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https://github.com/google-deepmind/alphafold

Open source code for AlphaFold.
https://github.com/google-deepmind/alphafold

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# AlphaFold



This package provides an implementation of the inference pipeline of AlphaFold

v2. For simplicity, we refer to this model as AlphaFold throughout the rest of

this document.



We also provide:



1.  An implementation of AlphaFold-Multimer. This represents a work in progress

    and AlphaFold-Multimer isn't expected to be as stable as our monomer

    AlphaFold system. [Read the guide](#updating-existing-installation) for how

    to upgrade and update code.

2.  The [technical note](docs/technical_note_v2.3.0.md) containing the models

    and inference procedure for an updated AlphaFold v2.3.0.

3.  A [CASP15 baseline](docs/casp15_predictions.zip) set of predictions along

    with documentation of any manual interventions performed.



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) and, if

applicable, the

[AlphaFold-Multimer paper](https://www.biorxiv.org/content/10.1101/2021.10.04.463034v1).



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).



If you have any questions, please contact the AlphaFold team at

[[email protected]](mailto:[email protected]).



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



## Installation and running your first prediction



You will need a machine running Linux, AlphaFold does not support other

operating systems. Full installation requires up to 3 TB of disk space to keep

genetic databases (SSD storage is recommended) and a modern NVIDIA GPU (GPUs

with more memory can predict larger protein structures).



Please follow these steps:



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.  Clone this repository and `cd` into it.



    ```bash

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

    cd ./alphafold

    ```



1.  Download genetic databases and model parameters:



    *   Install `aria2c`. On most Linux distributions it is available via the

    package manager as the `aria2` package (on Debian-based distributions this

    can be installed by running `sudo apt install aria2`).



    *   Please use the script `scripts/download_all_data.sh` to download

    and set up full databases. This may take substantial time (download size is

    556 GB), so we recommend running this script in the background:



    ```bash

    scripts/download_all_data.sh  > download.log 2> download_all.log &

    ```



    *   **Note: The download directory `` should *not* be a

    subdirectory in the AlphaFold repository directory.** If it is, the Docker

    build will be slow as the large databases will be copied into the docker

    build context.



    *   It is possible to run AlphaFold with reduced databases; please refer to

    the [complete documentation](#genetic-databases).



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).



    If you wish to run AlphaFold using Singularity (a common containerization

    platform on HPC systems) we recommend using some of the third party Singularity

    setups as linked in https://github.com/deepmind/alphafold/issues/10 or

    https://github.com/deepmind/alphafold/issues/24.



1.  Build the Docker image:



    ```bash

    docker build -f docker/Dockerfile -t alphafold .

    ```



    If you encounter the following error:



    ```

    W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

    E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease' is not signed.

    ```



    use the workaround described in

    https://github.com/deepmind/alphafold/issues/463#issuecomment-1124881779.



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.  Make sure that the output directory exists (the default is `/tmp/alphafold`)

    and that you have sufficient permissions to write into it.



1.  Run `run_docker.py` pointing to a FASTA file containing the protein

    sequence(s) for which you wish to predict the structure (`--fasta_paths`

    parameter). AlphaFold will search for the available templates before the

    date specified by the `--max_template_date` parameter; this could be used to

    avoid certain templates during modeling. `--data_dir` is the directory with

    downloaded genetic databases and `--output_dir` is the absolute path to the

    output directory.



    ```bash

    python3 docker/run_docker.py \

      --fasta_paths=your_protein.fasta \

      --max_template_date=2022-01-01 \

      --data_dir=$DOWNLOAD_DIR \

      --output_dir=/home/user/absolute_path_to_the_output_dir

    ```



1.  Once the run is over, the output directory shall contain predicted

    structures of the target protein. Please check the documentation below for

    additional options and troubleshooting tips.



### Genetic databases



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



AlphaFold needs multiple genetic (sequence) databases to run:



*   [BFD](https://bfd.mmseqs.com/),

*   [MGnify](https://www.ebi.ac.uk/metagenomics/),

*   [PDB70](http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/),

*   [PDB](https://www.rcsb.org/) (structures in the mmCIF format),

*   [PDB seqres](https://www.rcsb.org/) – only for AlphaFold-Multimer,

*   [UniRef30 (FKA UniClust30)](https://uniclust.mmseqs.com/),

*   [UniProt](https://www.uniprot.org/uniprot/) – only for AlphaFold-Multimer,

*   [UniRef90](https://www.uniprot.org/help/uniref).



We provide a script `scripts/download_all_data.sh` that can be used to download

and set up all of these databases:



*   Recommended default:



    ```bash

    scripts/download_all_data.sh 

    ```



    will download the full databases.



*   With `reduced_dbs` parameter:



    ```bash

    scripts/download_all_data.sh  reduced_dbs

    ```



    will download a reduced version of the databases to be used with the

    `reduced_dbs` database preset. This shall be used with the corresponding

    AlphaFold parameter `--db_preset=reduced_dbs` later during the AlphaFold run

    (please see [AlphaFold parameters](#running-alphafold) section).



:ledger: **Note: The download directory `` should *not* be a

subdirectory in the AlphaFold repository directory.** If it is, the Docker build

will be slow as the large databases will be copied during the image creation.



We don't provide exactly the database versions used in CASP14 – see the

[note on reproducibility](#note-on-casp14-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 556 GB

and the total size when unzipped is 2.62 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.**



:ledger: **Note: If the download directory and datasets don't have full read and

write permissions, it can cause errors with the MSA tools, with opaque

(external) error messages. Please ensure the required permissions are applied,

e.g. with the `sudo chmod 755 --recursive "$DOWNLOAD_DIR"` command.**



The `download_all_data.sh` script will also download the model parameter files.

Once the script has finished, you should have the following directory structure:



```

$DOWNLOAD_DIR/                             # Total: ~ 2.62 TB (download: 556 GB)

    bfd/                                   # ~ 1.8 TB (download: 271.6 GB)

        # 6 files.

    mgnify/                                # ~ 120 GB (download: 67 GB)

        mgy_clusters_2022_05.fa

    params/                                # ~ 5.3 GB (download: 5.3 GB)

        # 5 CASP14 models,

        # 5 pTM models,

        # 5 AlphaFold-Multimer models,

        # LICENSE,

        # = 16 files.

    pdb70/                                 # ~ 56 GB (download: 19.5 GB)

        # 9 files.

    pdb_mmcif/                             # ~ 238 GB (download: 43 GB)

        mmcif_files/

            # About 199,000 .cif files.

        obsolete.dat

    pdb_seqres/                            # ~ 0.2 GB (download: 0.2 GB)

        pdb_seqres.txt

    small_bfd/                             # ~ 17 GB (download: 9.6 GB)

        bfd-first_non_consensus_sequences.fasta

    uniref30/                              # ~ 206 GB (download: 52.5 GB)

        # 7 files.

    uniprot/                               # ~ 105 GB (download: 53 GB)

        uniprot.fasta

    uniref90/                              # ~ 67 GB (download: 34 GB)

        uniref90.fasta

```



`bfd/` is only downloaded if you download the full databases, 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 and CASP15 prediction data are made available under the terms of the

CC BY 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_2022-12-06.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

    (PAE) predicted aligned error values alongside their structure predictions

    (see Jumper et al. 2021, Suppl. Methods 1.9.7 for details).

*   5 AlphaFold-Multimer models that produce pTM and PAE values alongside their

    structure predictions.



### Updating existing installation



If you have a previous version you can either reinstall fully from scratch

(remove everything and run the setup from scratch) or you can do an incremental

update that will be significantly faster but will require a bit more work. Make

sure you follow these steps in the exact order they are listed below:



1.  **Update the code.**

    *   Go to the directory with the cloned AlphaFold repository and run `git

        fetch origin main` to get all code updates.

1.  **Update the UniProt, UniRef, MGnify and PDB seqres databases.**

    *   Remove `/uniprot`.

    *   Run `scripts/download_uniprot.sh `.

    *   Remove `/uniclust30`.

    *   Run `scripts/download_uniref30.sh `.

    *   Remove `/uniref90`.

    *   Run `scripts/download_uniref90.sh `.

    *   Remove `/mgnify`.

    *   Run `scripts/download_mgnify.sh `.

    *   Remove `/pdb_mmcif`. It is needed to have PDB SeqRes and

        PDB from exactly the same date. Failure to do this step will result in

        potential errors when searching for templates when running

        AlphaFold-Multimer.

    *   Run `scripts/download_pdb_mmcif.sh `.

    *   Run `scripts/download_pdb_seqres.sh `.

1.  **Update the model parameters.**

    *   Remove the old model parameters in `/params`.

    *   Download new model parameters using

        `scripts/download_alphafold_params.sh `.

1.  **Follow [Running AlphaFold](#running-alphafold).**



#### Using deprecated model weights



To use the deprecated v2.2.0 AlphaFold-Multimer model weights:



1.  Change `SOURCE_URL` in `scripts/download_alphafold_params.sh` to

    `https://storage.googleapis.com/alphafold/alphafold_params_2022-03-02.tar`,

    and download the old parameters.

2.  Change the `_v3` to `_v2` in the multimer `MODEL_PRESETS` in `config.py`.



To use the deprecated v2.1.0 AlphaFold-Multimer model weights:



1.  Change `SOURCE_URL` in `scripts/download_alphafold_params.sh` to

    `https://storage.googleapis.com/alphafold/alphafold_params_2022-01-19.tar`,

    and download the old parameters.

2.  Remove the `_v3` in the multimer `MODEL_PRESETS` in `config.py`.



## 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. For your first run, please follow the instructions

from [Installation and running your first prediction](#installation-and-running-your-first-prediction)

section.



1.  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 which AlphaFold model to run by adding the `--model_preset=`

    flag. We provide the following models:



    *   **monomer**: This is the original model used at CASP14 with no

        ensembling.



    *   **monomer\_casp14**: This is the original model used at CASP14 with

        `num_ensemble=8`, matching our CASP14 configuration. This is largely

        provided for reproducibility as it is 8x more computationally expensive

        for limited accuracy gain (+0.1 average GDT gain on CASP14 domains).



    *   **monomer\_ptm**: This is the original CASP14 model fine tuned with the

        pTM head, providing a pairwise confidence measure. It is slightly less

        accurate than the normal monomer model.



    *   **multimer**: This is the [AlphaFold-Multimer](#citing-this-work) model.

        To use this model, provide a multi-sequence FASTA file. In addition, the

        UniProt database should have been downloaded.



1.  You can control MSA speed/quality tradeoff by adding

    `--db_preset=reduced_dbs` or `--db_preset=full_dbs` 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. It

        requires 8 CPU cores (vCPUs), 8 GB of RAM, and 600 GB of disk space.



    *   **full\_dbs**: This runs with all genetic databases used at CASP14.



    Running the command above with the `monomer` model preset and the

    `reduced_dbs` data preset would look like this:



    ```bash

    python3 docker/run_docker.py \

      --fasta_paths=T1050.fasta \

      --max_template_date=2020-05-14 \

      --model_preset=monomer \

      --db_preset=reduced_dbs \

      --data_dir=$DOWNLOAD_DIR \

      --output_dir=/home/user/absolute_path_to_the_output_dir

    ```



1.  After generating the predicted model, AlphaFold runs a relaxation

    step to improve local geometry. By default, only the best model (by

    pLDDT) is relaxed (`--models_to_relax=best`), but also all of the models

    (`--models_to_relax=all`) or none of the models (`--models_to_relax=none`)

    can be relaxed.



1.  The relaxation step can be run on GPU (faster, but could be less stable) or

    CPU (slow, but stable). This can be controlled with `--enable_gpu_relax=true`

    (default) or `--enable_gpu_relax=false`.



1.  AlphaFold can re-use MSAs (multiple sequence alignments) for the same

    sequence via `--use_precomputed_msas=true` option; this can be useful for

    trying different AlphaFold parameters. This option assumes that the

    directory structure generated by the first AlphaFold run in the output

    directory exists and that the protein sequence is the same.



### Running AlphaFold-Multimer



All steps are the same as when running the monomer system, but you will have to



*   provide an input fasta with multiple sequences,

*   set `--model_preset=multimer`,



An example that folds a protein complex `multimer.fasta`:



```bash

python3 docker/run_docker.py \

  --fasta_paths=multimer.fasta \

  --max_template_date=2020-05-14 \

  --model_preset=multimer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



By default the multimer system will run 5 seeds per model (25 total predictions)

for a small drop in accuracy you may wish to run a single seed per model. This

can be done via the `--num_multimer_predictions_per_model` flag, e.g. set it to

`--num_multimer_predictions_per_model=1` to run a single seed per model.



### AlphaFold prediction speed



The table below reports prediction runtimes for proteins of various lengths. We

only measure unrelaxed structure prediction with three recycles while

excluding runtimes from MSA and template search. When running

`docker/run_docker.py` with `--benchmark=true`, this runtime is stored in

`timings.json`. All runtimes are from a single A100 NVIDIA GPU. Prediction

speed on A100 for smaller structures can be improved by increasing

`global_config.subbatch_size` in `alphafold/model/config.py`.



No. residues | Prediction time (s)

-----------: | ------------------:

100          | 4.9

200          | 7.7

300          | 13

400          | 18

500          | 29

600          | 36

700          | 53

800          | 60

900          | 91

1,000        | 96

1,100        | 140

1,500        | 280

2,000        | 450

2,500        | 969

3,000        | 1,240

3,500        | 2,465

4,000        | 5,660

4,500        | 12,475

5,000        | 18,824



### Examples



Below are examples on how to use AlphaFold in different scenarios.



#### Folding a monomer



Say we have a monomer with the sequence ``. The input fasta should be:



```fasta

>sequence_name



```



Then run the following command:



```bash

python3 docker/run_docker.py \

  --fasta_paths=monomer.fasta \

  --max_template_date=2021-11-01 \

  --model_preset=monomer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



#### Folding a homomer



Say we have a homomer with 3 copies of the same sequence ``. The input

fasta should be:



```fasta

>sequence_1



>sequence_2



>sequence_3



```



Then run the following command:



```bash

python3 docker/run_docker.py \

  --fasta_paths=homomer.fasta \

  --max_template_date=2021-11-01 \

  --model_preset=multimer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



#### Folding a heteromer



Say we have an A2B3 heteromer, i.e. with 2 copies of `` and 3 copies

of ``. The input fasta should be:



```fasta

>sequence_1



>sequence_2



>sequence_3



>sequence_4



>sequence_5



```



Then run the following command:



```bash

python3 docker/run_docker.py \

  --fasta_paths=heteromer.fasta \

  --max_template_date=2021-11-01 \

  --model_preset=multimer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



#### Folding multiple monomers one after another



Say we have a two monomers, `monomer1.fasta` and `monomer2.fasta`.



We can fold both sequentially by using the following command:



```bash

python3 docker/run_docker.py \

  --fasta_paths=monomer1.fasta,monomer2.fasta \

  --max_template_date=2021-11-01 \

  --model_preset=monomer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



#### Folding multiple multimers one after another



Say we have a two multimers, `multimer1.fasta` and `multimer2.fasta`.



We can fold both sequentially by using the following command:



```bash

python3 docker/run_docker.py \

  --fasta_paths=multimer1.fasta,multimer2.fasta \

  --max_template_date=2021-11-01 \

  --model_preset=multimer \

  --data_dir=$DOWNLOAD_DIR \

  --output_dir=/home/user/absolute_path_to_the_output_dir

```



### AlphaFold output



The outputs will be saved in a subdirectory of the directory provided via the

`--output_dir` flag of `run_docker.py` (defaults to `/tmp/alphafold/`). The

outputs 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:



```

/

    features.pkl

    ranked_{0,1,2,3,4}.pdb

    ranking_debug.json

    relax_metrics.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_uniref_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 predicted structures,

    after reordering by model confidence. Here `ranked_i.pdb` should contain

    the prediction with the (`i + 1`)-th highest confidence (so that

    `ranked_0.pdb` has the highest confidence). To rank model confidence, we use

    predicted LDDT (pLDDT) scores (see Jumper et al. 2021, Suppl. Methods 1.9.6

    for details). If `--models_to_relax=all` then all ranked structures are

    relaxed. If `--models_to_relax=best` then only `ranked_0.pdb` is relaxed

    (the rest are unrelaxed). If `--models_to_relax=none`, then the ranked

    structures are all unrelaxed.

*   `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.

*   `relax_metrics.json` – A JSON format text file containing relax metrics, for

    instance remaining violations.

*   `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 CASP14 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},

  volume  = {596},

  number  = {7873},

  pages   = {583--589},

  doi     = {10.1038/s41586-021-03819-2}

}

```



In addition, if you use the AlphaFold-Multimer mode, please cite:



```bibtex

@article {AlphaFold-Multimer2021,

  author       = {Evans, Richard and O{\textquoteright}Neill, Michael and Pritzel, Alexander and Antropova, Natasha and Senior, Andrew and Green, Tim and {\v{Z}}{\'\i}dek, Augustin and Bates, Russ and Blackwell, Sam and Yim, Jason and Ronneberger, Olaf and Bodenstein, Sebastian and Zielinski, Michal and Bridgland, Alex and Potapenko, Anna and Cowie, Andrew and Tunyasuvunakool, Kathryn and Jain, Rishub and Clancy, Ellen and Kohli, Pushmeet and Jumper, John and Hassabis, Demis},

  journal      = {bioRxiv},

  title        = {Protein complex prediction with AlphaFold-Multimer},

  year         = {2021},

  elocation-id = {2021.10.04.463034},

  doi          = {10.1101/2021.10.04.463034},

  URL          = {https://www.biorxiv.org/content/early/2021/10/04/2021.10.04.463034},

  eprint       = {https://www.biorxiv.org/content/early/2021/10/04/2021.10.04.463034.full.pdf},

}

```



## 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)

*   [pandas](https://pandas.pydata.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!



## Get in Touch



If you have any questions not covered in this overview, please contact the

AlphaFold team at [[email protected]](mailto:[email protected]).



We would love to hear your feedback and understand how AlphaFold has been useful

in your research. Share your stories with us at

[[email protected]](mailto:[email protected]).



## License and Disclaimer



This is not an officially supported Google product.



Copyright 2022 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 under the terms of the Creative

Commons Attribution 4.0 International (CC BY 4.0) license. You can find details

at: https://creativecommons.org/licenses/by/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.



*   [Uniref30: v2021_03](http://wwwuser.gwdg.de/~compbiol/uniclust/2021_03/)

    (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: v2022_05](http://ftp.ebi.ac.uk/pub/databases/metagenomics/peptide_database/2022_05/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/).