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https://github.com/HeliXonProtein/OmegaFold

OmegaFold Release Code
https://github.com/HeliXonProtein/OmegaFold

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OmegaFold Release Code

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# OmegaFold: High-resolution de novo Structure Prediction from Primary Sequence



#### This is the release code for paper [High-resolution de novo structure prediction from primary sequence](https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1).



We will continue to optimize this repository for more ease of use, for

instance, reducing the GRAM required to inference long proteins and

releasing possibly stronger models.



## Update Notes



### Model 2 release notes Dec 9. 2022



Now you can use model 2 by setting `--model 2` in the command line!



### Huge GRAM reduction



We have optimized (to some extent) the GRAM usage of OmegaFold model in our

latest release. Now the model can inference protein sequence as long as

_4096_ on NVIDIA A100 Graphics card with 80 GB of memory with

`--subbatch_size` set to 448 without hitting full memory.

This version's model is more sensitive to `--subbatch_size`.



### Setting Subbatch



Subbatch makes a trade-off between time and space.

One can greatly reduce the space requirements by setting `--subbatch_size`

very low.

The default is the number of residues in the sequence and the lowest

possible number is 1.

For now we do not have a rule of thumb for setting the `--subbatch_size`,

but we suggest half the value if you run into GPU memory limitations.



### MacOS Users



For macOS users, we support MPS (Apple Silicon) acceleration if the user

installs the latest nightly version of PyTorch.

Also, current code also requires macOS users need to `git clone` the

repository and use `python main.

py` (see below) to run the model.



## Setup



To prepare the environment to run OmegaFold,



- from source



```commandline

pip install git+https://github.com/HeliXonProtein/OmegaFold.git

```



- clone the repository



```commandline

git clone https://github.com/HeliXonProtein/OmegaFold

cd OmegaFold

python setup.py install

```



should get you where you want.



The `INPUT_FILE.fasta` should be a normal fasta file with possibly many

sequences with a comment line starting with `>` or `:` above the amino

acid sequence itself.



This command will download the weight

from https://helixon.s3.amazonaws.com/release1.pt

to `~/.cache/omegafold_ckpt/model.pt`

and load the model



## Running



You could simply



```commandline

omegafold INPUT_FILE.fasta OUTPUT_DIRECTORY

```



And voila!



### Alternatively (Or MacOS users)



Even if this failed, since we use minimal 3rd party libraries, you can

always just install the latest

[PyTorch](https://pytorch.org) and [biopython](https://biopython.org)

(and that's it!) yourself.

For mps accelerator, macOS users may need to install the lastest nightly

version of PyTorch.

In this case, you could run



```commandline

python main.py INPUT_FILE.fasta OUTPUT_DIRECTORY

```



### Notes on resources



However, since we have implemented sharded execution, it is possible to



1. trade computation time for GRAM: by changing `--subbatch_size`. The

   smaller

   this value is, the longer the execution can take, and the less memory is

   required, or,

2. trade computation time for average prediction quality, by changing

   `--num_cycle`



For more information, run



```commandline

omegafold --help

```



where we provide several options for both speed and weights utilities.



## Output



We produce one pdb for each of the sequences in `INPUT_FILE.fasta` saved in

the `OUTPUT_DIRECTORY`. We also put our confidence value the place of

b_factors in pdb files.



## Cite



If this is helpful to you, please consider citing the paper with



```tex

@article{OmegaFold,

	author = {Wu, Ruidong and Ding, Fan and Wang, Rui and Shen, Rui and Zhang, Xiwen and Luo, Shitong and Su, Chenpeng and Wu, Zuofan and Xie, Qi and Berger, Bonnie and Ma, Jianzhu and Peng, Jian},

	title = {High-resolution de novo structure prediction from primary sequence},

	elocation-id = {2022.07.21.500999},

	year = {2022},

	doi = {10.1101/2022.07.21.500999},

	publisher = {Cold Spring Harbor Laboratory},

	URL = {https://www.biorxiv.org/content/early/2022/07/22/2022.07.21.500999},

	eprint = {https://www.biorxiv.org/content/early/2022/07/22/2022.07.21.500999.full.pdf},

	journal = {bioRxiv}

}



```



## Note



Also some of the comments might be out-of-date as of now, and will be

updated very soon