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.

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 365 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 365 million domains (~324 million domains in TED100 and ~40 million domains in TED-redundant).

For all chains in the chain-level TED-redundant files, the file contains boundary predictions, consensus level and information on the TED100 representative.

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

We are making available 7,427 PDB files for potentially novel folds identified during the TED classification process, with an annotation table sorted by novelty, as well as 6,433 highly symmetrical folds representatives.

Please use the gunzip command to extract files with a '.gz' extension and "tar -xzvf file.tar.gz" to open .tar.gz files .

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 the standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for classification into superfamilies.

Changelog Version 5:

• Add: ted_365m.domain_summary.cath.globularity.taxid.tsv.tar.gz - This table, in the same format as the previous ted_100_324m.domain_summary.cath.globularity.taxid.tsv.tar.gz, contains per-domain annotations for the whole of TED, including metadata on domain quality metrics such as secondary structure elements counts, globularity scores, average pLDDT and taxonomical assignments.
• Add: high_symmetry_folds_set.domain_summary.tsv.gz - subset of ted_365m.domain_summary.cath.globularity.taxid.tsv containing information on 6,433 high symmetry folds in TED. The entries are sorted in descending order by Z-score obtained from SymD.
• Add: high_symmetry_folds_set_models.tar.gz - TED domain models in PDB format for 6,433 high symmetry folds in TED.
• Add: ISP_data.tar.gz - Raw data for Interacting SuperFamily Pairs calculations used in the manuscript. A more detailed description of the ISP data is available below as well as within the tar.gz file.
• Add: ted_redundant_40m_domain_id.list.gz - list of TED_domain_ID in TED redundant
• Add: ted_100_324m_domain_id.list.gz - list of TED_domain_ID in TED100
• Fix/Replace: A domain-level summary of TED, now consolidated into ted_365m.domain_summary.cath.globularity.taxid.tsv, is consistent with the protocol used in the manuscript. As Foldclass and Foldseek T-level hits provide all 4 CATH digits, we removed the H portion of the CATH code from each prediction at the T-level.
  Previously, the following columns
  14. cath_label - CATH superfamily code if predicted, either a C.A.T.H. homologous superfamily or C.A.T. fold assignment. i.e. 3.40.50.300
  15. cath_assignment_level - H for homologous superfamily assignment, T for fold level assignment.
  16. cath_assignment_method - Method used to assign a CATH label, either Foldseek or Foldclass
  sometimes showed an additional label with a T-level prediction by Foldclass in the case of T-level assignments obtained by Foldseek, e.g.
  3.40.30,3.40.30 T foldseek,foldclass
  This has now been corrected to reflect the TED protocol, with Foldclass T-level assignments applied only to domains where a T-level assignment could not be applied using Foldseek, e.g.
  domain-x 3.40.30 T foldseek
  domain-y 3.20.20 T foldclass
  
  Thus, in the current version of the data, CATH assignments label can only be
  H-level assignment by Foldseek (i.e. 3.40.50.300 H foldseek)
  T-level assignment by Foldseek (i.e. 3.40.30 T foldseek)
  T-level assignment by Foldclass (i.e. 3.40.30 T foldclass)
  or no assignment (- - - )

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_id.list.gz - list of ~324 million domain identifiers in TED100, one per line in the format AF-
• ted_redundant_40m_domain_id.list.gz - list of ~40 million domain identifiers in TED redundant, one per line in the format AF-
• ted_365m.domain_summary.cath.globularity.taxid.tsv, novel_folds_set.domain_summary.tsv and high_symmetry_folds_set.domain_summary.tsv are header-less with the following columns separated by tabs (.tsv). novel_folds_set.domain_summary.tsv is sorted by novelty
  Note: The TED protocol differs from that of the standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for classification into superfamilies.
  
  1. ted_id - TED domain identifier in the format AF-
• ted_324m_seq_clustering.cathlabels.tsv.gz
  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
  
  
• Domain assignments for TED redundant using single-chain and multi-chain consensus in ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv.gz and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv.gz
• The file ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv.gz contains a header with the

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.

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.

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:

Organism TaxonID

arabidopsis_thaliana 3702
caenorhabditis_elegans 6239
candida_albicans 237561
danio_rerio 7955
dictyostelium_discoideum 44689
drosophila_melanogaster 7227
escherichia_coli 83333
glycine_max 3847
homo_sapiens 9606
methanocaldococcus_jannaschii 243232
mus_musculus 10090
oryza_sativa 39947
rattus_norvegicus 10116
saccharomyces_cerevisiae 559292
schizosaccharomyces_pombe 284812
zea_mays 4577
ajellomyces_capsulatus 447093
brugia_malayi 6279
campylobacter_jejuni 192222
cladophialophora_carrionii 86049
dracunculus_medinensis 318479
fonsecaea_pedrosoi 1442368
haemophilus_influenzae 71421
helicobacter_pylori 85962
klebsiella_pneumoniae 1125630
leishmania_infantum 5671
madurella_mycetomatis 100816
mycobacterium_leprae 272631
mycobacterium_tuberculosis 83332
mycobacterium_ulcerans 1299332
neisseria_gonorrhoeae 242231
nocardia_brasiliensis 1133849
onchocerca_volvulus 6282
paracoccidioides_lutzii 502779
plasmodium_falciparum 36329
pseudomonas_aeruginosa 208964
salmonella_typhimurium 99287
schistosoma_mansoni 6183
shigella_dysenteriae 300267
sporothrix_schenckii 1391915
staphylococcus_aureus 93061
streptococcus_pneumoniae 171101
strongyloides_stercoralis 6248
trypanosoma_brucei 185431
trypanosoma_cruzi 353153
wuchereria_bancrofti 6293

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.

This dataset contains:

• 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).
• novel_folds_set.domain_summary.tsv is sorted by novelty.
  1. ted_id
  2. md5_domain
  3. consensus_level
  4. chopping
  5. nres_domain
  6. num_segments
  7. plddt
  8. num_helix_strand_turn
  9. num_helix
  10. num_strand
  11. num_helix_strand
  12. num_turn
  13. proteome_id
  14. cath_label
  15. cath_assignment_level
  16. cath_assignment_method
  17. packing_density
  18. norm_rg
  19. tax_common_name
  20. tax_scientific_name
  21. tax_lineage
• Domain assignments for TED redundant in ted_redundant_39m.consensus_domain_summary.taxid.tsv
  The file contains a header with the following fields. Each column is tab separated (.tsv).
  1. TED_redundant_id
  2. md5
  3. nres
  4. n_high
  5. n_med
  6. high_consensus
  7. med_consensus
  8. ndom_consensus
  9. n_targets
  10. proteome_id
  11. TED_redundant_species
  12. TED100_chain_rep
  13. TED100_chain_rep_species
• 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
  2. TED_chain_md5
  3. TED_chain_length
  4. ndoms
  5. Domain boundaries
  6. Prediction probability
• 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
• model_organisms_and_global_health_proteomes.tar.gz - domain assignments for 21 model organisms and 25 global health proteomes

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).

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

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 earlier version of a more up-to-date dataset at https://doi.org/10.17863/CAM.87873

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.

Models of SurA homologues which are present in the InterPro family IPRO15391 but are not in the EBI AlphaFold database (2024)

The dataset comprises a set of five structures of metamorphic proteins used for the study.

Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation". Includes:

All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models. Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes. Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).

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

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

Domain definitions of AlphaFold classifications of the human proteome (v1) from the AlphaFold Database. Also included are classifications of Danio rerio, Mus musculus, Pan paniscus, Drosophila melanogaster, Caenorhabditis elegans used for comparative analysis to human. See README file for descriptions of file formats.

Protein structure prediction files for the CHESS human protein structure database version 1.2. AlphaFold2/ColabFold predictions of the GTEx assembled human proteome.

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.

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.

The AlphaFold set contains computationally generated structures for metalloproteins that were used to test MAHOMES II's enzyme/non-enzyme predictive performance (Feehan et al. 2023).

README.md - Detailed description of AlphaFold set generation.

AF-...-model_v2.pdb - Files with the 3D atomic coordinates of a metalloprotein.

MAHOMES-II_AlphaFold_set_site_data.csv - Contains the data used during the generation of the AlphaFold set for the final sites. Columns are - Entry: The UniProt accession number of the protein with the bound metal site. - struc_id: The structures AlphaFold DB name (Febuary 2022) and the name of the file in this directory with added metal site. - metal_resName: The two letter PDB residue abbreviation for the site's metal - metal_seqID: The residue index number for the added metal ion. - Enzyme: The enzyme (True) or non-enzyme (False) label. - Entry name: UniProt entry name. - Protein names: The UniProt provided metalloprotein name(s). - Number of homologs with solved structures (PDB): Number of protein sequences in the PDB (May 21, 2020) with an E-value < 1. - Number of homologs in MAHOMES II dataset and T-metal-sites10: Number of protein sequences used to train and evaluate MAHOMES II with an E-value < 1 (0 for all entries). - Metal binding note: UniProt metal binding note that includes information covering the metal’s identity and catalytic flag. - Metal coordinating residue seqIDs: The sequence indices for the metal coordinating residues included in the UniProt’s metal binding section.