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LICENSE

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+MIT License
+
+Copyright (c) 2022 Justas Dauparas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+frameworks:
+- ""
+language:
+- en
+- zh
+license: mit
+tags:
+- OneScience
+- life-science
+- graph-neural-network
+- amino-acid-sequence
+tasks: []
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">ProteinMPNN</span>
+  </strong>
+</p>
+
+# Model Introduction
+
+ProteinMPNN is a protein sequence design model based on a Message Passing Neural Network. Given a protein backbone structure, it can efficiently generate highly expressible and foldable amino acid sequences.
+
+# Model Description
+
+ProteinMPNN uses an encoder-decoder architecture. The encoder extracts geometric and topological features of the backbone structure through a graph neural network, while the decoder generates the amino acid sequence position by position in an autoregressive manner.
+
+
+# Usage
+
+## 1. Using OneCode
+
+You can try intelligent one-click AI4S programming through the OneCode online environment:
+
+[Try intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
+
+## 2. Manual Installation and Usage
+
+**Hardware Requirements**
+
+- Running on a GPU or DCU is recommended.
+- A CPU can be used for import checks and small-configuration connectivity validation, but full training and inference will be slow.
+- DCU users need to install DTK in advance. DTK 25.04.2 or later is recommended, or the OneScience-recommended version that matches the current cluster.
+
+
+## 3. Quick Start
+
+### Download the Model Package
+
+```bash
+modelscope download --model OneScience/ProteinMPNN --local_dir ./ProteinMPNN 
+cd ProteinMPNN 
+```
+
+### Install the Runtime Environment
+#### DCU Environment
+
+```bash
+# Activate DTK and CONDA first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+# uv installation is supported
+pip install onescience[bio] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
+```
+
+If the runtime environment explicitly needs to point to the OneScience root directory, set:
+
+```bash
+export ONESCIENCE_ROOT=/path/to/onescience
+```
+
+## Quick Verification
+
+```bash
+export PYTHONPATH=$(pwd)/model:${ONESCIENCE_ROOT}/src:${PYTHONPATH:-}
+python -c "from proteinmpnn.protein_mpnn_utils import ProteinMPNN; print('proteinmpnn wrapper ok')"
+python scripts/inference.py --help
+python scripts/training.py --help
+```
+
+## Inference
+
+The current weights have been placed in subdirectories under `weight/`. The standard ProteinMPNN uses `weight/vanilla_model_weights/`; if `--path_to_model_weights` is not explicitly passed, `scripts/inference.py` uses this directory by default.
+
+### Run Minimal Inference with the Script
+
+```bash
+cd /path/to/proteinmpnn
+bash scripts/test_inference.sh
+```
+
+This script uses the following by default:
+
+```text
+Input PDB: data/inputs/PDB_monomers/pdbs/5L33.pdb
+Designed chain: A
+Model weights: weight/vanilla_model_weights
+Output directory: outputs/test_inference/
+```
+
+View the generated sequences:
+
+```bash
+ls outputs/test_inference/seqs
+```
+
+Equivalent command:
+
+```bash
+python scripts/inference.py \
+  --pdb_path ./data/inputs/PDB_monomers/pdbs/5L33.pdb \
+  --pdb_path_chains "A" \
+  --out_folder ./outputs/test_inference \
+  --path_to_model_weights ./weight/vanilla_model_weights \
+  --model_name v_48_020 \
+  --num_seq_per_target 2 \
+  --sampling_temp "0.1" \
+  --seed 37 \
+  --batch_size 1
+```
+
+Available weights:
+
+- Standard model: `--path_to_model_weights ./weight/vanilla_model_weights`
+- Soluble protein model: `--path_to_model_weights ./weight/soluble_model_weights` or add `--use_soluble_model`
+- CA-only model: `--path_to_model_weights ./weight/ca_model_weights` or add `--ca_only`
+
+## Inference Example Scripts
+
+The `scripts/infer_examples/` directory contains examples for 12 inference scenarios, all adapted to the current directory structure:
+
+| Script | Scenario |
+| --- | --- |
+| `submit_example_1.sh` | Inference on multiple single-chain PDBs. |
+| `submit_example_2.sh` | Multi-chain complex; design only the specified chains. |
+| `submit_example_3.sh` | Inference on a single PDB complex. |
+| `submit_example_3_score_only.sh` | Score existing structures/sequences without generating new sequences. |
+| `submit_example_3_score_only_from_fasta.sh` | Score a structure using FASTA sequences. |
+| `submit_example_4.sh` | Fix certain residue positions and exclude them from design. |
+| `submit_example_4_non_fixed.sh` | Design only the specified positions. |
+| `submit_example_5.sh` | Tied positions, with multi-position tied design. |
+| `submit_example_6.sh` | Homooligomer-constrained design. |
+| `submit_example_7.sh` | Output unconditional probabilities. |
+| `submit_example_8.sh` | Add a global amino acid bias. |
+| `submit_example_pssm.sh` | Add PSSM constraints to assist design. |
+
+Run a single example:
+
+```bash
+bash scripts/infer_examples/submit_example_3.sh
+```
+
+Note: `submit_example_3_score_only_from_fasta.sh` depends on `submit_example_3.sh` first generating `outputs/example_3_outputs/seqs/3HTN.fa`.
+
+## Training
+
+The current example training data is placed in `data/pdb_2021aug02_sample/`. This directory should contain:
+
+```text
+list.csv
+valid_clusters.txt
+test_clusters.txt
+pdb/<2nd-3rd characters of pdbid>/<pdbid>.pt
+pdb/<2nd-3rd characters of pdbid>/<pdbid>_<chain>.pt
+```
+
+Run the training example script directly:
+
+```bash
+cd /path/to/proteinmpnn
+bash scripts/test_train.sh
+```
+
+This script uses the following by default:
+
+```text
+Training data: data/pdb_2021aug02_sample
+Output directory: outputs/train/exp_020/
+Number of samples per epoch: 1000
+Save a checkpoint every 50 epochs
+```
+
+View the training log and weights:
+
+```bash
+cat outputs/train/exp_020/log.txt
+ls outputs/train/exp_020/model_weights
+```
+
+Equivalent command:
+
+```bash
+python scripts/training.py \
+  --path_for_training_data ./data/pdb_2021aug02_sample \
+  --path_for_outputs ./outputs/train/exp_020 \
+  --num_examples_per_epoch 1000 \
+  --save_model_every_n_epochs 50
+```
+
+To resume training, pass:
+
+```bash
+python scripts/training.py \
+  --path_for_training_data ./data/pdb_2021aug02_sample \
+  --path_for_outputs ./outputs/train/exp_020 \
+  --previous_checkpoint ./outputs/train/exp_020/model_weights/epoch_last.pt
+```
+
+## Common Parameters
+
+### Inference Parameters
+
+| Parameter | Description | Default/Example |
+| --- | --- | --- |
+| `--pdb_path` | Input path for a single PDB | `./data/inputs/PDB_monomers/pdbs/5L33.pdb` |
+| `--jsonl_path` | Parsed PDB JSONL input path | Generated by `parse_multiple_chains.py` |
+| `--pdb_path_chains` | Chains to design in single-PDB mode | `"A"` or `"A B"` |
+| `--out_folder` | Inference output directory | `./outputs/test_inference` |
+| `--path_to_model_weights` | Weight directory | `./weight/vanilla_model_weights` |
+| `--model_name` | Weight file name without `.pt` | `v_48_020` |
+| `--num_seq_per_target` | Number of sequences to generate for each target | `2` |
+| `--sampling_temp` | Sampling temperature | `"0.1"` |
+| `--score_only` | Score only, without generating new sequences | `0` or `1` |
+| `--save_score` | Save score files | `0` or `1` |
+| `--save_probs` | Save probability files | `0` or `1` |
+| `--ca_only` | Use the CA-only model | Disabled by default |
+| `--use_soluble_model` | Use the soluble protein model | Disabled by default |
+
+### Training Parameters
+
+| Parameter | Description | Default/Example |
+| --- | --- | --- |
+| `--path_for_training_data` | Preprocessed training data directory | `./data/pdb_2021aug02_sample` |
+| `--path_for_outputs` | Training output directory | `./outputs/train/exp_020` |
+| `--previous_checkpoint` | Checkpoint for resuming training | `epoch_last.pt` |
+| `--num_epochs` | Number of training epochs | Default `200` |
+| `--num_examples_per_epoch` | Number of samples loaded per epoch | Example `1000` |
+| `--batch_size` | Token batch size | Default `10000` |
+| `--save_model_every_n_epochs` | Save a checkpoint every N epochs | `50` in the example script |
+| `--mixed_precision` | Whether to use mixed precision | Default `True` |
+
+## Official OneScience Information
+
+| Platform | Documentation | OneScience Main Repository | Skills Repository |
+| --- | --- | --- | --- |
+| Gitee | https://gitee.com/onescience-ai/onescience-doc | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience-doc | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## Citations and License
+
+- Original ProteinMPNN paper: [Robust deep learning-based protein sequence design using ProteinMPNN](https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1).
+
+- ProteinMPNN-related source code uses the MIT License. See `LICENSE` in the repository root for details. The specific terms of use for model weights and data should follow the instructions provided by the corresponding publishers.
+
+- If you use ProteinMPNN in research, it is recommended to cite the corresponding original ProteinMPNN paper and relevant OneScience project information, and to add citations for downstream analysis tools or datasets according to the actual task.

config/config.yaml

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+model:
+  name: ProteinMPNN
+  package_path: ../model/proteinmpnn
+  base_dependency: OneScience
+runtime:
+  pythonpath: ../model
+  onescience_root_env: ONESCIENCE_ROOT
+inference:
+  entry: ../scripts/inference.py
+  input_dir: ../data/inputs
+  output_dir: ../outputs
+  weight_dir: ../weight/vanilla_model_weights
+  ca_weight_dir: ../weight/ca_model_weights
+  soluble_weight_dir: ../weight/soluble_model_weights
+train:
+  entry: ../scripts/training.py
+  data_dir: ../data/pdb_2021aug02_sample
+  output_dir: ../outputs/train

configuration.json

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+{
+  "model_name": "ProteinMPNN",
+  "framework": "PyTorch",
+  "task": "protein-sequence-design",
+  "entry_points": {
+    "inference": "scripts/inference.py",
+    "train": "scripts/training.py",
+    "helper_scripts": "scripts/helper_scripts"
+  },
+  "source_package": "model/proteinmpnn",
+  "base_dependency": "onescience",
+  "config": "config/config.yaml",
+  "weight_dir": "weight",
+  "license": "MIT"
+}

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data/pdb_2021aug02_sample/README

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+
+Training set for RosettaFold/MPNN etc.
+
+Each PDB entry is represented as a collection of .pt files:
+    PDBID_CHAINID.pt - contains CHAINID chain from PDBID
+    PDBID.pt         - metadata and information on biological assemblies
+
+PDBID_CHAINID.pt has the following fields:
+    seq  - amino acid sequence (string)
+    xyz  - atomic coordinates [L,14,3]
+    mask - boolean mask [L,14]
+    bfac - temperature factors [L,14]
+    occ  - occupancy [L,14] (is 1 for most atoms, <1 if alternative conformations are present)
+
+PDBID.pt:
+    method        - experimental method (str)
+    date          - deposition date (str)
+    resolution    - resolution (float)
+    chains        - list of CHAINIDs (there is a corresponding PDBID_CHAINID.pt file for each of these)
+    tm            - pairwise similarity between chains (TM-score,seq.id.,rmsd from TM-align) [num_chains,num_chains,3]
+    asmb_ids      - biounit IDs as in the PDB (list of str)
+    asmb_details  - how the assembly was identified: author, or software, or smth else (list of str)
+    asmb_method   - PISA or smth else (list of str)
+
+    asmb_chains    - list of chains which each biounit is composed of (list of str, each str contains comma separated CHAINIDs)
+    asmb_xformIDX  - (one per biounit) xforms to be applied to chains from asmb_chains[IDX], [n,4,4]
+                     [n,:3,:3] - rotation matrices
+                     [n,3,:3] - translation vectors
+
+list.csv:
+   CHAINID    - chain label, PDBID_CHAINID
+   DEPOSITION - deposition date
+   RESOLUTION - structure resolution
+   HASH       - unique 6-digit hash for the sequence
+   CLUSTER    - sequence cluster the chain belongs to (clusters were generated at seqID=30%)
+   SEQUENCE   - reference amino acid sequence
+
+
+valid_clusters.txt - clusters used for validation
+
+test_clusters.txt - clusters used for testing

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