Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Merge Musse to main branch (#102) #106

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Merge Musse to main branch (#102) #106

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
30 changes: 30 additions & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -243,6 +243,36 @@ bash run_uf_symmetry.sh \

to inference with UF-Symmetry. **Note that the input FASTA file should contain the sequences of the asymmetric unit only, and a symmetry group must be specified for the model.**

## Run Uni-Fold Musse

### Installing Uni-Fold MuSSe

Clone & install unifold.
```shell
git clone --single-branch -b Musse git@github.com:dptech-corp/Uni-Fold.git unifold_musse
cd unifold_musse
pip install -e .
```
### Downloading the pre-trained model parameters
Use the following command to download the parameters of our further pre-trained protein language model and single sequence protein complex predictor:
```shell
# the protein language model
wget https://bioos-hermite-beijing.tos-cn-beijing.volces.com/unifold_model/unifold_musse/plm.pt

# the protein complex predictor
wget https://bioos-hermite-beijing.tos-cn-beijing.volces.com/unifold_model/unifold_musse/mp.pt
```

### Running Uni-Fold MuSSe
Run the following command to predict the structure of the target fasta:
```shell
bash run_unifold_musse.sh \
/path/to/the/input.fasta \ # target fasta file
/path/to/the/output/directory/ \ # output directory
/path/to/multimer_model_parameters.pt \ # multimer predictor parameters
/path/to/pretrain_lm_parameters.pt # language model parameters

```
## Inference on Hermite

We provide covenient structure prediction service on [Hermite™](https://hermite.dp.tech/), a new-generation drug design platform powered by AI, physics, and computing. Users only need to upload sequences of protein monomers and multimers to obtain the predicted structures from Uni-Fold, acompanied by various analyzing tools. [Click here](https://docs.google.com/document/d/1iFdezkKJVuhyqN3WvzsC7-422T-zf18IhP7M9CBj5gs) for more information of how to use Hermite™.
Expand Down