AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure.The program is designed as a deep learning system.
AlphaFold software has had two major versions. A team of researchers that used AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. The program was particularly successful at predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with a partially similar sequence. A team that used AlphaFold 2 (2020) repeated the placement in the CASP14 competition in November 2020.The team achieved a level of accuracy much higher than any other group. It scored above 90 for around two-thirds of the proteins in CASP’s global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT.
AlphaFold 2’s results at CASP14 were described as “astounding” and “transformational.” 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. 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. 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
AlphaFold is an AI system developed by Google DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.
Google DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI) have partnered to create AlphaFold DB to make these predictions freely available to the scientific community. The latest database release contains over 200 million entries, providing broad coverage of UniProt (the standard repository of protein sequences and annotations). We provide individual downloads for the human proteome and for the proteomes of 47 other key organisms important in research and global health. We also provide a download for the manually curated subset of UniProt (Swiss-Prot).
Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57.
In CASP14, AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with high accuracy. While the system still has some limitations, the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.
Let us know how the AlphaFold Protein Structure Database has been useful in your research, or if you have questions not answered in the FAQs, at alphafold@deepmind.com.
If your use case isn’t covered by the database, you can generate your own AlphaFold predictions using Google DeepMind’s Colab notebook or open source code. Both resources also support multimer prediction.
Q8W3K0: A potential plant disease resistance protein. Mean pLDDT 82.24.