pub_soft/foldingdiff
3
0
Fork 0
mirror of https://github.com/microsoft/foldingdiff.git synced 2026-06-04 13:30:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

README updates

Browse Source
This commit is contained in:
Kevin Wu
2022-09-26 13:26:29 -07:00
parent daa881c505
commit 2f41b9b57f
1 changed files with 21 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
README.md
View File

@@ -38,10 +38,21 @@ results/
- training_args.json # Full set of arguments, can be used to reproduce run
```
## Pre-trained models
We provide weihts for a model trained on the CATH dataset. These weights are located under the `models/cath_pretrained` directory. To programmatically load these weights, you can use code defined under `protdiff/modelling.py` as such:
```python
import modelling
modelling.BertForDiffusion.from_dir("models/cath_pretrained").to(torch.device("cuda:0"))
```
Providing this path to premade script such as for sampling is detailed below.
## Downloading data
We requires some data files not packaged on Git due to their large size. These are required to be downloaded locally even
if you are running this on Singularity (as they are uploaded). To download these, do the following:
We requires some data files not packaged on Git due to their large size. These are required to be downloaded locally even if you are not training and are only sampling. The simple command to do this is as follows:
```bash
# Download the CATH dataset
@@ -51,10 +62,10 @@ cd data # Ensure that you are in the data subdirectory within the codebase
## Sampling protein backbones
To sample protein backbones, use the script `bin/sample.py`. An example command to do this is as follows.
To sample protein backbones, use the script `bin/sample.py`. An example command to do this using the pretrained weights described above is as follows.
```bash
python ~/protdiff/bin/sample.py ../projects/models/full_angles/results/ --num 512 --device cuda:3
python ~/projects/protdiff/bin/sample.py ~/projects/protdiff/models/cath_pretrained --num 256 --device cuda:3
```
This will run the model contained in the `results` folder and generate 512 sequences of varying lengths.
@@ -63,15 +74,16 @@ Not specifying a device will default to the first device `cuda:0`; use `--device
```
some_dir/
- plots/ # Contains plots comparing the distribution of training/generated angles
- sampled_angles/ # Contains .npy files with the angles we have sampled
- sampled_pdb/ # Contains the .pdb files resulting from converting the sampled angles to cartesian coordinates
- sampled_angles/ # Contains .csv.gz files with the sampled angles
- sampled_pdb/ # Contains .pdb files from converting the sampled angles to cartesian coordinates
- model_snapshot/ # Contains a copy of the model used to produce results
```
### Maximum training similarity TM scores
After generating sequences, we can calculate TM-scores to evaluate the simliarity of the generated sequences and the original sequences. This is done using the script under `bin/tmscore_training.py`.
### Visualizing "folding" process
### Visualizing diffusion "folding" process
The above sampling code can also be run with the ``--fullhistory`` flag to write an additional subdirectory `sample_history` under each of the `sampled_angles` and `sampled_pdb` folders that contain pdb/csv files coresponding to each timestep in the sampling process. The pdb files, for example, can then be passed into the script under `protdiff/pymol_vis.py` to generate a gif of the folding process (as shown above). An example command to do this is:
@@ -87,7 +99,7 @@ One way to evaluate the quality of generated backbones is via their "designabili
### Inverse folding with ESM
We use a different conda environment for this step; see <https://colab.research.google.com/github/facebookresearch/esm/blob/main/examples/inverse_folding/notebook.ipynb> for setup details. We found that the following command works on our machine:
We use a different conda environment for this step; see <https://colab.research.google.com/github/facebookresearch/esm/blob/main/examples/inverse_folding/notebook.ipynb> for setup details. We found that the following command works on our machines:
```bash
mamba create -n inverse python=3.9 pytorch cudatoolkit pyg -c pytorch -c conda-forge -c pyg
@@ -106,7 +118,7 @@ This creates a new folder, `esm_residues` that contains 10 potential residues fo
### Structural prediction with OmegaFold
We use [OmegaFold](https://github.com/HeliXonProtein/OmegaFold) to fold the amino acid sequences produced above. After creating a separate conda environment and following the authors' instructions for installing OmegaFold, we use the following script to split our input amino acid fasta files across GPUs for inference, and subsequently calculate the self-consistency TM (scTM) scores.
We use [OmegaFold](https://github.com/HeliXonProtein/OmegaFold) to fold the amino acid sequences produced above. After creating and activating a separate conda environment and following the authors' instructions for installing OmegaFold, we use the following script to split our input amino acid fasta files across GPUs for inference, and subsequently calculate the self-consistency TM (scTM) scores.
```bash
# Fold each fasta, spreading the work over GPUs 0 and 1, outputs to omegafold_predictions folder