DDI_single

DDI_single

Single-sequence domain assembly for multi-domain proteins

中文文档请参阅 README_ch.md

DDI_single (Domain Distance Inference, single-sequence) is a domain assembly method for multi-domain proteins that relies solely on the amino acid sequence. It uses the protein language model ESM-1b to extract sequence features and an improved Gated Cross-Attention module to predict inter-domain residue pair distances and orientations, enabling correct spatial assembly of structural domains.

Pipeline

<p align="center"> <img src="./Fig/pipeline.png" alt="DDI_single Pipeline" width="100%"> </p>

The pipeline takes a protein sequence and domain definitions as input. ESM-1b extracts per-residue embeddings, which are split by domain and refined through 4 Gated Cross-Attention layers with 4 recycle iterations. The model predicts inter-domain distance and orientation distributions (d, ω, θ, φ), which are combined with intra-domain constraints and fed into Rosetta for 3D structure assembly.

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Key Features

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Performance

Inter-domain residue pair distance prediction accuracy (given known domain definitions):

Domain assembly success rate (TM-score > 0.5):

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Quick Start

1. Install dependencies

pip install torch fair-esm numpy einops huggingface_hub

ESM-1b pretrained weights (~2.5 GB) will be downloaded automatically on first run.

2. Download model weights

Model checkpoints are hosted on this repository (~4.7 GB):

huggingface-cli download SilenceZong/DDI_single DDI_single.pth --local-dir .

<details> <summary>Or download with Python</summary>

from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="SilenceZong/DDI_single", filename="DDI_single.pth", local_dir=".")

</details>

3. Prepare input

• FASTA file — full-length amino acid sequence (standard FASTA format)
• Domain ranges — 1-based residue intervals, multiple ranges separated by ;

Example FASTA:

>protein
MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSG...

Example domain definitions:

• Domain 1: 1-150;200-280 (two discontinuous segments)
• Domain 2: 151-199;281-400

4. Run prediction

python predict.py <fasta> <domain1> <domain2> <output.npz> [--weights PATH] [--device cpu|cuda]

# CPU (default)
python predict.py protein.fasta "1-150" "151-300" result.npz

# GPU
python predict.py protein.fasta "1-150" "151-300" result.npz --device cuda

# Custom weights path
python predict.py protein.fasta "1-150" "151-300" result.npz --weights /path/to/DDI_single.pth

5. Load results

Results are saved as .npz with the following fields:

import numpy as np

data = np.load("result.npz")
dist = data["dist"]          # shape: [1, 37, L1, L2]
d1_idx = data["domain1_index"]
d2_idx = data["domain2_index"]

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Domain Assembly Workflow

Inter-domain constraints predicted by DDI_single can be combined with Rosetta for energy-minimization-based domain assembly:

1. Known domain conformations — Extract intra-domain constraints from PDB, predict inter-domain constraints with DDI_single, concatenate, and fold with Rosetta
2. Unknown domain conformations — Predict intra-domain constraints with trRosettaX_single first, then replace/enhance inter-domain constraints with DDI_single, and assemble with Rosetta

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Model Architecture

<details> <summary>Hyperparameters (config.py)</summary> </details>

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Project Structure

DDI_single/
├── config.py          # Model and training hyperparameters
├── model.py           # Network architecture (GAU, Single_DDI, Single_DDI_sESM)
├── predict.py         # Inference script
├── pipeline.png       # Full pipeline diagram
├── DDI_single.pth     # Model weights
├── README.md          # English documentation
└── README_ch.md       # Chinese documentation

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Citation

If you use DDI_single in your research, please cite:

@article{ddi_single,
  title   = {DDI_single: Single-sequence-based Domain Assembly via Gated Cross-Attention},
  author  = {Shengyi Zong},
  journal = {},
  year    = {}
}

Acknowledgements

• ESM — Protein language model
• trRosettaX — Single-sequence structure prediction baseline
• Rosetta — Structure modeling and energy minimization

License

Apache 2.0