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jozhang97/
ism_t33_650M_uc30pdb_L1022

Name: ism_t33_650M_uc30pdb_L1022
Availability: InStock

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ISM

By Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao, Philipp Krähenbühl, Adam Klivans, Daniel J. Diaz

This repository contains the model presented in the paper Distilling Structural Representations into Protein Sequence Models. The official github can be found at https://github.com/jozhang97/ism.

TL; DR. ESM2 with enriched structural representations

Quickstart

This quickstart assumes that the user is already working with ESM2 and is interested in replacing ESM with ISM. First, download ISM.

bash

# recommended
huggingface-cli download jozhang97/ism_t33_650M_uc30pdb_L1022 --local-dir /path/to/save/ism

# alternative
git clone https://huggingface.co/jozhang97/ism_t33_650M_uc30pdb_L1022

If the user is starting from fair-esm, add the following lines of code.

python

import esm
model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
ckpt = torch.load('/path/to/ism_t33_650M_uc30pdb_L1022/checkpoint.pth')
model.load_state_dict(ckpt)

If the user is starting from huggingface, replace the model and tokenizer with the following line of code.

python

from transformers import AutoTokenizer, AutoModel
config_path = "/path/to/ism_t33_650M_uc30pdb_L1022/"
model = AutoModel.from_pretrained(config_path)
tokenizer = AutoTokenizer.from_pretrained(config_path)

Please change /path/to/ism_t33_650M_uc30pdb_L1022 to the path where the model is downloaded.

Citing ISM

If you find ISM useful in your research, please consider citing:

bibtex

@article{ouyangzhang2024distilling,
  title={Distilling Structural Representations into Protein Sequence Models},
  author={Ouyang-Zhang, Jeffrey and Gong, Chengyue and Zhao, Yue and Kr{\"a}henb{\"u}hl, Philipp and Klivans, Adam and Diaz, Daniel J},
  journal={bioRxiv},
  doi={10.1101/2024.11.08.622579},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}