A little over a year ago I highlighted the AlphaFold Protein Structure Database in which AlphaFold DB provided open access to protein structure predictions for the human proteome and 20 other key organisms to accelerate scientific research. Well things have moved on.
DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have made AI-powered predictions of the three-dimensional structures of nearly all catalogued proteins known to science freely and openly available to the scientific community, via the AlphaFold Protein Structure Database.
The database is being expanded by approximately 200 times, from nearly 1 million protein structures to over 200 million, covering almost every organism on Earth that has had its genome sequenced. The expansion of the database includes predicted structures for a wide range of species, including plants, bacteria, animals, and other organisms.
The full dataset of all predictions is available at no cost and under a CC-BY-4.0 licence from Google Cloud Public Datasets. We've grouped this by single-species for ease of downloading subsets or all of the data. We suggest that you only download the full dataset if you need to process all the data with local computing resources (the size of the dataset is 23 TiB, ~1M tar files).
Downloads can be found here https://alphafold.ebi.ac.uk/download#full-dataset-section.
The details of the latest Critical Assessment of Structure Prediction (CASP) experiment to determine and advance the state of the art in modeling biomolecular structures have been published https://predictioncenter.org/casp15/index.cgi.
The core of CASP remains the same: blind testing of methods with independent assessment against experiment to establish the state-of-art in modeling proteins and protein complexes. CASP15 will include following categories.
- Single Protein and Domain Modeling As in previous CASPs, the accuracy of single proteins and where appropriate single protein domains will be assessed, using the established metrics. Two changes will be the elimination of the distinction between template-based and template-free modeling, and an emphasis on the fine-grained accuracy of models, such as local main chain motifs and side chains. Because of the high accuracy of the new modeling methods, we expect assessment against high resolution experimental structures will be most informative.
- Assembly As in recent CASPs, the ability of current methods to correctly model domain-domain, subunit-subunit, and protein-protein interactions will be assessed. We will again work in close collaboration with our CAPRI partners. Because of the promising deep learning results reported so far, substantial progress is expected.
- Accuracy Estimation Members of the community will be invited to submit accuracy estimates for multimeric complexes and inter-subunit interfaces. There will no longer be a category for estimating the accuracy of single protein models, since it has become clear these cannot compete with modeling method specific estimates. Instead, there will be increased emphasis on assessment of self-reported accuracy estimates at the atomic level. Note the units will now be pLDDT, not Angstroms.
- RNA structures and complexes There will be a pilot experiment to assess the accuracy of modeling for RNA models and protein-RNA complexes. The assessment will be done in collaboration with the RNA-Puzzles and Marta Szachniuk's group in Poznan.
- Protein-ligand complexes Subject to the availability of adequate resources, there will also be a pilot experiment in this area. Deep-learning is already having an impact here, and there is high interest because of the relevance to drug design.
- Data Assisted As in recent CASPs, there will be assessment of the extent to which the accuracy of models can be increased by the provision of sparse data, particularly that provided by SAXS and mass spectroscopy/chemical crosslinking. Only targets where these low-resolution data are likely to be useful will be considered, that is, large single proteins and complexes. As previously, we will work with collaborators to obtain the necessary experimental data. Targets will initially be released without the experimental data, followed by a second round of prediction including those data.
- Protein conformational ensembles Following the success of deep-learning methods for single structures, it is increasingly important to assess methods for predicting structure ensembles. This is a huge area, ranging from the many conformations of disordered regions to the small number of conformations that may be involved in allosteric transitions and enzyme excited states to local protein dynamics. While it is clear that deep learning and other methods have the potential to generate ensembles in some circumstances, the difficulty is in finding cases where there are sufficiently accurate and extensive experimental data to allow rigorous assessment. One promising avenue is modeling sets of conformations in regions of cryo-EM structures where there is evidence of local conformational heterogeneity. If suitable cases arise, we will present these as a special type of sub-target. First requesting conformational ensembles that will be evaluated against the election density map and then in a possible second stage providing the map for data assisted ensemble prediction. A second possibility is for cases where detailed NMR data have already established the structure of two or more conformations. We have a good lead for a few targets of this type. In addition to this, we are considering a non-blind experiment (a departure from normal CASP practice), where we will first ask those interested to reproduce the known conformations. We will also ask participants to identify any additional conformations that appear to be present. It may then be possible to test these against existing or new experimental data.
Details of the targets will be made available over the next week https://predictioncenter.org/casp15/targetlist.cgi.
The AlphaFold Protein Structure Database Developed by DeepMind and EMBL-EBI is now available online.
AlphaFold DB provides open access to protein structure predictions for the human proteome and 20 other key organisms to accelerate scientific research.
AlphaFold DB currently provides predicted structures for the organisms listed below and includes human, laboratory species, and key pathogens. All the predictions for all the species can be downloaded from the EBI FTP site ftp://ftp.ebi.ac.uk/pub/databases/alphafold.
|Species||Common Name||Reference Proteome||Predicted Structures||Download|
|Arabidopsis thaliana||Arabidopsis||UP000006548||27,434||Download (3642 MB)|
|Caenorhabditis elegans||Nematode worm||UP000001940||19,694||Download (2601 MB)|
|Candida albicans||C. albicans||UP000000559||5,974||Download (965 MB)|
|Danio rerio||Zebrafish||UP000000437||24,664||Download (4141 MB)|
|Dictyostelium discoideum||Dictyostelium||UP000002195||12,622||Download (2150 MB)|
|Drosophila melanogaster||Fruit fly||UP000000803||13,458||Download (2174 MB)|
|Escherichia coli||E. coli||UP000000625||4,363||Download (448 MB)|
|Glycine max||Soybean||UP000008827||55,799||Download (7142 MB)|
|Homo sapiens||Human||UP000005640||23,391||Download (4784 MB)|
|Leishmania infantum||L. infantum||UP000008153||7,924||Download (1481 MB)|
|Methanocaldococcus jannaschii||M. jannaschii||UP000000805||1,773||Download (171 MB)|
|Mus musculus||Mouse||UP000000589||21,615||Download (3547 MB)|
|Mycobacterium tuberculosis||M. tuberculosis||UP000001584||3,988||Download (421 MB)|
|Oryza sativa||Asian rice||UP000059680||43,649||Download (4416 MB)|
|Plasmodium falciparum||P. falciparum||UP000001450||5,187||Download (1132 MB)|
|Rattus norvegicus||Rat||UP000002494||21,272||Download (3404 MB)|
|Saccharomyces cerevisiae||Budding yeast||UP000002311||6,040||Download (960 MB)|
|Schizosaccharomyces pombe||Fission yeast||UP000002485||5,128||Download (776 MB)|
|Staphylococcus aureus||S. aureus||UP000008816||2,888||Download (268 MB)|
|Trypanosoma cruzi||T. cruzi||UP000002296||19,036||Download (2905 MB)|
|Zea mays||Maize||UP000007305||39,299||Download (5014 MB)|
The search bar at the top of the query page accepts queries based on protein name, gene name, UniProt identifier, or organism name. At present you can't search using a sequence and look for similar proteins. You would first need to do a BLAST search and use the results from that as queries.
Here I searched for Plasmodium falciparum carbonic anhydrase (Q8IHW5) a potential Malaria target. As you can see there is no crystal structure in the PDB. Whilst the active site is predicted with high confidence there are clearly regions for which there is very low confidence.
You can then download the structure in PDB or mmCIF format.
I made a homology model (in purple below) of this protein a while back and it has little sequence similarity with any proteins in the PDB. Despite not including a Zinc the Alphafold Predicted Structure includes histidines in positions to potentially coordinate to the Zinc. If it is possible to include the Zinc in the structure prediction I'd be interested in finding out.
Overall I'd say this is a very useful starting point.