alphafold README

The project's README from github.com/google-deepmind/alphafold. As one markdown file: README.md.

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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 for how to upgrade and update code.
  2. The technical note containing the models and inference procedure for an updated AlphaFold v2.3.0.
  3. A CASP15 baseline 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 the AlphaFold paper and, if applicable, the AlphaFold-Multimer paper.

Please also refer to the Supplementary Information for a detailed description of the method.

**You can use a slightly simplified version of AlphaFold with community-supported versions (see below).

If you have any questions, please contact the AlphaFold team at [email protected].

CASP14 predictions

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.

  2. Clone this repository and cd into it.

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

  3. 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). Same for rsync.

    • 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:

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

    • Note: The download directory <DOWNLOAD_DIR> 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.

  4. Check that AlphaFold will be able to use a GPU by running:

    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 or take a look at the following NVIDIA Docker issue.

    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.

  5. Build the Docker image:

    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.

  6. Install the run_docker.py dependencies. Note: You may optionally wish to create a Python Virtual Environment to prevent conflicts with your system's Python environment.

    pip3 install -r docker/requirements.txt

  7. Make sure that the output directory exists (the default is /tmp/alphafold) and that you have sufficient permissions to write into it.

  8. 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.

    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

  9. 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:

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

:ledger: Note: The download directory <DOWNLOAD_DIR> 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. Some of the databases are mirrored for speed, see 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 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:

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.
  2. Update the UniProt, UniRef, MGnify and PDB seqres databases.
    • Remove <DOWNLOAD_DIR>/uniprot.
    • Run scripts/download_uniprot.sh <DOWNLOAD_DIR>.
    • Remove <DOWNLOAD_DIR>/uniclust30.
    • Run scripts/download_uniref30.sh <DOWNLOAD_DIR>.
    • Remove <DOWNLOAD_DIR>/uniref90.
    • Run scripts/download_uniref90.sh <DOWNLOAD_DIR>.
    • Remove <DOWNLOAD_DIR>/mgnify.
    • Run scripts/download_mgnify.sh <DOWNLOAD_DIR>.
    • Remove <DOWNLOAD_DIR>/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 <DOWNLOAD_DIR>.
    • Run scripts/download_pdb_seqres.sh <DOWNLOAD_DIR>.
  3. Update the model parameters.
    • Remove the old model parameters in <DOWNLOAD_DIR>/params.
    • Download new model parameters using scripts/download_alphafold_params.sh <DOWNLOAD_DIR>.
  4. Follow 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 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 for more details.

  2. 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 model. To use this model, provide a multi-sequence FASTA file. In addition, the UniProt database should have been downloaded.

  3. 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:

    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

  4. 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.

  5. 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.

  6. AlphaFold can reuse 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

An example that folds a protein complex multimer.fasta:

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 <SEQUENCE>. The input fasta should be:

>sequence_name
<SEQUENCE>

Then run the following command:

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 <SEQUENCE>. The input fasta should be:

>sequence_1
<SEQUENCE>
>sequence_2
<SEQUENCE>
>sequence_3
<SEQUENCE>

Then run the following command:

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 <SEQUENCE A> and 3 copies of <SEQUENCE B>. The input fasta should be:

>sequence_1
<SEQUENCE A>
>sequence_2
<SEQUENCE A>
>sequence_3
<SEQUENCE B>
>sequence_4
<SEQUENCE B>
>sequence_5
<SEQUENCE B>

Then run the following command:

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:

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:

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:

<target_name>/
    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:

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:

For templates:

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:

@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:

@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):

Acknowledgements

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

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].

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].

License and Disclaimer

This is not an officially supported Google product.

Copyright 2022 DeepMind Technologies Limited.

AlphaFold 2 and its output are for theoretical modeling only. They are not intended, validated, or approved for clinical use. You should not use the AlphaFold 2 or its output for clinical purposes or rely on them for medical or other professional advice. Any content regarding those topics is provided for informational purposes only and is not a substitute for advice from a qualified professional.

Output of AlphaFold 2 are predictions with varying levels of confidence and should be interpreted carefully. Use discretion before relying on, publishing, downloading or otherwise using AlphaFold 2 and its output.

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 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:

alphafold (google-deepmind/alphafold)

Searching Database…

14.9K ·GitHub
Alphafold
这是什么？AlphaFold 是 Google DeepMind 开源的 AlphaFold v2 推理流程实现，可根据氨基酸序列预测蛋白质的三维结构；另外还包含一个仍在开发中的 AlphaFold-Multimer 模型，用于预测蛋白质复合物。
这是一个我可以导入的库，还是需要单独运行的流程？它是一个基于 Docker 的独立预测流程，你通过命令行对 FASTA 文件运行即可，而不是可以导入到自己代码中的 Python 库。
怎样才能跑起第一次预测？安装 Docker 和 NVIDIA Container Toolkit，克隆仓库，用 scripts/download_all_data.sh 下载基因数据库和模型参数，构建 Docker 镜像，然后运行 docker/run_docker.py，并将 --fasta_paths 指向你的蛋白质序列文件。
开始之前需要什么硬件和工具？你需要一台 Linux 机器、一块较新的 NVIDIA GPU、安装了 NVIDIA Container Toolkit 的 Docker，以及用于下载数据库的 aria2c/rsync 工具；完整的基因数据库集最多需要 3 TB 磁盘空间（最好是 SSD）。
除了 Linux，它还能在其他系统上运行吗？不能——AlphaFold 明确不支持 Linux 以外的操作系统，而 GPU 配置问题是安装时常见的坑。
整个项目都能免费使用吗，包括训练好的模型？代码采用 Apache-2.0 许可证，但训练好的 AlphaFold 模型参数和 CASP15 预测数据则单独以 CC BY 4.0 许可证发布。
没有 NVIDIA GPU 能运行吗？实际使用时需要较新的 NVIDIA GPU；松弛（relaxation）步骤可以退回到 CPU 运行（更慢但更稳定），但预测速度和能否运行很大程度上取决于 GPU 显存。
AlphaFold-Multimer 是做什么用的？它是用于预测由多条序列组成的蛋白质复合物结构的模型变体；你需要提供多序列 FASTA 文件并设置 --model_preset=multimer，它还额外需要 UniProt 数据库。
跳过完整的基因数据库可以节省磁盘空间吗？可以——下载脚本提供 reduced_dbs 选项，它使用较小的 BFD 数据库，只需约 600 GB 磁盘空间和 8 个 CPU 核心，配合 --db_preset=reduced_dbs 使用，代价是牺牲部分精度以换取速度并降低硬件要求。
基因数据库下载有多大？完整数据库下载约为 556 GB（压缩后），解压后在磁盘上约占 2.62 TB。
AlphaFold 模型最初是在什么数据上验证的？五个主要模型曾在 CASP14 结构预测任务上进行了大量验证；仓库还附带了 CASP15 基线预测集，并记录了人工干预的说明。
What are similar projects?tapnet, math, wuji-ego-mint, OpenWAM, sam3, olmocr, google-research, pointdit
Which topics is it in?bioinformatics, science, deep-neural-networks, dataset, google, academic-research, artificial-intelligence, nvidia, cuda, benchmark, deep-learning, research
Creator
google-deepmind
Size
16.4 MB
Stars
14.9K
Updated
CommitApr 22Releasev2.3.2Apr 05
Age
5y
Commits · 90 Days