| Name | protenc JSON |
| Version |
0.1.6
JSON |
| download |
| home_page | https://github.com/kklemon/ProtEnc |
| Summary | Extract protein embeddings from protein language models. |
| upload_time | 2023-10-06 08:35:57 |
| maintainer | |
| docs_url | None |
| author | Kristian Klemon |
| requires_python | >=3.10,<3.13 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
ProtEnc: generate protein embeddings the easy way
=======
[ProtEnc](https://github.com/kklemon/ProtEnc) aims to simplify extraction of protein embeddings from various pre-trained models by providing simple APIs and bulk generation scripts for the ever-growing landscape of protein language models (pLMs). Currently, supported models are:
* [ProtTrans](https://github.com/agemagician/ProtTrans) family
* [ESM](https://github.com/facebookresearch/esm)
* AlphaFold (coming soon™)
* [OmegaPLM](https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1) (coming soon™)
Usage
-----
### Installation
```bash
pip install protenc
```
### Python API
```python
import protenc
# List available models
print(protenc.list_models())
# Load encoder model
encoder = protenc.get_encoder('esm2_t30_150M_UR50D', device='cuda')
proteins = [
'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG',
'KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE'
]
for embed in encoder(proteins, return_format='numpy'):
# Embeddings have shape [L, D] where L is the sequence length and D the embedding dimensionality.
print(embed.shape)
# Derive a single per-protein embedding vector by averaging along the sequence dimension
embed.mean(0)
```
### Command-line interface
After installation, use the `protenc` shell command for bulk generation and export of protein embeddings.
```bash
protenc sequences.fasta embeddings.lmdb --model_name=<name-of-model>
```
By default, input and output formats are inferred from the file extensions.
Run
```bash
protenc --help
```
for a detailed usage description.
**Example**
Generate protein embeddings using the ESM2 650M model for sequences provided in a [FASTA](https://en.wikipedia.org/wiki/FASTA_format) file and write embeddings to an [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database):
```bash
protenc proteins.fasta embeddings.lmdb --model_name=esm2_t33_650M_UR50D
```
The generated embeddings will be stored in a lmdb key-value store and can be easily accessed using the `read_from_lmdb` utility function:
```python
from protenc.utils import read_from_lmdb
for label, embed in read_from_lmdb('embeddings.lmdb'):
print(label, embed)
```
**Features**
Input formats:
* CSV
* JSON
* [FASTA](https://en.wikipedia.org/wiki/FASTA_format)
Output format:
* [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database)
* [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) (coming soon)
General:
* Multi-GPU inference with (`--data_parallel`)
* FP16 inference (`--amp`)
Development
-----------
Clone the repository:
```bash
git clone git+https://github.com/kklemon/protenc.git
```
Install dependencies via [Poetry](https://python-poetry.org/):
```bash
poetry install
```
Contribution
------------
Have feature ideas or found a bug? Love to see support for a new model? Feel free to [create an issue](https://github.com/kklemon/ProtEnc/issues/new).
Todo
----
- [ ] Support for more input formats
- [X] CSV
- [ ] Parquet
- [X] FASTA
- [X] JSON
- [ ] Support for more output formats
- [X] LMDB
- [ ] HDF5
- [ ] DataFrame
- [ ] Pickle
- [ ] Support for large models
- [ ] Model offloading
- [ ] Sharding
- [ ] FlashAttention (via Kernl?)
- [ ] Support for more protein language models
- [X] Whole ProtTrans family
- [X] Whole ESM family
- [ ] AlphaFold (?)
- [X] Implement all remaining TODOs in code
- [ ] Evaluation
- [ ] Demos
- [ ] Distributed inference
- [ ] Maybe support some sort of optimized inference such as quantization
- This may be up to the model providers
- [ ] Improve documentation
- [ ] Support translation of gene sequences
- [ ] Add tests. We need tests!!!
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"description": "ProtEnc: generate protein embeddings the easy way\n=======\n\n[ProtEnc](https://github.com/kklemon/ProtEnc) aims to simplify extraction of protein embeddings from various pre-trained models by providing simple APIs and bulk generation scripts for the ever-growing landscape of protein language models (pLMs). Currently, supported models are:\n\n* [ProtTrans](https://github.com/agemagician/ProtTrans) family\n* [ESM](https://github.com/facebookresearch/esm)\n* AlphaFold (coming soon\u2122)\n* [OmegaPLM](https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1) (coming soon\u2122)\n\nUsage\n-----\n\n### Installation\n\n```bash\npip install protenc\n```\n\n### Python API\n\n```python\nimport protenc\n\n# List available models\nprint(protenc.list_models())\n\n# Load encoder model\nencoder = protenc.get_encoder('esm2_t30_150M_UR50D', device='cuda')\n\nproteins = [\n 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG',\n 'KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE'\n]\n\nfor embed in encoder(proteins, return_format='numpy'):\n # Embeddings have shape [L, D] where L is the sequence length and D the embedding dimensionality.\n print(embed.shape)\n \n # Derive a single per-protein embedding vector by averaging along the sequence dimension\n embed.mean(0)\n```\n\n### Command-line interface\n\nAfter installation, use the `protenc` shell command for bulk generation and export of protein embeddings.\n\n```bash\nprotenc sequences.fasta embeddings.lmdb --model_name=<name-of-model>\n```\n\nBy default, input and output formats are inferred from the file extensions.\n\nRun\n\n```bash\nprotenc --help\n```\n\nfor a detailed usage description.\n\n**Example**\n\nGenerate protein embeddings using the ESM2 650M model for sequences provided in a [FASTA](https://en.wikipedia.org/wiki/FASTA_format) file and write embeddings to an [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database):\n\n```bash\nprotenc proteins.fasta embeddings.lmdb --model_name=esm2_t33_650M_UR50D\n```\n\nThe generated embeddings will be stored in a lmdb key-value store and can be easily accessed using the `read_from_lmdb` utility function:\n\n```python\nfrom protenc.utils import read_from_lmdb\n\nfor label, embed in read_from_lmdb('embeddings.lmdb'):\n print(label, embed)\n```\n\n**Features**\n\nInput formats:\n* CSV\n* JSON\n* [FASTA](https://en.wikipedia.org/wiki/FASTA_format)\n\nOutput format:\n* [LMDB](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database)\n* [HDF5](https://en.wikipedia.org/wiki/Hierarchical_Data_Format) (coming soon)\n\nGeneral:\n* Multi-GPU inference with (`--data_parallel`)\n* FP16 inference (`--amp`)\n\nDevelopment\n-----------\n\nClone the repository:\n\n```bash\ngit clone git+https://github.com/kklemon/protenc.git\n```\n\nInstall dependencies via [Poetry](https://python-poetry.org/):\n\n```bash\npoetry install\n```\n\nContribution\n------------\n\nHave feature ideas or found a bug? Love to see support for a new model? Feel free to [create an issue](https://github.com/kklemon/ProtEnc/issues/new).\n\nTodo\n----\n\n- [ ] Support for more input formats\n - [X] CSV\n - [ ] Parquet\n - [X] FASTA\n - [X] JSON\n- [ ] Support for more output formats\n - [X] LMDB\n - [ ] HDF5\n - [ ] DataFrame\n - [ ] Pickle\n- [ ] Support for large models\n - [ ] Model offloading\n - [ ] Sharding\n - [ ] FlashAttention (via Kernl?)\n- [ ] Support for more protein language models\n - [X] Whole ProtTrans family\n - [X] Whole ESM family\n - [ ] AlphaFold (?)\n- [X] Implement all remaining TODOs in code\n- [ ] Evaluation\n- [ ] Demos\n- [ ] Distributed inference\n- [ ] Maybe support some sort of optimized inference such as quantization\n - This may be up to the model providers\n- [ ] Improve documentation\n- [ ] Support translation of gene sequences\n- [ ] Add tests. We need tests!!!\n",
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