MSA by ML researchers for ML researchers --️ in pytorch/cuda ❤️
# install
pip install sseqs
mamba install -c nvidia cuda-toolkit # or conda install ...
wget https://foldify.org/uniref_bfd_mgy_cf.xbit
# python
from sseqs import msa
msa("HPETLVKVKDAEDQLGARVG"*10, "msa.a3m")
db_len=998 q_len=200: 100%|█████████| 121/121 [00:11<00:00, 10.40GB/s]
# boltz2 msa-server 🔥
DBPATH=uniref_bfd_mgy_cf.xbit python server.py --port 8000
boltz predict demo.fasta --msa_server_url http://0.0.0.0:8000 --use_msa_server
No need for $5h/h server with 1000GB RAM. Developed for $0.3/h rtx4090+128GB RAM.
limitations
- no protein pairing
- sequence length < 1000 (working on 2048)
- 128gb RAM (working on 64GB w/ compression)
- no <16GB approximate version (yet)
- no evals (working on runs'n'poses + antibody)
1 maintainer
Metadata
1 maintainer
Release files for sseqs 0.0.11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sseqs-0.0.11.tar.gz | 61.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sseqs-0.0.11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 136.1 kB
Release files / sseqs-0.0.11.tar.gz
| Download URL | sseqs-0.0.11.tar.gz |
|---|---|
| Size | 61.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.11.0
|
Release files / sseqs-0.0.11-py3-none-any.whl
| Download URL | sseqs-0.0.11-py3-none-any.whl |
|---|---|
| Size | 74.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.0
|
