ProtFlash 0.1.1

ProtFlash: A lightweight protein language model

Install

As a prerequisite, you must have PyTorch installed to use this repository.

You can use this one-liner for installation, using the latest release version

# latest version
pip install git+https://github.com/isyslab-hust/ProtFlash

# stable version
pip install ProtFlash

Model details

Usage

protein sequence embedding

from ProtFlash.pretrain import load_prot_flash_base
from ProtFlash.utils import batchConverter
data = [
    ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
    ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
]
ids, batch_token, lengths = batchConverter(data)
model = load_prot_flash_base()
with torch.no_grad():
    token_embedding = model(batch_token, lengths)
# Generate per-sequence representations via averaging
sequence_representations = []
for i, (_, seq) in enumerate(data):
    sequence_representations.append(token_embedding[i, 0: len(seq) + 1].mean(0))

loading weight files

import torch
from ProtFlash.model import FLASHTransformer

model_data = torch.load(your_parameters_file)
hyper_parameter = model_data["hyper_parameters"]
model = FLASHTransformer(hyper_parameter['dim'], hyper_parameter['num_tokens'], hyper_parameter         ['num_layers'], group_size=hyper_parameter['num_tokens'],
                             query_key_dim=hyper_parameter['qk_dim'], max_rel_dist=hyper_parameter['max_rel_dist'], expansion_factor=hyper_parameter['expansion_factor'])

model.load_state_dict(model_data['state_dict'])

License

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Citation

If you use this code or one of our pretrained models for your publication, please cite our paper:

Lei Wang, Hui Zhang, Wei Xu, Zhidong Xue, and Yan Wang. ProtFlash: Deciphering the protein landscape with a novel and lightweight language model, Under revision (2023)