![header](imgs/header.jpg)

# AlphaFold

This package provides an implementation of the inference pipeline of AlphaFold
v2.0. This is a completely new model that was entered in CASP14 and published in
Nature. For simplicity, we refer to this model as AlphaFold throughout the rest
of this document.

Any publication that discloses findings arising from using this source code or
the model parameters should [cite](#citing-this-work) the
[AlphaFold paper](https://doi.org/10.1038/s41586-021-03819-2). Please also refer
to the
[Supplementary Information](https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-021-03819-2/MediaObjects/41586_2021_3819_MOESM1_ESM.pdf)
for a detailed description of the method.

**You can use a slightly simplified version of AlphaFold with
[this Colab
notebook](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb)**
or community-supported versions (see below).

![CASP14 predictions](imgs/casp14_predictions.gif)

## First time setup

The following steps are required in order to run AlphaFold:

1.  Install [Docker](https://www.docker.com/).
    *   Install
        [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
        for GPU support.
    *   Setup running
        [Docker as a non-root user](https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user).
1.  Download genetic databases (see below).
1.  Download model parameters (see below).
1.  Check that AlphaFold will be able to use a GPU by running:

    ```bash
    docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
    ```

    The output of this command should show a list of your GPUs. If it doesn't,
    check if you followed all steps correctly when setting up the
    [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
    or take a look at the following
    [NVIDIA Docker issue](https://github.com/NVIDIA/nvidia-docker/issues/1447#issuecomment-801479573).

### Genetic databases

This step requires `aria2c` to be installed on your machine.

AlphaFold needs multiple genetic (sequence) databases to run:

*   [UniRef90](https://www.uniprot.org/help/uniref),
*   [MGnify](https://www.ebi.ac.uk/metagenomics/),
*   [BFD](https://bfd.mmseqs.com/),
*   [Uniclust30](https://uniclust.mmseqs.com/),
*   [PDB70](http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/),
*   [PDB](https://www.rcsb.org/) (structures in the mmCIF format).

We provide a script `scripts/download_all_data.sh` that can be used to download
and set up all of these databases:

*   Default:

    ```bash
    scripts/download_all_data.sh <DOWNLOAD_DIR>
    ```

    will download the full databases.

*   With `reduced_dbs`:

    ```bash
    scripts/download_all_data.sh <DOWNLOAD_DIR> reduced_dbs
    ```

    will download a reduced version of the databases to be used with the
    `reduced_dbs` preset.

We don't provide exactly the versions used in CASP14 -- see the [note on
reproducibility](#note-on-reproducibility). Some of the databases are mirrored
for speed, see [mirrored databases](#mirrored-databases).

:ledger: **Note: The total download size for the full databases is around 415 GB
and the total size when unzipped is 2.2 TB. Please make sure you have a large
enough hard drive space, bandwidth and time to download. We recommend using an
SSD for better genetic search performance.**

This script will also download the model parameter files. Once the script has
finished, you should have the following directory structure:

```
$DOWNLOAD_DIR/                             # Total: ~ 2.2 TB (download: 438 GB)
    bfd/                                   # ~ 1.7 TB (download: 271.6 GB)
        # 6 files.
    mgnify/                                # ~ 64 GB (download: 32.9 GB)
        mgy_clusters_2018_12.fa
    params/                                # ~ 3.5 GB (download: 3.5 GB)
        # 5 CASP14 models,
        # 5 pTM models,
        # LICENSE,
        # = 11 files.
    pdb70/                                 # ~ 56 GB (download: 19.5 GB)
        # 9 files.
    pdb_mmcif/                             # ~ 206 GB (download: 46 GB)
        mmcif_files/
            # About 180,000 .cif files.
        obsolete.dat
    small_bfd/                             # ~ 17 GB (download: 9.6 GB)
        bfd-first_non_consensus_sequences.fasta
    uniclust30/                            # ~ 86 GB (download: 24.9 GB)
        uniclust30_2018_08/
            # 13 files.
    uniref90/                              # ~ 58 GB (download: 29.7 GB)
        uniref90.fasta
```

`bfd/` is only downloaded if you download the full databasees, and `small_bfd/`
is only downloaded if you download the reduced databases.

### Model parameters

While the AlphaFold code is licensed under the Apache 2.0 License, the AlphaFold
parameters are made available for non-commercial use only under the terms of the
CC BY-NC 4.0 license. Please see the [Disclaimer](#license-and-disclaimer) below
for more detail.

The AlphaFold parameters are available from
https://storage.googleapis.com/alphafold/alphafold_params_2021-07-14.tar, and
are downloaded as part of the `scripts/download_all_data.sh` script. This script
will download parameters for:

*   5 models which were used during CASP14, and were extensively validated for
    structure prediction quality (see Jumper et al. 2021, Suppl. Methods 1.12
    for details).
*   5 pTM models, which were fine-tuned to produce pTM (predicted TM-score) and
    predicted aligned error values alongside their structure predictions (see
    Jumper et al. 2021, Suppl. Methods 1.9.7 for details).

## Running AlphaFold

**The simplest way to run AlphaFold is using the provided Docker script.** This
was tested on Google Cloud with a machine using the `nvidia-gpu-cloud-image`
with 12 vCPUs, 85 GB of RAM, a 100 GB boot disk, the databases on an additional
3 TB disk, and an A100 GPU.

1.  Clone this repository and `cd` into it.

    ```bash
    git clone https://github.com/deepmind/alphafold.git
    ```

1.  Modify `DOWNLOAD_DIR` in `docker/run_docker.py` to be the path to the
    directory containing the downloaded databases.
1.  Build the Docker image:

    ```bash
    docker build -f docker/Dockerfile -t alphafold .
    ```

1.  Install the `run_docker.py` dependencies. Note: You may optionally wish to
    create a
    [Python Virtual Environment](https://docs.python.org/3/tutorial/venv.html)
    to prevent conflicts with your system's Python environment.

    ```bash
    pip3 install -r docker/requirements.txt
    ```

1.  Run `run_docker.py` pointing to a FASTA file containing the protein sequence
    for which you wish to predict the structure. If you are predicting the
    structure of a protein that is already in PDB and you wish to avoid using it
    as a template, then `max_template_date` must be set to be before the release
    date of the structure. For example, for the T1050 CASP14 target:

    ```bash
    python3 docker/run_docker.py --fasta_paths=T1050.fasta --max_template_date=2020-05-14
    ```

    By default, Alphafold will attempt to use all visible GPU devices. To use a
    subset, specify a comma-separated list of GPU UUID(s) or index(es) using the
    `--gpu_devices` flag. See
    [GPU enumeration](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/user-guide.html#gpu-enumeration)
    for more details.

1.  You can control AlphaFold speed / quality tradeoff by adding
    `--preset=reduced_dbs`, `--preset=full_dbs` or `--preset=casp14` to the run
    command. We provide the following presets:

    *   **reduced_dbs**: This preset is optimized for speed and lower hardware
        requirements. It runs with a reduced version of the BFD database and
        with no ensembling. It requires 8 CPU cores (vCPUs), 8 GB of RAM, and
        600 GB of disk space.
    *   **full_dbs**: The model in this preset is 8 times faster than the
        `casp14` preset with a very minor quality drop (-0.1 average GDT drop on
        CASP14 domains). It runs with all genetic databases and with no
        ensembling.
    *   **casp14**: This preset uses the same settings as were used in CASP14.
        It runs with all genetic databases and with 8 ensemblings.

    Running the command above with the `casp14` preset would look like this:

    ```bash
    python3 docker/run_docker.py --fasta_paths=T1050.fasta --max_template_date=2020-05-14 --preset=casp14
    ```

### AlphaFold output

The outputs will be in a subfolder of `output_dir` in `run_docker.py`. They
include the computed MSAs, unrelaxed structures, relaxed structures, ranked
structures, raw model outputs, prediction metadata, and section timings. The
`output_dir` directory will have the following structure:

```
<target_name>/
    features.pkl
    ranked_{0,1,2,3,4}.pdb
    ranking_debug.json
    relaxed_model_{1,2,3,4,5}.pdb
    result_model_{1,2,3,4,5}.pkl
    timings.json
    unrelaxed_model_{1,2,3,4,5}.pdb
    msas/
        bfd_uniclust_hits.a3m
        mgnify_hits.sto
        uniref90_hits.sto
```

The contents of each output file are as follows:

*   `features.pkl` – A `pickle` file containing the input feature NumPy arrays
    used by the models to produce the structures.
*   `unrelaxed_model_*.pdb` – A PDB format text file containing the predicted
    structure, exactly as outputted by the model.
*   `relaxed_model_*.pdb` – A PDB format text file containing the predicted
    structure, after performing an Amber relaxation procedure on the unrelaxed
    structure prediction (see Jumper et al. 2021, Suppl. Methods 1.8.6 for
    details).
*   `ranked_*.pdb` – A PDB format text file containing the relaxed predicted
    structures, after reordering by model confidence. Here `ranked_0.pdb` should
    contain the prediction with the highest confidence, and `ranked_4.pdb` the
    prediction with the lowest confidence. To rank model confidence, we use
    predicted LDDT (pLDDT) scores (see Jumper et al. 2021, Suppl. Methods 1.9.6
    for details).
*   `ranking_debug.json` – A JSON format text file containing the pLDDT values
    used to perform the model ranking, and a mapping back to the original model
    names.
*   `timings.json` – A JSON format text file containing the times taken to run
    each section of the AlphaFold pipeline.
*   `msas/` - A directory containing the files describing the various genetic
    tool hits that were used to construct the input MSA.
*   `result_model_*.pkl` – A `pickle` file containing a nested dictionary of the
    various NumPy arrays directly produced by the model. In addition to the
    output of the structure module, this includes auxiliary outputs such as:

    *   Distograms (`distogram/logits` contains a NumPy array of shape [N_res,
        N_res, N_bins] and `distogram/bin_edges` contains the definition of the
        bins).
    *   Per-residue pLDDT scores (`plddt` contains a NumPy array of shape
        [N_res] with the range of possible values from `0` to `100`, where `100`
        means most confident). This can serve to identify sequence regions
        predicted with high confidence or as an overall per-target confidence
        score when averaged across residues.
    *   Present only if using pTM models: predicted TM-score (`ptm` field
        contains a scalar). As a predictor of a global superposition metric,
        this score is designed to also assess whether the model is confident in
        the overall domain packing.
    *   Present only if using pTM models: predicted pairwise aligned errors
        (`predicted_aligned_error` contains a NumPy array of shape [N_res,
        N_res] with the range of possible values from `0` to
        `max_predicted_aligned_error`, where `0` means most confident). This can
        serve for a visualisation of domain packing confidence within the
        structure.

The pLDDT confidence measure is stored in the B-factor field of the output PDB
files (although unlike a B-factor, higher pLDDT is better, so care must be taken
when using for tasks such as molecular replacement).

This code has been tested to match mean top-1 accuracy on a CASP14 test set with
pLDDT ranking over 5 model predictions (some CASP targets were run with earlier
versions of AlphaFold and some had manual interventions; see our forthcoming
publication for details). Some targets such as T1064 may also have high
individual run variance over random seeds.

## Inferencing many proteins

The provided inference script is optimized for predicting the structure of a
single protein, and it will compile the neural network to be specialized to
exactly the size of the sequence, MSA, and templates. For large proteins, the
compile time is a negligible fraction of the runtime, but it may become more
significant for small proteins or if the multi-sequence alignments are already
precomputed. In the bulk inference case, it may make sense to use our
`make_fixed_size` function to pad the inputs to a uniform size, thereby reducing
the number of compilations required.

We do not provide a bulk inference script, but it should be straightforward to
develop on top of the `RunModel.predict` method with a parallel system for
precomputing multi-sequence alignments. Alternatively, this script can be run
repeatedly with only moderate overhead.

## Note on reproducibility

AlphaFold's output for a small number of proteins has high inter-run variance,
and may be affected by changes in the input data. The CASP14 target T1064 is a
notable example; the large number of SARS-CoV-2-related sequences recently
deposited changes its MSA significantly. This variability is somewhat mitigated
by the model selection process; running 5 models and taking the most confident.

To reproduce the results of our CASP14 system as closely as possible you must
use the same database versions we used in CASP. These may not match the default
versions downloaded by our scripts.

For genetics:

*   UniRef90:
    [v2020_01](https://ftp.uniprot.org/pub/databases/uniprot/previous_releases/release-2020_01/uniref/)
*   MGnify:
    [v2018_12](http://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2018_12/)
*   Uniclust30: [v2018_08](http://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/)
*   BFD: [only version available](https://bfd.mmseqs.com/)

For templates:

*   PDB: (downloaded 2020-05-14)
*   PDB70: [2020-05-13](http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/old-releases/pdb70_from_mmcif_200513.tar.gz)

An alternative for templates is to use the latest PDB and PDB70, but pass the
flag `--max_template_date=2020-05-14`, which restricts templates only to
structures that were available at the start of CASP14.

## Citing this work

If you use the code or data in this package, please cite:

```bibtex
@Article{AlphaFold2021,
  author  = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
  journal = {Nature},
  title   = {Highly accurate protein structure prediction with {AlphaFold}},
  year    = {2021},
  doi     = {10.1038/s41586-021-03819-2},
  note    = {(Accelerated article preview)},
}
```

## Community contributions

Colab notebooks provided by the community (please note that these notebooks may
vary from our full AlphaFold system and we did not validate their accuracy):

*   The [ColabFold AlphaFold2 notebook](https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)
    by Martin Steinegger, Sergey Ovchinnikov and Milot Mirdita, which uses an
    API hosted at the Södinglab based on the MMseqs2 server [(Mirdita et al.
    2019, Bioinformatics)](https://academic.oup.com/bioinformatics/article/35/16/2856/5280135)
    for the multiple sequence alignment creation.

## Acknowledgements

AlphaFold communicates with and/or references the following separate libraries
and packages:

*   [Abseil](https://github.com/abseil/abseil-py)
*   [Biopython](https://biopython.org)
*   [Chex](https://github.com/deepmind/chex)
*   [Colab](https://research.google.com/colaboratory/)
*   [Docker](https://www.docker.com)
*   [HH Suite](https://github.com/soedinglab/hh-suite)
*   [HMMER Suite](http://eddylab.org/software/hmmer)
*   [Haiku](https://github.com/deepmind/dm-haiku)
*   [Immutabledict](https://github.com/corenting/immutabledict)
*   [JAX](https://github.com/google/jax/)
*   [Kalign](https://msa.sbc.su.se/cgi-bin/msa.cgi)
*   [matplotlib](https://matplotlib.org/)
*   [ML Collections](https://github.com/google/ml_collections)
*   [NumPy](https://numpy.org)
*   [OpenMM](https://github.com/openmm/openmm)
*   [OpenStructure](https://openstructure.org)
*   [pymol3d](https://github.com/avirshup/py3dmol)
*   [SciPy](https://scipy.org)
*   [Sonnet](https://github.com/deepmind/sonnet)
*   [TensorFlow](https://github.com/tensorflow/tensorflow)
*   [Tree](https://github.com/deepmind/tree)
*   [tqdm](https://github.com/tqdm/tqdm)

We thank all their contributors and maintainers!

## License and Disclaimer

This is not an officially supported Google product.

Copyright 2021 DeepMind Technologies Limited.

### AlphaFold Code License

Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at https://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

### Model Parameters License

The AlphaFold parameters 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

### Third-party software

Use of the third-party software, libraries or code referred to in the
[Acknowledgements](#acknowledgements) section above may be governed by separate
terms and conditions or license provisions. Your use of the third-party
software, libraries or code is subject to any such terms and you should check
that you can comply with any applicable restrictions or terms and conditions
before use.

### Mirrored Databases

The following databases have been mirrored by DeepMind, and are available with reference to the following:

*   [BFD](https://bfd.mmseqs.com/) (unmodified), by Steinegger M. and Söding J., available under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).

*   [BFD](https://bfd.mmseqs.com/) (modified), by Steinegger M. and Söding J., modified by DeepMind, available under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/). See the Methods section of the [AlphaFold proteome paper](https://www.nature.com/articles/s41586-021-03828-1) for details.

*   [Uniclust30: v2018_08](http://wwwuser.gwdg.de/~compbiol/uniclust/2018_08/) (unmodified), by Mirdita M. et al., available under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).

*   [MGnify: v2018_12](http://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/current_release/README.txt) (unmodified), by Mitchell AL et al., available free of all copyright restrictions and made fully and freely available for both non-commercial and commercial use under [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/).
