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Augustin Zidek
2020-01-15 15:11:08 +00:00
committed by Diego de Las Casas
parent 32abf74fd5
commit c8052237b8
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@@ -4,12 +4,15 @@ This package provides an implementation of the contact prediction network,
associated model weights and CASP13 dataset as published in Nature.
Any publication that discloses findings arising from using this source code must
cite *AlphaFold: Protein structure prediction using potentials from deep
learning* by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick,
Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson,
Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan,
cite *Improved protein structure prediction using potentials from deep learning*
by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent
Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex
Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan,
Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
The paper is available at https://www.nature.com/articles/s41586-019-1923-7 (DOI
10.1038/s41586-019-1923-7).
## Setup
### Dependencies
@@ -24,8 +27,9 @@ Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
2.0+.
* [TensorFlow Probability 0.7.0](https://www.tensorflow.org/probability)
You can set up Python virtual environment with these dependencies inside the
forked `deepmind_research` repository using:
You can set up Python virtual environment (you might need to install the
`python3-venv` package first) with all needed dependencies inside the forked
`deepmind_research` repository using:
```shell
python3 -m venv alphafold_venv
@@ -34,33 +38,32 @@ pip install wheel
pip install -r alphafold_casp13/requirements.txt
```
Alternatively, you can just use the `run_eval.sh` script provided which runs
these commands for you, see the section on running the system below for more
Alternatively, you can just use the `run_eval.sh` script provided which will run
these commands for you. See the section on running the system below for more
details.
## Data
While the code is licensed under the Apache License, the AlphaFold weights and
data are made available for non-commercial use only under the terms of the
While the code is licensed under the Apache 2.0 License, the AlphaFold weights
and data are made available for non-commercial use only under the terms of the
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
license. You can find details at:
https://creativecommons.org/licenses/by-nc/4.0/legalcode
In order to download the AlphaFold weights and data, you will need to request
access using the
[request form](https://docs.google.com/forms/d/1yrZXhQfSlwYnouDujrL2RkZKVBjF5AjomyF_RJ95dew/).
Once you have obtained access, you can download the data from
[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
You can download the data from:
* http://bit.ly/alphafold-data-license: The data license file.
* http://bit.ly/alphafold-data-casp13: The dataset to reproduce AlphaFold's
CASP13 results.
* http://bit.ly/alphafold-data-weights: The model checkpoints.
### Input data
The dataset to reproduce AlphaFold's CASP13 results can be downloaded from
[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
The dataset is in a file called `casp13_data.zip` which has about **43.5 GB**.
http://bit.ly/alphafold-data-casp13. The dataset is in a single zip file called
`casp13_data.zip` which has about **43.5 GB**.
The zip file contains 1 directory for each CASP13 target and a `LICENSE.md`
The zip file contains 1 directory for each CASP13 target and a `LICENSE.txt`
file. Each target directory contains the following files:
1. `TARGET.tfrec` file. This is a
@@ -84,9 +87,8 @@ targets to get the contact map.
### Model checkpoints
The model checkpoints can be downloaded from
[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
The model checkpoints are in a file called `alphafold_casp13_weights.zip` which
has about **210 MB**.
http://bit.ly/alphafold-data-weights. The model checkpoints are in a zip file
called `alphafold_casp13_weights.zip` which has about **210 MB**.
The zip file contains:
@@ -94,7 +96,7 @@ The zip file contains:
1. A directory `916425`. This contains the weights for the background distogram
model.
1. A directory `941521`. This contains the weights for the torsion model.
1. `LICENSE.md`. The model checkpoints have a non-commercial license which is
1. `LICENSE.txt`. The model checkpoints have a non-commercial license which is
defined in this file.
Each directory with model weights contains a number of different model
@@ -109,15 +111,18 @@ used for feature normalization specific to that model.
You can use the `run_eval.sh` script to run the entire Distogram prediction
system. There are a few steps you need to start with:
1. Download the input data as described above. Unpack the data in the
directory with the code.
1. Download the input data as described above. Unpack the data in the directory
with the code.
1. Download the model checkpoints as described above. Unpack the data.
1. In `run_eval.sh` set the following:
* `DISTOGRAM_MODEL` to the path to the directory with the distogram model.
* `BACKGROUND_MODEL` to the path to the directory with the background
model.
* `TORSION_MODEL` to the path to the directory with the torsion model.
* `TARGET` to the path to the directory with the target input data.
* `TARGET` to the name of the target.
* `TARGET_PATH` to the path to the directory with the target input data.
* `OUTPUT_DIR` is by default set to a new directory with a timestamp
within your home directory.
Then run `alphafold_casp13/run_eval.sh` from the `deepmind_research` parent
directory (you will get errors if you try running `run_eval.sh` directly from
@@ -133,8 +138,8 @@ The contact prediction works in the following way:
1. 1 replica is launched to predict the torsions.
1. The predictions from the different replicas are averaged together using
`ensemble_contact_maps.py`.
1. The predictions for the 64 × 64 distogram crops are pasted together using
`paste_contact_maps.py`.
1. The predictions for the 64 × 64, 128 × 128 and 256 × 256 distogram crops are
pasted together using `paste_contact_maps.py`.
When running `run_eval.sh` the output has the following directory structure:
@@ -149,7 +154,8 @@ When running `run_eval.sh` the output has the following directory structure:
* **torsion/**: Contains 1 subfolder as there was only a single replica. This
folder contains contains the predicted ASA, secondary structure, backbone
torsions and a pickle file with the distogram for each crop. It also
contains an `ensemble` directory with the ensembled torsions.
contains an `ensemble` directory, which contains a copy of the predicted
output as there is only a single replica in this case.
* **pasted/**: Contains distograms obtained from the ensembled distograms by
pasting. An RR contact map file is computed from this pasted distogram.
**This is the final distogram that was used in the subsequent AlphaFold
@@ -159,6 +165,7 @@ When running `run_eval.sh` the output has the following directory structure:
We used a version of [PDB](https://www.rcsb.org/) downloaded on 2018-03-15. The
train/test split can be found in the `train_domains.txt` and `test_domains.txt`
files.
files in this repository. The split is based on the
[CATH 2018-03-16](https://www.cathdb.info/) database.
Disclaimer: This is not an official Google product.