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Complete User Guide

1. Command Line Interface

# Basic prediction
python -m proteinfoldpro --sequence MKALIV... --output results/

# Advanced options
python -m proteinfoldpro \
  --input my_protein.fasta \
  --use-colabfold \
  --num-models 5 \
  --enable-openmm

2. Python API

from proteinfoldpro import ProteinFoldPro

# Create predictor with custom options
predictor = ProteinFoldPro(
    output_dir="custom_results",
    enable_gpu=True
)

# Run full prediction workflow
result = predictor.fold_protein(
    sequence="MKALIVLGLVLL...",
    protein_id="my_protein",
    use_all_methods=True
)

3. Output Files

File Type Description
*.pdb Predicted 3D structure
*_scores.json Confidence metrics
*_msa.a3m Multiple sequence alignment
*_report.md Validation summary