Database of protein structure predictions by AlphaFold that are freely and openly available to global scientific community. Included are nearly all catalogued proteins known to science. Provides programmatic access to and interactive visualization of predicted atomic coordinates, per residue and pairwise model confidence estimates and predicted aligned errors.

Dataset description:

The Encyclopedia of Domains (TED) is a joint effort by CATH (Orengo group) and the Jones group at University College London to identify and classify protein domains in AlphaFold2 models from AlphaFold Database version 4, covering over 188 million unique sequences and 324 million domain assignments.

In this data release, we will be making available to the community a table of domain boundaries and additional metadata on quality (pLDDT, globularity, number of secondary structures), taxonomy and putative CATH SuperFamily or Fold assignments for all 324 million domains in TED100.

For all chains in the TED-redundant dataset, the attached file contains boundaries predictions, consensus level and information on the TED100 representative.

Additionally, an archive with chain-level consensus domain assignments are available for 21 model organisms and 25 global health proteomes:

For both TED100 and TEDredundant we provide domain boundaries predictions outputted by each of the three methods employed in the project (Chainsaw, Merizo, UniDoc).

We are making available 7,427 novel folds PDB files, identified during the TED classification process with an annotation table sorted by novelty.

Please use the gunzip command to extract files with a '.gz' extension.

CATH annotations have been assigned using the FoldSeek algorithm applied in various modes and the FoldClass algorithm, both of which are used to report significant structural similarity to a known CATH domain.
Note: The TED protocol differs from that of our standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for remote matches.

This dataset contains:

• ted_214m_per_chain_segmentation.tsv
  The file contains all 214M protein chains in TED with consensus domain boundaries and proteome information in the following columns.
  1. AFDB_model_ID: chain identifier from AFDB in the format AF-
• ted_365m_domain_boundaries_consensus_level.tsv.gz
  The file contains all domain assignments in TED100 and TED-redundant (365M) in the format:
  1. TED_ID: TED domain identifier in the format AF-
• ted_100_324m.domain_summary.cath.globularity.taxid.tsv and novel_folds_set.domain_summary.tsv are header-less with the following columns separated by tabs (.tsv).
• ted_324m_seq_clustering.cathlabels.tsv
  The file contains the results of the domain sequences clustering with MMseqs2.
  Columns:
  1. Cluster_representative
  2. Cluster_member
  3. CATH code assignment if available i.e. 3.40.50.300 for a domain with a homologous match or 3.20.20 for a domain matching at the fold level in the CATH classification
  4. CATH assignment type - either Foldseek-T, Foldseek-H or Foldclass
• novel_folds_set.domain_summary.tsv is sorted by novelty.
  1. ted_id - TED domain identifier in the format AF-
• Domain assignments for TED redundant using single-chain and multi-chain consensus in ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv
  The files contain a header with the following fields. Each column is tab-separated (.tsv).
  1. TED_redundant_id - TED chain identifier in the format AF-
• and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv
  The file contains a header with the following fields. Each column is tab-separated (.tsv).
  1. TED_redundant_id - TED chain identifier in the format AF-
• novel_folds_set_models.tar.gz contains PDB files of all novel folds identified in TED100.
• All per-tool domain boundaries predictions are in the same format with the following columns.
  1. TED_chainID - TED chain identifier in the format AF-
• Domain boundaries predictions share the same format, with each segment separated by '_' and segment boundaries (start,stop) separated by '-'
  
  i.e.domain prediction by Merizo for AF-A0A000-F1-model_v4
  AF-A0A000-F1-model_v4 e8872c7a0261b9e88e6ff47eb34e4162 394 2 10-52_289-394,53-288 0.90077
  
  Merizo predicts one continuous domain and a discontinuous domain,
  Domain1 (discontinuous): 10-52_289-394
  segment1: 10-52
  segment2: 289-394
  Domain 2 (continuous):
  segment 1: 53-288
• ted-tools-main.zip - copy of the https://github.com/psipred/ted-tools repository, containing tools and software used to generate TED.
• cath-alphaflow-main.zip - copy of CATH-AlphaFlow, used to generate globularity scores for TED domains.
• ted-web-master.zip - copy of TED-web, containing code to generate the web interface of TED (https://ted.cathdb.info)
• gofocus_data.tar.bz2 - GOFocus model weights

The AlphaFold Protein Structure Database is a collection of protein structure predictions made using the machine learning model AlphaFold. AlphaFold was developed by DeepMind , and this database was created in partnership with EMBL-EBI . For information on how to interpret, download and query the data, as well as on which proteins are included / excluded, and change log, please see our main dataset guide and FAQs . To interactively view individual entries or to download proteomes / Swiss-Prot please visit https://alphafold.ebi.ac.uk/ . The current release aims to cover most of the over 200M sequences in UniProt (a commonly used reference set of annotated proteins). The files provided for each entry include the structure plus two model confidence metrics (pLDDT and PAE). The files can be found in the Google Cloud Storage bucket gs://public-datasets-deepmind-alphafold-v4 with metadata in the BigQuery table bigquery-public-data.deepmind_alphafold.metadata . If you use this data, please cite: Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021) Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021) This public dataset is hosted in Google Cloud Storage and is available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Here are deposited all of the predictions generated for the test cases presented in "AlphaFold Unmasked: integration of experiments and predictions with a smarter template mechanism" (doi: https://doi.org/10.1101/2023.09.20.558579) along with the log files necessary to reproduce the experiments.Each tar.gz file includes one or more AlphaFold experiments, where multiple predictions have been generated either with AlphaFold-Multimer (standard pipeline, v2.2 and/or v2.3 parameters) or with AF_unmasked. An experiment is made of a set of 3D structure predictions (.pdb files) along with the ancillary data generated by AlphaFold (pickle files) and the corresponding inputs (Multiple Sequence Alignments, sequences). Scripts to reproduce the results are included along with the log files generated during the experiments.H1111, H1142, T1109 and T1110 are multimeric prediction targets from CASP15 (https://predictioncenter.org/casp15/) chosen because most or all predictors failed to correctly predict these complexes in the 2021 edition of CASP.Rubisco, NF1 and ClpB are examples of large and/or challenging targets where Cryo-EM data is available to be integrated in the prediction pipeline.The PDB benchmark is made of a set of protein heterodimeric structures deposited in the PDB before January 2022, i.e. before AlphaFold v2.3 was trained and released. These heterodimers have been redundancy reduced by structural similarity (MMalign score threshold: 0.4) to increase their diversity

AlphaFold 2 Beta Strand Database

  Dataset Summary and Creation

The AlphaFold 2 (AF2) Beta Strand Database is a database for high-confidence scored beta strand pairs as predicted by Alphafold 2, a revolutionary protein structure prediction system. All 214 million protein structures from the Alphafold Protein Structure Database (Alphafold DB) were analyzed and well-aligned pairs of amino acid sequences, which exhibited beta-strand conformations, were collected using specific… See the full description on the dataset page: https://huggingface.co/datasets/hz3519/AF2_Beta_Strand_Database.

In this work, we are using AlphaFold structure models to find the closest homologues proteins between Homo sapiens and D. melanogaster, C. elegans, S. cerevisiae and S. pombe as well as between S. cerevisiae and S. pombe. We are using the structure aligner Foldseek to run all against all and search for the best scoring hit in both directions to detect the Reciprocal Best Structure Hits (RBSH). We compare the results to protein pairs detected by their sequence similarity as Reciprocal Best Hits (RBH) and verify the results using the PANTHER family classification files. \( \ \) Note: This dataset is an updated version of the dataset at https://doi.org/10.17863/CAM.85487.

Description: TMvis ("TMvis496.tar.gz") is a dataset containing 496 3D-structures of predicted human transmembrane proteins (TMP) and their predicted membrane embedding. The method TMbed [1], based on the protein language model ProtT5 [2] predicted 4.967 TMP for the human proteome (20,375 proteins, UniProt [3] version April 2022; excluding TITIN_HUMAN due to length). For these proteins, we obtained AlphaFold [4] structures from AlphaFoldDB [5] with an average per-residue confidence score (pLDDT) of more than 90%. This resulted in the 496 proteins of TMvis, as can be found in "TMvis496.fasta". The membrane embedding was predicted using the methods ANVIL [6], PPM3 [7], and per-residue TMbed predictions. As the three methods are based on different approaches, we decided to publish results for all. The figure “TMvis_project_overview.png” provides a graphical overview for each step described above.

TMvis Folder Structure: TMvis is separated into “alpha” containing predicted alpha-helical TMPs, and “beta” containing predicted beta-barrel TMPs. Within these folders, each protein is assigned one folder, identifiable by the respective unique UniProt ID. Each protein folder consists of: - “UniprotID.fasta” with UniProt ID, sequence, TMbed per-residue prediction - “AF-UniprotID-F1-model_v2.pdb” with the AlphaFold structure - “AF-UniprotID-F1-model_v2.cif” with the AlphaFold structure - “AF-UniprotID-F1-model_v2_ANVIL.pdb” with predicted ANVIL membrane embedding - “AF-UniprotID-F1-model_v2_ppm.pdb” predicted PPM3 membrane embedding

TMvis
|
├── alpha
│ │
│ ├── A0A087X1C5
│ │ ├── A0A087X1C5.fasta
│ │ ├── AF-A0A087X1C5-F1-model_v2.pdb
│ │ ├── AF-A0A087X1C5-F1-model_v2.cif
│ │ ├── AF-A0A087X1C5-F1-model_v2_ANVIL.pdb
│ │ └── AF-A0A087X1C5-F1-model_v2_ppm.PDB
│ └── ...
└── beta
└── P45880

TMvis visualization: The 3D-visualization of every protein in the dataset TMvis can be easily accessed using the Jupyter Notebook “TMvis.ipynb”. It contains detailed descriptions the different membrane prediction tools ANVIL, PPM3, and TMbed as well as the respective code. Additionally, it allows to visualize the per-residue confidence scores (pLDDT) of AlphaFold.

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References:

[1] TMbed - TMbed Bernhofer, Michael, and Burkhard Rost. 2022. “TMbed – Transmembrane Proteins Predicted through Language Model Embeddings.” bioRxiv.

[2] ProtT5 - A. Elnaggar et al., "ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3095381.

[3] UniProt - UniProt Consortium (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic acids research, 49(D1), D480–D489.

[4] AlphaFold - AlphaFold Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89.

[5] Alphafold DB - Varadi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, et al. 2022. “AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models.” Nucleic Acids Research 50 (D1): D439–44.

[6] ANVIL - ANVIL Postic, Guillaume, Yassine Ghouzam, Vincent Guiraud, and Jean-Christophe Gelly. 2016. “Membrane Positioning for High- and Low-Resolution Protein Structures through a Binary Classification Approach.” Protein Engineering, Design & Selection: PEDS 29 (3): 87–91.

[7] PPM3 - PPM3 Lomize, Mikhail A., Irina D. Pogozheva, Hyeon Joo, Henry I. Mosberg, and Andrei L. Lomize. 2012. “OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes.” Nucleic Acids Research 40 (Database issue): D370–76.

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License:

This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0).

AlphaFold DB provides open access to over 200 million protein structure predictions to accelerate scientific research.

These repository provides:

1. A file including pdb.gz files for human protein structures from the AlphaFoldDb with occupancy and temperature factor columns set to residue-wise mean of AlphaMissense scores.
2. A PyMOL plugin file (coloram.py) for coloring these structures.
3. Python scripts and additional files (e.g. Jinja2 templates, matplotlib styles) are in pub.zip.

Please see details at https://alphamissense.hegelab.org

Disclaimer: The AlphaMissense Database and other information provided on or linked to this site is for theoretical modelling only, caution should be exercised in use. It is provided "as-is" without any warranty of any kind, whether express or implied. For clarity, no warranty is given that use of the information shall not infringe the rights of any third party (and this disclaimer takes precedence over any contrary provisions in the Google Cloud Platform Terms of Service). The information provided is not intended to be a substitute for professional medical advice, diagnosis, or treatment, and does not constitute medical or other professional advice.

Data contained within the AlphaMissense Database is provided for non-commercial research use only under CC BY-NC-SA 4.0 license.

DeepMind - AlphaMissense: https://doi.org/10.1126/science.adg7492

This dataset supplements the code at https://github.com/aozalevsky/alphafold2_vs_swissmodel for the comparison of the AlphaFold2 database (https://alphafold.ebi.ac.uk) with the SwissModel Repository (https://swissmodel.expasy.org/repository). Results of the analysis were published as part of the AlphaFold community review https://www.nature.com/articles/s41594-022-00849-w

Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting "folding frenzy" has already produced predicted protein structure databases for the entire human and other organisms' proteomes. However, rapidly ascertaining a predicted structure's reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients (D0t(20,w),s0(20,w)) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US‑SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding D0t(20,w), s0(20,w), [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold's drawbacks were mitigated, such as generating whenever possible a protein's mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US‑SOMO‑AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures. Methods Production of this dataset required three major steps: collect the AlphaFold entries and additional metadata; prepare the structures for hydrodynamic, structural and CD calculations; and compute the hydrodynamic, structural and CD propertiesBriefly, each entry in the entire AlphaFold database was first compared with the corresponding entry in the UniProt database to find the (putative) initiator methionine, signal peptide and transit peptide regions, which were subsequently removed from the AlphaFold PDB files. Additional variants were created when propeptides were found. Potential disulfides were identified (subsequently allowing a better evaluation of the partial specific volume and of M) and written as SSBOND records in the cured PDBs, together with HELIX and SHEET information identified using the DSSP implementation in UCSF Chimera (Pettersen et al, 2004. Journal of computational chemistry, 25(13), pp.1605-1612). Batch-mode US-SOMO was then used to calculate the mass M, The translational diffusion coefficient D0t(20,w), the sedimentation coefficient s0(20,w), the derived Stokes' (or hydrodynamic) radius Rs, the intrinsic viscosity [η], the radius of gyration Rg, the maximum extensions along the principal X, Y and Z axes of the molecule, and the generation of an anhydrous small angle X-ray scattering pairwise distribution function p( r ) vs. r distributions (that are normalized by the M of the structure). SESCA was subsequently used to generate 170-270 nm circular dichroism CD spectra from each cured structure.

Phytoplasma AlphaFold-2 Structural Models from the AlphaFold Database (NCBI Taxonomy ID: txid33926) Exhibiting SAP05-like folds.

Interactive database of protein protein interactions modeled by AlphaFold multimer. Classifier-curated database of AlphaFold-modeled protein-protein interactions.

This folder contains the files in cif format generated by AlphaFold 3 to build Figure 4.1 of the thesis of Sofia Megalhães Moreira - https://hdl.handle.net/10523/43234. The data is embargoed in Figshare until 24 June 2026.

The zip file contains the pdb files of top ranked alphafold predicted structures of the proteins found in the particle proteome of Naiavirus.

The prediction was performed using Alphafold v2.2.

The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology. References: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589. Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

This contains AlphaFold predictions for X proteins that are found in the Protein Data Bank (PDB), that were used to evalluate AlphaFold's predictions of mutation effects. This includes one set of structures predicted by AlphaFold2.0, using default settings, and one structure for each of 5 models. This also includes structures predicted by the ColabFold version of AlphaFold (6 recycles, 5 models, no template, amber minimization, 4 repeats). There are also additional predicted structures that are found in the PDB that were not analyzed in the paper. There are AlphaFold predictions for three proteins (BFP / RFP, GFP, and PafA), covering either all (BFP/RFP, PafA) or a subset (GFP) of the sequences in three datasets of phenotype measurements from high-throughput experiments. Results are separated into tar files based on whether DeepMind (AF2.0) or ColabFold implementation was used. Folders under "ColabFold/PDB" are labelled according to a sequence ID, since multiple PDB structures can exist for a single sequence. These sequence IDs can be mapped back to PDB IDs using the information in "seq_id_pdb_id.json". All PDB files have been compressed using Foldcomp (https://github.com/steineggerlab/foldcomp). Foldcomp is required to decompress the ".fcz" files in order to recover the ".pdb" files.

List of Mollicute AlphaFold-2 structural models in the AlphaFold database (NCBI Taxonomy ID: txid544448).

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.

Comprehensive database of Discoba protein sequences, gathered for the purpose of improving protein structure predictions of Discoba species (including Trypanosoma and Leishmania) by AlphaFold and RoseTTAFold. Originally gathered for use with: https://github.com/zephyris/discoba_alphafold