats	PDB, mmCIF, JSON (scores), PyMOL sessions

Access Rosetta through Omic

Run Rosetta calculations without infrastructure setup. Enterprise customers get dedicated compute resources.