proteinmpnn

KO | EN

gitlite — search

#python #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #docker #awesome #awesome-list #security #ios

(★ 40)

our in-house version of ProteinMPNN

proteinmpnn 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ProteinMPNN

This repo includes the Kuhlman Lab fork of ProteinMPNN. It includes all the functionality of the original ProteinMPNN repo (linked [here](https://github.com/dauparas/ProteinMPNN)), with the following additions:
- Improved input parsing for custom design runs
- Multi-state design support
- Additional utilities to provide integration with [EvoPro](https://github.com/Kuhlman-Lab/evopro)

![ProteinMPNN](https://docs.google.com/drawings/d/e/2PACX-1vTtnMBDOq8TpHIctUfGN8Vl32x5ISNcPKlxjcQJF2q70PlaH2uFlj2Ac4s3khnZqG1YxppdMr0iTyk-/pub?w=889&h=358)
Read [ProteinMPNN paper](https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1).

## Installation:

```
git clone git@github.com:Kuhlman-Lab/proteinmpnn.git
cd proteinmpnn
mamba create env -f setup/proteinmpnn.yml
```

### NOTE (July 2025):

ProteinMPNN uses CUDA 11.3, which is too old for the new H100 GPUs (CUDA 11.8+). This means it may hang if run from the default ```mpnn``` environment.

To fix this, we can generate a CUDA 12.4 environment as follows:
```
# Install original env without torch/cuda dependencies
mamba env create -f setup/proteinmpnn_cu12.4.yml -n mpnn_cu12.4

# Install torch/cuda 12.4 dependencies
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124
```

To use this, simply replace ```conda activate mpnn``` with ```conda activate mpnn_cu2.4``` wherever present.

## Usage Guidelines:

### General Usage

The different input arguments available for each script can be viewed by adding `-h` to your python call (e.g., `python generate_json.py -h`).

ProteinMPNN accepts PDB files as input and produces FASTA files as output.

Unlike the original repo, our ProteinMPNN organizes the different input options (aka arguments) into `.flag` files:
- `json.flags` is used to specify design constraints, like fixed residues and symmetry
- `proteinmpnn.flags` is used to specify prediction flags, like which sampling temperature and model variant to use.

In general, there are two steps to running ProteinMPNN:
1. Run the `generate_json.py` script and pass it the `json.flags` file.
- This makes a new file called `proteinmpnn_res_specs.json` containing parsed design information.
2. Run the `run_protein_mpnn.py ` script and pass it `proteinmpnn.flags` and `proteinmpnn_res_specs.json` to obtain the actual ProteinMPNN prediction.

### Useful Flags

Used in `json.flags`:

`--default_design_setting`: this is an optional filter to allow/disallow certain residue types during design. By default, it is set to `all`, which allows all 20 amino acids. Possible settings include:
    `all-hydphob`: exclude hydrophobic residues (`CDEHKNPQRSTX`)
    `all-hydphil`: exclude hydrophilic residues (`ACFGILMPVWYX`)
    `all-CLD`: exclude specific amino acids (in this case, Cys, Leu, and Asp)
    `L+polar`: mix-and-match amino acids and categories (in this case, allow all polar amino acids and also Leu)

Used in `proteinmpnn.flags`:
`--model_name`: specifies which ProteinMPNN model checkpoint to use. Possible options include:
    `v_48_002`: vanilla (default) model with k=48 neighbors and 0.02A noise
    `s_48_010`: soluble protein model with k=48 neighbors and 0.1A noise

`--sampling_temp`: specifies the sampling temperature, which changes how diverse the generated sequences will be. Ranges from 0 to 1, inclusive. A temperature of 0 returns the "best" prediction every time (zero diversity), while a temperature of 1 will return completely random samples. Recommended range is 0.0 - 0.3 or so.

`--dump_probs`: if included, ProteinMPNN will save the predicted sequence probability table for each scaffold. This will be a numpy array of shape [L, 21], for a protein of length L. If multiple sequences are generated per scaffold, probabilities will be averaged before saving. A helper script for visualizing these tables is included at `run/helper_scripts/other_tools/view_probs.py`.

### Example Cases

Example input and expected output files, as well as jobscripts and flag files, for many different design tasks are included in `examples/`. For a summary and explanation of each example, see `examples/EXAMPLES.md`. Currently supported protocols include:

1. Monomer Design (with user-friendly parsing of designable residues)
2. Binder Design
2. Oligomer Design (with support for abitrary symmetries in homooligomers)
3. Multi-state Design (with support for multiple complex design constraints)

-----------------------------------------------------------------------------------------------------

## Unit Testing

TODO

## Code organization:
* `run/run_protein_mpnn.py` - the main script to initialialize and run the model.
* `run/generate_json.py` - function to automatically generate json of design constraints.
* `run/helper_scripts/` - helper functions to parse PDBs, assign which chains to design, which residues to fix, adding AA bias, tying residues etc.
* `examples/` - simple example inputs/outputs and runscripts for different tasks.
* `model_weights/` - trained proteinmpnn model weights.
    * `v_48_...` - vanilla proteinmpnn models trained at different noise levels.
    * `s_48_...` - solublempnn models trained at different noise levels.
    * `ca_48_...` - Ca-only models trained at different noise levels.

## License

ProteinMPNN is distributed under an MIT license, which can be found at `proteinmpnn/LICENSE`. See license file for more details.

proteinmpnn

proteinmpnn 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

proteinmpnn

proteinmpnn 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README