BindMPNN checkpoints and inference instructions

.gitattributes

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BindMPNN_v_32_010_25.pt

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BindMPNN_v_32_020_25.pt

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README.md

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+---
+language:
+- en
+tags:
+- biology
+- protein-design
+- inverse-folding
+- ligandmpnn
+- bindmpnn
+---
+
+# BindMPNN
+
+BindMPNN provides two **LigandMPNN-compatible checkpoints** for amino-acid sequence design from protein structures. Use them with the [official LigandMPNN implementation](https://github.com/dauparas/LigandMPNN) and `--model_type ligand_mpnn`.
+
+## Checkpoints
+
+| Checkpoint | Backbone noise level | Protein neighbors | Atom context size |
+|---|---:|---:|---:|
+| `BindMPNN_v_32_010_25.pt` | 0.10 Å | 32 | 25 |
+| `BindMPNN_v_32_020_25.pt` | 0.20 Å | 32 | 25 |
+
+The noise level identifies the checkpoint variant; it is **not** the sampling temperature and does not require adding noise to your input structure. Both files contain inference weights and the metadata required by LigandMPNN's checkpoint loader.
+
+## Installation and download
+
+Use a Python 3.11 environment and install LigandMPNN according to its [installation instructions](https://github.com/dauparas/LigandMPNN#dependencies). For example:
+
+```bash
+git clone https://github.com/dauparas/LigandMPNN.git
+cd LigandMPNN
+python -m pip install -r requirements.txt
+python -m pip install huggingface_hub
+```
+
+Download the checkpoints into this checkout:
+
+```python
+from huggingface_hub import hf_hub_download
+
+for filename in ("BindMPNN_v_32_010_25.pt", "BindMPNN_v_32_020_25.pt"):
+    hf_hub_download(
+        repo_id="MurrellLab/SeqGivenStruct",
+        filename=filename,
+        local_dir="checkpoints",
+    )
+```
+
+## Sequence design
+
+This example redesigns chain **B** of a target–binder complex while keeping the amino-acid identities of the other chains fixed:
+
+```bash
+python run.py \
+  --model_type ligand_mpnn \
+  --checkpoint_ligand_mpnn checkpoints/BindMPNN_v_32_010_25.pt \
+  --pdb_path complex.pdb \
+  --chains_to_design B \
+  --ligand_mpnn_use_side_chain_context 1 \
+  --temperature 0.1 \
+  --seed 123 \
+  --batch_size 1 \
+  --number_of_batches 8 \
+  --out_folder outputs/bindmpnn
+```
+
+Replace `B` with the chain you want to redesign and `complex.pdb` with your input structure. To use the 0.20 Å variant, change the checkpoint filename to `BindMPNN_v_32_020_25.pt`.
+
+- `--chains_to_design` specifies the mutable chains; other chains remain fixed.
+- `--ligand_mpnn_use_side_chain_context 1` exposes sidechain atoms of fixed residues as context. Use `0` to omit that sidechain context.
+- `--temperature` controls sequence sampling diversity independently of the checkpoint's noise label. The example uses 0.1; adjust it for your application.
+- `--seed` controls reproducibility. Sequence files are written under the output folder's `seqs/` directory.
+
+These are sequence-design checkpoints. Sidechain packing uses LigandMPNN's separate packing checkpoint and options; consult the upstream documentation for that workflow and for residue-level constraints, scoring, and batching.
+
+## Programmatic loading
+
+The checkpoint files are PyTorch dictionaries with `model_state_dict`, `num_edges`, `atom_context_num`, and `noise_level`. A compatible loader can use:
+
+```python
+import torch
+
+checkpoint = torch.load(
+    "checkpoints/BindMPNN_v_32_010_25.pt",
+    map_location="cpu",
+    weights_only=True,
+)
+# Instantiate the upstream ProteinMPNN with model_type="ligand_mpnn",
+# k_neighbors=checkpoint["num_edges"], and
+# atom_context_num=checkpoint["atom_context_num"], then:
+# model.load_state_dict(checkpoint["model_state_dict"], strict=True)
+```
+
+## Attribution
+
+BindMPNN uses the LigandMPNN architecture and checkpoint format. Please acknowledge [LigandMPNN](https://github.com/dauparas/LigandMPNN) when using these checkpoints.

checkpoints.json

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+{
+  "BindMPNN_v_32_010_25.pt": {
+    "sha256": "1bd78f7902dd5f0ce88100aee083d3ed779d97227538c884b5a17d810446cd77",
+    "size_bytes": 10537732,
+    "num_edges": 32,
+    "atom_context_num": 25,
+    "noise_level": 0.1
+  },
+  "BindMPNN_v_32_020_25.pt": {
+    "sha256": "274c66aef16a9219c718d8e212b26f2ff4bb54cee57d5264587d11af364daff7",
+    "size_bytes": 10537732,
+    "num_edges": 32,
+    "atom_context_num": 25,
+    "noise_level": 0.2
+  }
+}