Getting Started

This guide will get you from zero to your first BAGEL protein design run.

Prerequisites

Python 3.11-3.13
pip (or uv for development)
A Modal account for serverless oracle inference, or a local GPU with sufficient VRAM

Installation

Install BAGEL from PyPI:

pip install biobagel

For local GPU execution of protein models (instead of Modal):

pip install biobagel[local]

Oracle setup

BAGEL uses boileroom to run oracle inference — the ML model predictions (structure folding, embeddings) that energy terms depend on. You have two options:

Modal (serverless)

No GPU required. Models run on Modal’s serverless infrastructure. You need a Modal account with credits.

pip install modal
modal token new

Then pass use_modal=True when creating oracles.

Local GPU

Requires a GPU with sufficient VRAM (16 GB+ recommended). Install with:

pip install biobagel[local]

Then pass use_modal=False when creating oracles.

See the boileroom getting started guide for details on backend configuration.

Your first design

Here is a minimal example that designs a 30-residue protein scaffold using simulated annealing. The design goal is a confident, globular structure with low surface hydrophobicity:

import numpy as np
import bagel as bg

# Generate a random starting sequence
sequence = np.random.choice(list(bg.constants.aa_dict.keys()), size=30)

# Create mutable residues and chain
residues = [
    bg.Residue(name=aa, chain_ID="A", index=i, mutable=True)
    for i, aa in enumerate(sequence)
]
chain = bg.Chain(residues)

# Set up the folding oracle (ESMFold via Modal)
esmfold = bg.oracles.ESMFold(use_modal=True)

# Define energy terms
state = bg.State(
    chains=[chain],
    energy_terms=[
        bg.energies.PTMEnergy(oracle=esmfold, weight=1.0),
        bg.energies.OverallPLDDTEnergy(oracle=esmfold, weight=1.0),
        bg.energies.HydrophobicEnergy(oracle=esmfold, weight=3.0, mode="surface"),
        bg.energies.GlobularEnergy(oracle=esmfold, weight=1.0, residues=residues),
    ],
    name="scaffold",
)

# Run simulated annealing
minimizer = bg.minimizer.SimulatedAnnealing(
    mutator=bg.mutation.Canonical(),
    initial_temperature=0.2,
    final_temperature=0.05,
    n_steps=500,
    callbacks=[
        bg.callbacks.DefaultLogger(log_interval=1),
        bg.callbacks.FoldingLogger(folding_oracle=esmfold, log_interval=50),
    ],
)

best_system = minimizer.minimize_system(system=bg.System([state]))

This script will:

Start from a random 30-residue sequence
Fold each candidate with ESMFold
Evaluate pTM, pLDDT, hydrophobicity, and globularity
Accept or reject mutations based on the Metropolis criterion
Return the best system found after 500 steps

Output files are saved to a timestamped directory alongside your script, including energy traces, sequences (FASTA), and predicted structures (CIF).

What’s next

Concepts

Understand the energy landscape, system hierarchy, and Monte Carlo optimization.

Energy Terms

Explore all built-in energy terms and their parameters.

Customization

Write your own energy terms, minimizers, mutation protocols, oracles, and callbacks.

Case Studies

See complete design workflows for binders, scaffolds, and enzyme variants.

​Prerequisites

​Installation

​Oracle setup