ProteinMPNN
# 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>/