bio-transformers 0.0.2

Wrapper on top of ESM/Protbert model in order to easily work with protein embedding

Description

bio-transformers is a wrapper on top of the ESM/Protbert model, trained on millions on proteins and used to predict embeddings. This package provide other functionalities (like compute the loglikelihood of a protein) or compute embeddings on multiple-gpu.

Installation

It is recommended to work with conda environnements in order to manage the specific dependencies of the package.

  conda create --name bio-transformers python=3.7 -y 
  conda activate bio-transformers
  pip install bio-transformers

How it works

The main class BioTranformers allow the developper to use Protbert and ESM backend

from biotransformers import BioTransformers
BioTransformers.list_backend()

Embeddings

Choose a backend and pass a list of sequences of Amino acids to compute the embeddings. By default, the compute_embeddings function return the <CLS> token embedding. You can add a pooling_list in addition , so you can compute the mean of the tokens embeddings.

from biotransformers import BioTransformers

sequences = [
        "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
        "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
    ]

bio_trans = BioTransformers(model_dir="Rostlab/prot_bert")
embeddings = bio_trans.compute_embeddings(sequences, pooling_list=['mean'])

cls_emb = embeddings['cls']
mean_emb = embeddings['mean']

Loglikelihood

Choose a backend and pass a list of sequences of Amino acids to compute the Loglikelihood.

from biotransformers import BioTransformers

sequences = [
        "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
        "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
    ]

bio_trans = BioTransformers(model_dir="Rostlab/prot_bert")
loglikelihood = bio_trans.compute_loglikelihood(sequences)