Alphafold Database Download

DeepMind and EMBL-EBI have now partnered to make hundreds of thousands (and eventually many millions) of AlphaFold structure predictions freely available through AlphaFold DB. The initial release of the database includes structure predictions for 98.5% of the proteins in the human proteome. By contrast, only 11% of human proteins have had their structure determined experimentally.

AlphaFold DB could also open up the development of research areas that were previously impossible, impractical or limited by the relatively restricted amounts of 3D structural information available. PDB contains over 180,000 entries which cover ~55,000 unique proteins. This is a tiny fraction of the number of known protein sequences, which is estimated at 220 million sequences in UniProt, or even more in metagenomics databases such as MGnify (>600 million). Imagine what discoveries the millions of protein structure predictions set to be made available in AlphaFold DB can help to unlock.

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AlphaFold database (AlphaFold DB) provides open access to over 200 million protein structure predictions to accelerate scientific research. This webinar aims to provide a comprehensive introduction to AlphaFold DB. Participants will gain a clear understanding of the fundamental concepts, principles, and functionalities of the database. They will learn how to access and evaluate predicted protein structures, explore the underlying technologies and algorithms used in AlphaFold, and identify the potential applications of AlphaFold DB in their research

AlphaFold 2's results at CASP14 were described as "astounding"[6] and "transformational."[7] Some researchers noted that the accuracy is not high enough for a third of its predictions, and that it does not reveal the mechanism or rules of protein folding for the protein folding problem to be considered solved.[8][9] Nevertheless, there has been widespread respect for the technical achievement, and analysis suggests that AlphaFold 2 is accurate enough to predict even single-mutation effects.[10] On 15 July 2021 the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes.[11][12][13] A more advanced version of AlphaFold is currently under development. It allows modeling of protein complexes with nucleic acids, small ligands, ions, and modified residues.[14]

With so little yet known about the internal patterns that AlphaFold 2 learns to make its predictions, it is not yet clear to what extent the program may be impaired in its ability to identify novel folds, if such folds are not well represented in the existing protein structures known in structure databases.[8][64] It is also not well known the extent to which protein structures in such databases, overwhelmingly of proteins that it has been possible to crystallise to X-ray, are representative of typical proteins that have not yet been crystallised. And it is also unclear how representative the frozen protein structures in crystals are of the dynamic structures found in the cells in vivo. AlphaFold 2's difficulties with structures obtained by protein NMR methods may not be a good sign.

Also, because AlphaFold processes protein-only sequences by design, other associated biomolecules are not considered. On the impact of absent metals, co-factors and, most visibly, co- and post-translational modifications such as protein glycosylation from AlphaFold models, Elisa Fadda (Maynooth University, Ireland) and Jon Agirre (University of York, UK) highlighted the need for scientists to check databases such as UniProt-KB for likely missing components, as these can play an important role not just in folding but in protein function.[68] However, the authors highlighted that many AlphaFold models were accurate enough to allow for the introduction of post-predictional modifications.[68]

The AlphaFold Protein Structure Database was launched on July 22, 2021, as a joint effort between AlphaFold and EMBL-EBI. At launch the database contains AlphaFold-predicted models of protein structures of nearly the full UniProt proteome of humans and 20 model organisms, amounting to over 365,000 proteins. The database does not include proteins with fewer than 16 or more than 2700 amino acid residues,[72] but for humans they are available in the whole batch file.[73] AlphaFold planned to add more sequences to the collection, the initial goal (as of beginning of 2022) being to cover most of the UniRef90 set of more than 100 million proteins. As of May 15, 2022, 992,316 predictions were available.[74]

AlphaFold has been used to predict structures of proteins of SARS-CoV-2, the causative agent of COVID-19. The structures of these proteins were pending experimental detection in early 2020.[83][7] Results were examined by the scientists at the Francis Crick Institute in the United Kingdom before release into the larger research community. The team also confirmed accurate prediction against the experimentally determined SARS-CoV-2 spike protein that was shared in the Protein Data Bank, an international open-access database, before releasing the computationally determined structures of the under-studied protein molecules.[84] The team acknowledged that although these protein structures might not be the subject of ongoing therapeutical research efforts, they will add to the community's understanding of the SARS-CoV-2 virus.[84] Specifically, AlphaFold 2's prediction of the structure of the ORF3a protein was very similar to the structure determined by researchers at University of California, Berkeley using cryo-electron microscopy. This specific protein is believed to assist the virus in breaking out of the host cell once it replicates. This protein is also believed to play a role in triggering the inflammatory response to the infection.[85]

By developing an efficient way to compare all predicted protein structures in the AlphaFold database, researchers have revealed similarities between proteins across different species. This work aids our understanding of protein evolution and has uncovered new insights into the origin of human immunity proteins.

The AlphaFold database is a transformative resource in the field of protein research, serving as a comprehensive repository of AI-predicted 3D structures for all known proteins. The database fills a critical gap in understanding protein function and evolution by offering high-quality structural predictions. Although AI predictions are not a substitute for experimentally determined structures, they do provide invaluable insights for the scientific community.

"We estimated that clustering all structures with established methods would have taken a decade when compared to the five days it took using our new method, Foldseek Cluster. Our algorithm can sift through millions of predicted protein structures in the AlphaFold database and cluster them based on their 3D shapes. This acceleration in computational power doesn't just make things faster; it makes things possible."

As the AlphaFold database and other life science databases continue to grow there is a significant need to help users sift through the vast amount of data while reducing the computational costs of analyzing and managing these data. Approaches such as the Foldseek Cluster algorithm, that is scalable to billions of structures, will be invaluable in helping researchers navigate this wealth of information.

"Foldseek Cluster is more than just a technological advancement; it's an enhancement that elevates the entire AlphaFold database experience for researchers worldwide," said Sameer Velankar, Team Leader at EMBL-EBI.

In TmAlphaFold database the relative position of the transmembrane protein structure to the membrane layer is calculated by TMDET algorithm, and according to this calculation an evaluation of the predicted structure is also provided.

TmAlphaFold database contains all alpha-helical transmembrane protein orientation in the membrane deposited so far in AlphaFold database at EBI, and we would like to update its content following the growth of AlphaFold database.

In less than a month, researchers have used AlphaFold, an artificial intelligence (AI)-powered protein structure database, to design and synthesize a potential drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer.

The reference databases and models were downloaded in the directory /shared/work/NBD_Utilities/AlphaFold/databases and the singularity image file (alphafold_2.1.0.sif) of AlphaFold is available at /shared/work/NBD_Utilities/AlphaFold

AlphaFold-predicted structures vary in confidence levels(see coloring) and should be interpreted with caution.The alphafold command is also implemented as the toolsAlphaFoldand AlphaFold Error Plot.Several ChimeraXpresentations andvideos show modeling with AlphaFold and related analyses.See also:esmfold,blastprotein,modeller,swapaa

For a specified structure chain, a model is obtained for its exact UniProt entry if available, otherwise the single top hit identified byK-mer search of theAlphaFold Database(details...).For each model with a corresponding structure chainfrom the alphafold match command or thealignTo option of alphafold fetch:

When results are returned, the hits are listed in aBlast Protein window.Double-clicking a hit usesalphafold fetch to retrieve the model,or multiple chosen hits can be retrieved at once by using the results panelcontext menuor Load Structures button(details...).

The alphafold predict command runs a calculation onGoogle Colab using ColabFold,an open-source, optimized version ofAlphaFold 2. Users should cite:ColabFold: making protein folding accessible to all.Mirdita M, Schtze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M.Nat Methods. 2022 Jun;19(6):679-682.For monomer prediction: 2351a5e196