huangtinglin/revisiting-plms

Exploring Evolution-aware & free protein language models as protein function predictors

Exploring Evolution-aware & free protein language models as protein function predictors

Env:

Jax(Alphafold2):

https://github.com/kalininalab/alphafold_non_docker

Pytorch(ESM-1b,MSA-Transformer):

1. As a prerequisite, you must have PyTorch installed(https://pytorch.org/get-started/locally/).

2. pip install fair-esm # latest release, OR:

   pip install git+https://github.com/facebookresearch/esm.git

data:

For SSP & Contact map:

ESMStructuralSplitDataset:

Name	Description	URL
splits	train/valid splits	https://dl.fbaipublicfiles.com/fair-esm/structural-data/splits.tar.gz
pkl	pkl objects containing sequence, SSP labels, distance map, and 3d coordinates	https://dl.fbaipublicfiles.com/fair-esm/structural-data/pkl.tar.gz
msas	a3m files containing MSA for each domain	https://dl.fbaipublicfiles.com/fair-esm/structural-data/msas.tar.gz

from https://github.com/facebookresearch/esm

For Contact map Test:

CAMEO(https://www.cameo3d.org/)

For Fitness dataset:

Tape(https://github.com/songlab-cal/tape)

For pretrain ESM-1b & MSA-Transformer

Alphafold2 training data:

https://registry.opendata.aws/openfold/

from Openfold(https://github.com/aqlaboratory/openfold)

@article{hu2022exploring,
  title={Exploring evolution-aware \&-free protein language models as protein function predictors},
  author={Hu, Mingyang and Yuan, Fajie and Yang, Kevin K and Ju, Fusong and Su, Jin and Wang, Hui and Yang, Fei and Ding, Qiuyang},
  journal={arXiv preprint arXiv:2206.06583},
  year={2022}
}