AlphaFold

AlphaFold is an <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> (AI) program developed by <a href="/facts/DeepMind/n5ehBwCx">DeepMind</a>, a subsidiary of <a href="/facts/Alphabet_Inc./K3QvP6ns">Alphabet</a>, which performs <a href="/facts/Protein_structure_prediction/sj6AbLjI">predictions of protein structure</a>. It is designed using <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a> techniques.
AlphaFold 1 (2018) placed first in the overall rankings of the 13th <a href="/facts/Critical_Assessment_of_Structure_Prediction/A7fWoph9">Critical Assessment of Structure Prediction</a> (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 <a href="/facts/Threading_(protein_sequence)/Abv8Cvgy">template structures</a> 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 <a href="/facts/Global_distance_test/pXwm1nkI">global distance test</a> (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 <a href="/facts/Metagenomics/GWFeMd7z">metagenomic</a> data has improved the quality of the prediction of <a href="/facts/Multiple_sequence_alignment/en7c7SQs">MSAs</a>. 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 <a href="/facts/Protein_folding/rf3nR2Y1">protein folding</a> for the <a href="/facts/Protein_folding_problem/sj6AbLjI">protein folding problem</a>, which remains unsolved.
Despite this, the technical achievement was widely recognized. On 15 July 2021, the AlphaFold 2 paper was published in <a href="/facts/Nature_(journal)/XmlJXkFx">Nature</a> as an advance access publication alongside <a href="/facts/Open-source_software/HzTaB8E9">open source software</a> and a searchable database of species <a href="/facts/Proteome/02gupgJW">proteomes</a>. As of February 2025, the paper had been cited nearly 32,000 times.
AlphaFold 3 was announced on 8 May 2024. It can predict the structure of <a href="/facts/Protein_complexes/GW11Ee34">complexes</a> created by proteins with <a href="/facts/DNA/nB89Iauz">DNA</a>, <a href="/facts/RNA/HxPeNfNj">RNA</a>, various <a href="/facts/Ligand_(biochemistry)/44QLL4eU">ligands</a>, and <a href="/facts/Ion/3bePpDna">ions</a>. 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.
<a href="/facts/Demis_Hassabis/Cua93qw0">Demis Hassabis</a> and <a href="/facts/John_M._Jumper/lhCqYdJw">John Jumper</a> of <a href="/facts/Google_DeepMind/n5ehBwCx">Google DeepMind</a> shared one half of the 2024 <a href="/facts/Nobel_Prize_in_Chemistry/8Nb8Ajjy">Nobel Prize in Chemistry</a>, awarded "for protein structure prediction," while the other half went to <a href="/facts/David_Baker_(biochemist)/LXWk3UDn">David Baker</a> "for computational protein design." Hassabis and Jumper had previously won the <a href="/facts/Breakthrough_Prize_in_Life_Sciences/QJCb2m8F">Breakthrough Prize in Life Sciences</a> and the <a href="/facts/Albert_Lasker_Award_for_Basic_Medical_Research/xYxF5z22">Albert Lasker Award for Basic Medical Research</a> in 2023 for their leadership of the AlphaFold project.

AlphaFold open-in-new

AlphaFold