SwitchCraft

Implementation of SwitchCraft: A Programmatic Framework for Desigining State Switching Protein (ICML 2026) by Bowen Jing*, Mihir Bafna*, Anisha Parsan, Heyuan Michael Ni, David Kwabi-Addo, Bryan Bryson, Adam Klivans, Bonnie Berger.

SwitchCraft is a framework for designing state-switching proteins via gradient-based optimization through AF3-style co-folding models (here, Boltz-1). Given a multistate design objective, SwitchCraft constructs and optimizes a compositional loss function to design a sequence that exhibits the desired multistate behavior. Please see our preprint for detailed methodology and benchmarks.

Note

This repository is provided for in silico research reproducibility. It does not yet provide an end-to-end design workflow with in vitro experimental validation.

---

Installation

1. Clone the repository

git clone https://github.com/bjing2016/switchcraft
cd switchcraft

2. Install PyTorch with CUDA support

Install a PyTorch version matching your CUDA driver. For CUDA 12.x:

pip install "torch==2.7.1+cu126" --index-url https://download.pytorch.org/whl/cu126

3. Install Boltz and remaining dependencies

pip install -e boltz/
pip install prody

4. Download Boltz model weights and CCD ligand database

python -c "
from boltz.main import download
from pathlib import Path
cache = Path('boltz')
cache.mkdir(parents=True, exist_ok=True)
download(cache)
"

5. Install LigandMPNN and model weights (only needed if using --ligandmpnn_seqs)

cd LigandMPNN && bash get_model_params.sh ./model_params && cd ..

---

Quick Start

Each design run is driven by a YAML config file that fully specifies the design problem. Template configs for example tasks live in tasks/.

Example command:

python switchcraft.py --config tasks/pos_allostery.yaml 

This runs the pos_allostery task: design a protein that scaffolds motif 1prw in the ligand-bound state and loses the motif geometry when the ligand unbinds.

Output for each design is saved to <outpath>/<motif>/design<N>/:

• state<i>_sample<j>.pdb — predicted structures (5 diffusion samples per state)
• state<i>_sample<j>.cif — same structures in mmCIF format
• state<i>.pkl — raw model output (pLDDT, pTM, pAE, etc.)
• <motif>_spec.pkl — motif specification used for evaluation

Command line arguments that can be provided include

• --length: override scaffold length (required if no motifs and not set in config)
• --recycles: Boltz recycling steps during optimization
• --ligandmpnn_seqs: if >0, run LigandMPNN redesign producing N sequences per design
• --verbose: whether to use progress bars (recommended)
• --out: output directory
• --num_designs: total number of designs

---

Design Configs

Each multistate design problem is specified as a YAML file which provides (1) a list of states, each defined as a list of ligands, and (2) a list of loss functions with appropriate arguments. Template configs for built-in tasks described in our paper are in tasks/. Copy a template (or create a new one), fill in your motifs and ligands, and pass it to --config.

Schema

num_states: 2          # number of structural states

motifs:                # PDB stems under motifs/ (determines scaffold length)
  - 1prw

states:                # list of states; each state is a list of ligand strings
  - []                 # state 0: apo — no ligands
  - ["ccd:<CODE>"]     # state 1: holo — one CCD ligand
  # ligand string formats:
  #   "ccd:<CODE>"        small molecule by CCD code
  #   "ligand:<SMILES>"   small molecule by SMILES
  #   "protein:<SEQ>"     protein chain
  #   "dna:<SEQ>"         DNA (both strands added automatically)
  #   "rna:<SEQ>"         RNA

losses:
  - type: MotifLoss        # loss class name (see Loss Types below)
    motif: 0               # index into motifs list (MotifLoss/AntiMotifLoss only)
    state: 0               # state index (int) or pair of states (list) for ConfChangeLoss
    weight: 1.0            # optional loss weight (default 1.0)
    strength: 1.0          # optional strength parameter (loss-specific)

# contact_loss: true     # set false to disable automatic ContactLoss on all states
# length: 100            # explicit scaffold length (overrides motif-derived length)

Loss Types

Losses are specified in losses.py and can be easily extended.

Loss	Description	Parameters
MotifLoss	Enforce motif geometry	motif (index)
AntiMotifLoss	Penalize motif geometry	motif (index)
LigandContactLoss	Promote protein–ligand contacts	strength, idx
AntiLigandContactLoss	Penalize protein–ligand contacts	strength, idx
ConfChangeLoss	Maximize conformational difference between two states	strength; state must be a 2-element list
HelixBiasLoss	Bias toward (strength > 0) or away from helices	strength
SheetBiasLoss	Bias toward (strength > 0) or away from sheets	strength
RadiusOfGyrationLoss	Penalize over-extended conformations	strength
SequenceSimilarityLoss	Push sequence toward a target	target_sequence, strength

A ContactLoss (to promote well-structured backbones) is added automatically to every state unless contact_loss: false is set.

Built-in Tasks

Config	States	Description
tasks/neg_allostery.yaml	2	Negative allostery: motif active when apo, disrupted when ligand binds
tasks/pos_allostery.yaml	2	Positive allostery: motif absent when apo, formed when ligand binds
tasks/induced_binding.yaml	2	Binds one ligand only in the presence of a second effector ligand
tasks/ligand_discrimination.yaml	3	Adopts different conformations depending on which of two ligands are bound
tasks/motif_switching.yaml	2	Motif 1 active when apo, motif 2 active when holo

---

Evaluation

Analysis scripts used in the paper are in ./analysis.

---

License

MIT. Separate licenses may apply to third-party source code.

Acknowledgement

This repository is adapted from or based on https://github.com/yehlincho/BoltzDesign1, https://github.com/jwohlwend/boltz, and https://github.com/dauparas/LigandMPNN. Big thanks to the developers of these open-source projects!