AlphaFold

Extracted Attributes

• Type

  Artificial Intelligence program

• Impact

  Used by over 2 million researchers from 190 countries

• Developer

  DeepMind

• Technique

  Deep learning

• Primary Function

  Protein structure prediction

• Parent Organization

  Alphabet (Google)

• Training Data (AlphaFold 2)

  Big Fantastic Database (BFD) with 65,983,866 protein families and 2,204,359,010 protein sequences

• Database Size (AlphaFold DB)

  Over 200 million entries

• Key Achievement (AlphaFold 1)

  First place in CASP 2018

• Key Achievement (AlphaFold 2)

  First place in CASP14 2020, achieved GDT score above 90 for approximately two-thirds of proteins

• Key Achievement (AlphaFold 3)

  Predicts structures of complexes involving proteins with DNA, RNA, ligands, and ions; minimum 50% improvement in accuracy for interactions with other molecules

Timeline

• AlphaFold's inception date. (Source: Wikidata)

  2018-01-01

• AlphaFold 1 placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) competition. (Source: Wikipedia)

  2018-12-01

• AlphaFold 2 placed first in the CASP14 competition, achieving a much higher level of accuracy than other entries. (Source: Wikipedia)

  2020-11-01

• The AlphaFold 2 paper was published in Nature, accompanied by open-source software and a searchable database of species proteomes. (Source: Wikipedia)

  2021-07-15

• AlphaFold 2 continued to have the highest success rate in the CASP 15 competition. (Source: Web Search Results)

  2022-01-01

• Demis Hassabis and John Jumper won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research for their leadership of the AlphaFold project. (Source: Wikipedia)

  2023-01-01

• AlphaFold 3 was announced, capable of predicting structures of complexes involving proteins with DNA, RNA, ligands, and ions. (Source: Wikipedia)

  2024-05-08

• Demis Hassabis and John Jumper were awarded half of the 2024 Nobel Prize in Chemistry for their work on protein structure prediction. (Source: Summary)

  2024-10-01

AlphaFold

AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. It was particularly successful at predicting the most accurate structures for targets rated as most difficult by the competition organizers, where no existing template structures were available from proteins with partially similar sequences. AlphaFold 2 (2020) repeated this placement in the CASP14 competition in November 2020. It achieved a level of accuracy much higher than any other entry. It scored above 90 on CASP's global distance test (GDT) for approximately two-thirds of the proteins, a test measuring the similarity between a computationally predicted structure and the experimentally determined structure, where 100 represents a complete match. The inclusion of metagenomic data has improved the quality of the prediction of MSAs. One of the biggest sources of the training data was the custom-built Big Fantastic Database (BFD) of 65,983,866 protein families, represented as MSAs and hidden Markov models (HMMs), covering 2,204,359,010 protein sequences from reference databases, metagenomes, and metatranscriptomes. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of protein folding for the protein folding problem, which remains unsolved. Despite this, the technical achievement was widely recognized. 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. As of February 2025, the paper had been cited nearly 35,000 times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions. The new prediction method shows a minimum 50% improvement in accuracy for protein interactions with other molecules compared to existing methods. Moreover, for certain key categories of interactions, the prediction accuracy has effectively doubled. Demis Hassabis and John Jumper of Google DeepMind shared one half of the 2024 Nobel Prize in Chemistry, awarded "for protein structure prediction," while the other half went to David Baker "for computational protein design." Hassabis and Jumper had previously won the Breakthrough Prize in Life Sciences and the Albert Lasker Award for Basic Medical Research in 2023 for their leadership of the AlphaFold project.

Web Search Results

• AlphaFold Protein Structure Database

  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. [...] Image 1: image1 Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57. View protein 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. [...] 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

• Highly accurate protein structure prediction with AlphaFold - Nature

  The methodology that we have taken in designing AlphaFold is a combination of the bioinformatics and physical approaches: we use a physical and geometric inductive bias to build components that learn from PDB data with minimal imposition of handcrafted features (for example, AlphaFold builds hydrogen bonds effectively without a hydrogen bond score function). This results in a network that learns far more efficiently from the limited data in the PDB but is able to cope with the complexity and [...] In particular, AlphaFold is able to handle missing the physical context and produce accurate models in challenging cases such as intertwined homomers or proteins that only fold in the presence of an unknown haem group. The ability to handle underspecified structural conditions is essential to learning from PDB structures as the PDB represents the full range of conditions in which structures have been solved. In general, AlphaFold is trained to produce the protein structure most likely to appear [...] methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

• AlphaFold Server

  AlphaFold Database is a large collection of precomputed protein predictions, generated with the AlphaFold2 model. It covers a significant proportion of the proteins in UniProt, and entries can be quickly downloaded including in bulk. However, the predictions are always single chains (even if the protein forms multimers in nature) and contain _only_ the protein part (no ligands or co-factors). AlphaFold Database is free for commercial and non-commercial use, and requires no registration to [...] AlphaFold Server is a web-service that offers customized biomolecular structure prediction. It makes several newer AlphaFold3 capabilities available, including support for a wider range of molecule types (DNA, RNA, ions, ligands, chemical modifications). The service is free for non-commercial use and requires a simple sign up involving accepting non-commercial use terms. How can I increase the diversity of my predictions? --------------------------------------------------- link expand_more [...] AlphaFold Server is a web-service that can generate highly accurate biomolecular structure predictions containing proteins, DNA, RNA, ligands, ions, and also model chemical modifications for proteins and nucleic acids in one platform. It’s powered by the newest AlphaFold 3 model. ### Take a look at some examples Protein-RNA-Ion: PDB 8AW3 Protein-Glycan-Ion: PDB 7BBV Protein-DNA-Ion: PDB 7RCE Terms of use and attribution ----------------------------

• Alphafold 2: The AI System That Won Google a Nobel Prize

  In 2018, the AlphaFold project entered the Critical Assessment of protein Structure Prediction (CASP) competition, a biannual global challenge for protein structure prediction. AlphaFold's performance in this competition signalled a new era in structural biology. However, the true breakthrough came in 2020 with the unveiling of AlphaFold 2, which solved many of the most difficult protein folding problems with unprecedented accuracy. [...] At the heart of this year's Nobel Prize in Chemistry lies AlphaFold 2, an AI system developed by Google DeepMind. This remarkable tool has cracked what was once considered an insurmountable problem in biology: accurately predicting the three-dimensional structure of proteins from their amino acid sequences. [...] The impact of AlphaFold has been profound and far-reaching. The AlphaFold Protein Structure Database, which makes the system's predictions freely accessible, has been used by over two million researchers from 190 countries. This democratisation of cutting-edge AI technology has enabled breakthroughs in fields ranging from molecular biology to drug development and even climate science.

• How to predict structures with AlphaFold - Proteopedia, life in 3D

  In 2020, the AlphaFold project of Google's DeepMind team demonstrated a major breakthrough in predicting protein structure from sequence. Their success in the blind CASP competition astonished many experts. For an overview, see Theoretical models, bearing in mind "The Joys and Perils of AlphaFold". AlphaFold2 continued to have the highest success rate in the 2022 CASP 15 competition. In 2024, the AlphaFold team won half of the Nobel Prize in Chemistry. [...] In July, 2021, DeepMind released AlphaFold as open source code. Subsequently, several Colabs became available offering free structure prediction for user-submitted protein sequences. These Google Colabs (collaboratories). enable users to submit sequences via web browser, executing the code in the Google cloud, using space private to each user, returning predicted structures. In 2024, DeepMind provided the AlphaFold3 server (see below).

• Image

  

• Instance Of

  Q7397

• Inception Date

  1/1/2018

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 AI 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 protein 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 CASP 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 CASP 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. Nevertheless, there has been widespread respect for the technical achievement. On 15 July 2021 the AlphaFold 2 paper was published at Nature as an advance access publication alongside open source software and a searchable database of species proteomes.