目录
- afdbRelease code for v2.3.03年前
- alphafold[Remove scipy dependency] Use custom ks_normal_test to test normality of distribution. This approximates the p value of the distribution.3个月前
- dockerRun Pyformat on the entire AlphaFold 2 codebase.4个月前
- docsRelease code for v2.3.03年前
- imgsInitial release of AlphaFold.4年前
- notebooksDeprecate the AlphaFold 2 notebook12个月前
- scriptsUse /usr/bin/env for bash shebang.4个月前
- serverAdd information about PDBe template cutoff date10个月前
- .dockerignoreUpdate .dockerignore4年前
- CONTRIBUTING.mdInitial release of AlphaFold.4年前
- LICENSEInitial release of AlphaFold.4年前
- README.md[Remove scipy dependency] Remove last traces of scipy.3个月前
- conftest.pyUse pytest for testing instead of setup.py test6个月前
- pyproject.toml[Remove scipy dependency] Remove last traces of scipy.3个月前
- requirements.txt[Remove scipy dependency] Remove last traces of scipy.3个月前
- run_alphafold.pyRevert: Bring model config creation in one place by simplifying num_ensemble setting3个月前
- run_alphafold_test.pyRun Pyformat on the entire AlphaFold 2 codebase.4个月前
邀请码
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:
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 alphafold@deepmind.com.
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:
Install Docker.
Clone this repository and
cdinto it.Download genetic databases and model parameters:
Install
aria2c. On most Linux distributions it is available via the package manager as thearia2package (on Debian-based distributions this can be installed by runningsudo apt install aria2). Same forrsync.Please use the script
scripts/download_all_data.shto 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: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.
Check that AlphaFold will be able to use a GPU by running:
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.
Build the Docker image:
If you encounter the following error:
use the workaround described in https://github.com/deepmind/alphafold/issues/463#issuecomment-1124881779.
Install the
run_docker.pydependencies. Note: You may optionally wish to create a Python Virtual Environment to prevent conflicts with your system’s Python environment.Make sure that the output directory exists (the default is
/tmp/alphafold) and that you have sufficient permissions to write into it.Run
run_docker.pypointing to a FASTA file containing the protein sequence(s) for which you wish to predict the structure (--fasta_pathsparameter). AlphaFold will search for the available templates before the date specified by the--max_template_dateparameter; this could be used to avoid certain templates during modeling.--data_diris the directory with downloaded genetic databases and--output_diris the absolute path to the output directory.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
aria2cto be installed on your machine.AlphaFold needs multiple genetic (sequence) databases to run:
We provide a script
scripts/download_all_data.shthat can be used to download and set up all of these databases:Recommended default:
will download the full databases.
With
reduced_dbsparameter:will download a reduced version of the databases to be used with the
reduced_dbsdatabase preset. This shall be used with the corresponding AlphaFold parameter--db_preset=reduced_dbslater during the AlphaFold run (please see AlphaFold parameters section).<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.
sudo chmod 755 --recursive "$DOWNLOAD_DIR"command.The
download_all_data.shscript will also download the model parameter files. Once the script has finished, you should have the following directory structure:bfd/is only downloaded if you download the full databases, andsmall_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.shscript. 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:
git fetch origin mainto get all code updates.<DOWNLOAD_DIR>/uniprot.scripts/download_uniprot.sh <DOWNLOAD_DIR>.<DOWNLOAD_DIR>/uniclust30.scripts/download_uniref30.sh <DOWNLOAD_DIR>.<DOWNLOAD_DIR>/uniref90.scripts/download_uniref90.sh <DOWNLOAD_DIR>.<DOWNLOAD_DIR>/mgnify.scripts/download_mgnify.sh <DOWNLOAD_DIR>.<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.scripts/download_pdb_mmcif.sh <DOWNLOAD_DIR>.scripts/download_pdb_seqres.sh <DOWNLOAD_DIR>.<DOWNLOAD_DIR>/params.scripts/download_alphafold_params.sh <DOWNLOAD_DIR>.Using deprecated model weights
To use the deprecated v2.2.0 AlphaFold-Multimer model weights:
SOURCE_URLinscripts/download_alphafold_params.shtohttps://storage.googleapis.com/alphafold/alphafold_params_2022-03-02.tar, and download the old parameters._v3to_v2in the multimerMODEL_PRESETSinconfig.py.To use the deprecated v2.1.0 AlphaFold-Multimer model weights:
SOURCE_URLinscripts/download_alphafold_params.shtohttps://storage.googleapis.com/alphafold/alphafold_params_2022-01-19.tar, and download the old parameters._v3in the multimerMODEL_PRESETSinconfig.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-imagewith 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.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_devicesflag. See GPU enumeration for more details.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.
You can control MSA speed/quality tradeoff by adding
--db_preset=reduced_dbsor--db_preset=full_dbsto 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
monomermodel preset and thereduced_dbsdata preset would look like this: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.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.AlphaFold can reuse MSAs (multiple sequence alignments) for the same sequence via
--use_precomputed_msas=trueoption; 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
--model_preset=multimer,An example that folds a protein complex
multimer.fasta: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_modelflag, e.g. set it to--num_multimer_predictions_per_model=1to 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.pywith--benchmark=true, this runtime is stored intimings.json. All runtimes are from a single A100 NVIDIA GPU. Prediction speed on A100 for smaller structures can be improved by increasingglobal_config.subbatch_sizeinalphafold/model/config.py.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:Then run the following command:
Folding a homomer
Say we have a homomer with 3 copies of the same sequence
<SEQUENCE>. The input fasta should be:Then run the following command:
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:Then run the following command:
Folding multiple monomers one after another
Say we have a two monomers,
monomer1.fastaandmonomer2.fasta.We can fold both sequentially by using the following command:
Folding multiple multimers one after another
Say we have a two multimers,
multimer1.fastaandmultimer2.fasta.We can fold both sequentially by using the following command:
AlphaFold output
The outputs will be saved in a subdirectory of the directory provided via the
--output_dirflag ofrun_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_dirdirectory will have the following structure:The contents of each output file are as follows:
features.pkl– Apicklefile 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. Hereranked_i.pdbshould contain the prediction with the (i + 1)-th highest confidence (so thatranked_0.pdbhas 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=allthen all ranked structures are relaxed. If--models_to_relax=bestthen onlyranked_0.pdbis 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– Apicklefile 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:distogram/logitscontains a NumPy array of shape [N_res, N_res, N_bins] anddistogram/bin_edgescontains the definition of the bins).plddtcontains a NumPy array of shape [N_res] with the range of possible values from0to100, where100means 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.ptmfield 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.predicted_aligned_errorcontains a NumPy array of shape [N_res, N_res] with the range of possible values from0tomax_predicted_aligned_error, where0means 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_sizefunction 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.predictmethod 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:
In addition, if you use the AlphaFold-Multimer mode, please cite:
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 alphafold@deepmind.com.
We would love to hear your feedback and understand how AlphaFold has been useful in your research. Share your stories with us at alphafold@deepmind.com.
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:
BFD (unmodified), by Steinegger M. and Söding J., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
BFD (modified), by Steinegger M. and Söding J., modified by DeepMind, available under a Creative Commons Attribution-ShareAlike 4.0 International License. See the Methods section of the AlphaFold proteome paper for details.
Uniref30: v2021_03 (unmodified), by Mirdita M. et al., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
MGnify: v2022_05 (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. \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 alphafold@deepmind.com.
We would love to hear your feedback and understand how AlphaFold has been useful in your research. Share your stories with us at alphafold@deepmind.com.
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:
BFD (unmodified), by Steinegger M. and Söding J., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
BFD (modified), by Steinegger M. and Söding J., modified by DeepMind, available under a Creative Commons Attribution-ShareAlike 4.0 International License. See the Methods section of the AlphaFold proteome paper for details.
Uniref30: v2021_03 (unmodified), by Mirdita M. et al., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
MGnify: v2022_05 (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. \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:
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 alphafold@deepmind.com.
We would love to hear your feedback and understand how AlphaFold has been useful in your research. Share your stories with us at alphafold@deepmind.com.
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:
BFD (unmodified), by Steinegger M. and Söding J., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
BFD (modified), by Steinegger M. and Söding J., modified by DeepMind, available under a Creative Commons Attribution-ShareAlike 4.0 International License. See the Methods section of the AlphaFold proteome paper for details.
Uniref30: v2021_03 (unmodified), by Mirdita M. et al., available under a Creative Commons Attribution-ShareAlike 4.0 International License.
MGnify: v2022_05 (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.