TM-Vec: template modeling vectors for fast homology detection and alignment: https://www.biorxiv.org/content/10.1101/2022.07.25.501437v1
First create a conda environment with python=3.9 installed. If you are using cpu, use
conda create -n tmvec faiss-cpu python -c pytorch
If the installation fails, you may need to install mkl via conda install mkl=2021 mkl_fft
Once your conda enviroment is installed and activated (i.e. conda activate tmvec), then install tmvec via
pip install tmvec. If you are using a GPU, you may need to reinstall the gpu version of pytorch.
See the pytorch webpage for more details.
If the computer is connected to the internet, then all the models will be downloaded automatically. If the computer is not connected to the internet, then the models will need to be downloaded manually, and the paths to the models will need to be specified.
tmvec build-db \
--input-fasta small_embed.fasta \
--output db_test/small_fasta
To query a sequences against a database use:
tmvec search \
--query small_embed.fasta \
--database db_test/small_fasta.npz \
--output db_test/result.tsv
We suggest to make first runs on a smaller batches with internet connection. After first run models will be downloaded to cache directory, and afterwards can be manually inputted into CLI in case computing nodes do not have access to the internet.
For CPU execution, we utilize ONNX protein language models, which give a slight speedup. For GPU, we use a standard ProtT5 with torch.compile directive, which seems to be faster than ONNX.