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<h2 id="background">Background</h2> <p class="note">See also: <a href="/facts/Protein_structure_prediction/sj6AbLjI">Protein structure prediction</a> and <a href="/facts/De_novo_protein_structure_prediction/vsqAt3gb">De novo protein structure prediction</a></p> <p><a href="/facts/Protein/Jxmagu3E">Proteins</a> consist of <a href="/facts/Protein_primary_structure/QNBoERdu">chains of amino acids</a> which <a href="/facts/Protein_folding/rf3nR2Y1">spontaneously fold</a> to form the <a href="/facts/Protein_tertiary_structure/1QJVj1V1">three dimensional (3-D) structures</a> of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. </p><p>Protein structures can be determined experimentally through techniques such as <a href="/facts/X-ray_crystallography/WtI0F5DN">X-ray crystallography</a>, <a href="/facts/Cryo-Electron_Microscopy/Hjbcf4Ec">cryo-electron microscopy</a> and <a href="/facts/Nuclear_magnetic_resonance/pGYAn9Bz">nuclear magnetic resonance</a>, which are all expensive and time-consuming.<a class="footnote-ref" id="fnref:22" href="#fn:22"><sup>22</sup></a> Such efforts, using the experimental methods, have identified the structures of about 170,000 proteins over the last 60 years, while there are over 200 million known proteins across all life forms.<a class="footnote-ref" id="fnref:23" href="#fn:23"><sup>23</sup></a> </p><p>Over the years, researchers have applied numerous computational methods to <a href="/facts/Protein_structure_prediction/sj6AbLjI">predict the 3D structures of proteins</a> from their amino acid sequences, accuracy of such methods in best possible scenario is close to experimental techniques (NMR) by the use of <a href="/facts/Homology_modeling/rgaHkvJX">homology modeling</a> based on molecular evolution. <a href="/facts/CASP/A7fWoph9">CASP</a>, which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that <a href="/facts/Global_distance_test/pXwm1nkI">GDT</a> scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016.<a class="footnote-ref" id="fnref:24" href="#fn:24"><sup>24</sup></a> AlphaFold started competing in the 2018 CASP using an <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a> (AI) <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a> technique.<a class="footnote-ref" id="fnref:25" href="#fn:25"><sup>25</sup></a> </p> <h2 id="algorithm">Algorithm</h2> <p>DeepMind is known to have trained the program on over 170,000 proteins from the <a href="/facts/Protein_Data_Bank/oUhq4ABD">Protein Data Bank</a>, a public repository of protein sequences and structures. The program uses a form of <a href="/facts/Attention_(machine_learning)/AQ566C3b">attention network</a>, a <a href="/facts/Deep_learning/JLuwD3ea">deep learning</a> technique that focuses on having the <a href="/facts/AI/lJGauQwX">AI</a> identify parts of a larger problem, then piece it together to obtain the overall solution.<a class="footnote-ref" id="fnref:26" href="#fn:26"><sup>26</sup></a> The overall training was conducted on processing power between 100 and 200 <a href="/facts/GPUs/PTK1RQVp">GPUs</a>.<a class="footnote-ref" id="fnref:27" href="#fn:27"><sup>27</sup></a> </p> <h3>AlphaFold 1 (2018)</h3> <p>AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databanks of related DNA sequences now available from many different organisms (most without known 3D structures), to try to find changes at different <a href="/facts/Residue_(chemistry)/xE6sUTDa">residues</a> (peptides) that appeared to be correlated, even though the residues were not consecutive in the main chain. Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a <a href="/facts/Contact_map/neTkoWEV">contact map</a> to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this by estimating a probability distribution for the distances between residues, effectively transforming the contact map into a distance map. It also used more advanced learning methods than previously to develop the inference.<a class="footnote-ref" id="fnref:28" href="#fn:28"><sup>28</sup></a><a class="footnote-ref" id="fnref:29" href="#fn:29"><sup>29</sup></a> </p> <h3>AlphaFold 2 (2020)</h3> <p>The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind.<a class="footnote-ref" id="fnref:30" href="#fn:30"><sup>30</sup></a><a class="footnote-ref" id="fnref:31" href="#fn:31"><sup>31</sup></a> </p><p>AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined with a physics-based energy potential. AlphaFold 2 replaced this with a system of interconnected sub-networks, forming a single, differentiable, end-to-end model based on pattern recognition. This model was trained in an integrated manner.<a class="footnote-ref" id="fnref:32" href="#fn:32"><sup>32</sup></a><a class="footnote-ref" id="fnref:33" href="#fn:33"><sup>33</sup></a> After the neural network's prediction converges, a final refinement step applies local physical constraints using energy minimization based on the <a href="/facts/AMBER/AqkJF591">AMBER</a> force field. This step only slightly adjusts the predicted structure.<a class="footnote-ref" id="fnref:34" href="#fn:34"><sup>34</sup></a> </p><p>A key part of the 2020 system are two modules, believed to be based on a <a href="/facts/Transformer_(machine_learning_model)/cDbjx6a8">transformer</a> design, which are used to progressively refine a <a href="/facts/Word_embedding/7uRcBPqo">vector of information</a> for each relationship (or "<a href="/facts/Connectivity_(graph_theory)/FSNjsYhz">edge</a>" in graph-theory terminology) between an <a href="/facts/Amino_acid_residue/BptnTs9X">amino acid residue</a> of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input <a href="/facts/Sequence_alignment/p8GQfF7b">sequence alignment</a> (these relationships are represented by the array shown in red).<a class="footnote-ref" id="fnref:35" href="#fn:35"><sup>35</sup></a> Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learnt from training data. These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information.<a class="footnote-ref" id="fnref:36" href="#fn:36"><sup>36</sup></a> As the iteration progresses, according to one report, the "attention algorithm ... mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps—in this case clusters of amino acids—and then searching for ways to join the clumps in a larger whole."<a class="footnote-ref" id="fnref:37" href="#fn:37"><sup>37</sup></a>[<i>needs update</i>] </p><p>The output of these iterations then informs the final structure prediction module,<a class="footnote-ref" id="fnref:38" href="#fn:38"><sup>38</sup></a> which also uses transformers,<a class="footnote-ref" id="fnref:39" href="#fn:39"><sup>39</sup></a> and is itself then iterated. In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations – i.e. unphysical bond angles or lengths. With subsequent iterations the number of stereochemical violations fell. By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero.<a class="footnote-ref" id="fnref:40" href="#fn:40"><sup>40</sup></a> </p><p>The training data was originally restricted to single peptide chains. However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data. DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions.<a class="footnote-ref" id="fnref:41" href="#fn:41"><sup>41</sup></a> </p> <h3>AlphaFold 3 (2024)</h3> <p>Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and <a href="/facts/Isomorphic_Labs/XzEc9rdJ">Isomorphic Labs</a>, both subsidiaries of <a href="/facts/Alphabet_Inc./K3QvP6ns">Alphabet</a>. AlphaFold 3 is not limited to single-chain proteins, as it can also predict the structures of <a href="/facts/Protein_complex/GW11Ee34">protein complexes</a> with <a href="/facts/DNA/nB89Iauz">DNA</a>, <a href="/facts/RNA/HxPeNfNj">RNA</a>, <a href="/facts/Post-translational_modification/KMUayTIc">post-translational modifications</a> and selected <a href="/facts/Ligand/6etB3qeW">ligands</a> and <a href="/facts/Ion/3bePpDna">ions</a>.<a class="footnote-ref" id="fnref:42" href="#fn:42"><sup>42</sup></a><a class="footnote-ref" id="fnref:43" href="#fn:43"><sup>43</sup></a> </p><p>AlphaFold 3 introduces the "Pairformer," a deep learning architecture inspired by the transformer, which is considered similar to, but simpler than, the Evoformer used in AlphaFold 2.<a class="footnote-ref" id="fnref:44" href="#fn:44"><sup>44</sup></a><a class="footnote-ref" id="fnref:45" href="#fn:45"><sup>45</sup></a> The Pairformer module's initial predictions are refined by a <a href="/facts/Diffusion_model/ay78DDLn">diffusion model</a>. This model begins with a cloud of atoms and iteratively refines their positions, guided by the Pairformer's output, to generate a 3D representation of the molecular structure.<a class="footnote-ref" id="fnref:46" href="#fn:46"><sup>46</sup></a> </p><p>The AlphaFold server was created to provide free access to AlphaFold 3 for non-commercial research.<a class="footnote-ref" id="fnref:47" href="#fn:47"><sup>47</sup></a> </p> <h2 id="competitions">Competitions</h2> <h3>CASP13</h3> <p>In December 2018, DeepMind's AlphaFold placed first in the overall rankings of the 13th <a href="/facts/Critical_Assessment_of_Techniques_for_Protein_Structure_Prediction/A7fWoph9">Critical Assessment of Techniques for Protein Structure Prediction</a> (CASP).<a class="footnote-ref" id="fnref:48" href="#fn:48"><sup>48</sup></a><a class="footnote-ref" id="fnref:49" href="#fn:49"><sup>49</sup></a> </p><p>The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing <a href="/facts/Threading_(protein_sequence)/Abv8Cvgy">template structures</a> were available from proteins with a partially similar sequence. AlphaFold gave the best prediction for 25 out of 43 protein targets in this class,<a class="footnote-ref" id="fnref:50" href="#fn:50"><sup>50</sup></a><a class="footnote-ref" id="fnref:51" href="#fn:51"><sup>51</sup></a><a class="footnote-ref" id="fnref:52" href="#fn:52"><sup>52</sup></a> achieving a median score of 58.9 on the CASP's <a href="/facts/Global_distance_test/pXwm1nkI">global distance test</a> (GDT) score, ahead of 52.5 and 52.4 by the two next best-placed teams,<a class="footnote-ref" id="fnref:53" href="#fn:53"><sup>53</sup></a> who were also using deep learning to estimate contact distances.<a class="footnote-ref" id="fnref:54" href="#fn:54"><sup>54</sup></a><a class="footnote-ref" id="fnref:55" href="#fn:55"><sup>55</sup></a> Overall, across all targets, AlphaFold 1 achieved a GDT score of 68.5.<a class="footnote-ref" id="fnref:56" href="#fn:56"><sup>56</sup></a> </p><p>In January 2020, implementations and illustrative code of AlphaFold 1 was released <a href="/facts/Open-source_software/HzTaB8E9">open-source</a> on <a href="/facts/GitHub/n1hNCsc3">GitHub</a>.<a class="footnote-ref" id="fnref:57" href="#fn:57"><sup>57</sup></a><a class="footnote-ref" id="fnref:58" href="#fn:58"><sup>58</sup></a> but, as stated in the "Read Me" file on that website: "This code can't be used to predict structure of an arbitrary protein sequence. It can be used to predict structure only on the CASP13 dataset (links below). The feature generation code is tightly coupled to our internal infrastructure as well as external tools, hence we are unable to open-source it." Therefore, in essence, the code deposited is not suitable for general use but only for the CASP13 proteins. The company has not announced plans to make their code publicly available as of 5 March 2021. </p> <h3>CASP14</h3> <p>In November 2020, DeepMind's new version, AlphaFold 2, won CASP14.<a class="footnote-ref" id="fnref:59" href="#fn:59"><sup>59</sup></a><a class="footnote-ref" id="fnref:60" href="#fn:60"><sup>60</sup></a> Overall, AlphaFold 2 made the best prediction for 88 out of the 97 targets.<a class="footnote-ref" id="fnref:61" href="#fn:61"><sup>61</sup></a> </p><p>On the competition's preferred <a href="/facts/Global_distance_test/pXwm1nkI">global distance test</a> (GDT) measure of accuracy, the program achieved a median score of 92.4 (out of 100), meaning that more than half of its predictions were scored at better than 92.4% for having their atoms in more-or-less the right place,<a class="footnote-ref" id="fnref:62" href="#fn:62"><sup>62</sup></a><a class="footnote-ref" id="fnref:63" href="#fn:63"><sup>63</sup></a> a level of accuracy reported to be comparable to experimental techniques like <a href="/facts/X-ray_crystallography/WtI0F5DN">X-ray crystallography</a>.<a class="footnote-ref" id="fnref:64" href="#fn:64"><sup>64</sup></a><a class="footnote-ref" id="fnref:65" href="#fn:65"><sup>65</sup></a><a class="footnote-ref" id="fnref:66" href="#fn:66"><sup>66</sup></a> In 2018 AlphaFold 1 had only reached this level of accuracy in two of all of its predictions.<a class="footnote-ref" id="fnref:67" href="#fn:67"><sup>67</sup></a> 88% of predictions in the 2020 competition had a GDT_TS score of more than 80. On the group of targets classed as the most difficult, AlphaFold 2 achieved a median score of 87. </p><p>Measured by the <a href="/facts/Root-mean-square_deviation_of_atomic_positions/TEvYFrSA">root-mean-square deviation</a> (RMS-D) of the placement of the alpha-carbon atoms of the protein backbone chain, which tends to be dominated by the performance of the worst-fitted outliers, 88% of AlphaFold 2's predictions had an RMS deviation of less than 4 <a href="/facts/Angstrom/T5mjdd97">Å</a> for the set of overlapped C-alpha atoms.<a class="footnote-ref" id="fnref:68" href="#fn:68"><sup>68</sup></a> 76% of predictions achieved better than 3 Å, and 46% had a C-alpha atom RMS accuracy better than 2 Å,<a class="footnote-ref" id="fnref:69" href="#fn:69"><sup>69</sup></a> with a median RMS deviation in its predictions of 2.1 Å for a set of overlapped CA atoms.<a class="footnote-ref" id="fnref:70" href="#fn:70"><sup>70</sup></a> AlphaFold 2 also achieved an accuracy in modelling surface <a href="/facts/Side_chain/Lw8KPAXn">side chains</a> described as "really really extraordinary". </p><p>To further validate AlphaFold 2, the conference organizers approached four leading experimental groups working on structures they found particularly challenging and had been unable to determine. In all four cases the three-dimensional models produced by AlphaFold 2 were sufficiently accurate to determine structures of these proteins by <a href="/facts/Molecular_replacement/U09mRy5u">molecular replacement</a>. These included target T1100 (Af1503), a small <a href="/facts/Membrane_protein/FboTeM8Q">membrane protein</a> studied by experimentalists for ten years.<a class="footnote-ref" id="fnref:71" href="#fn:71"><sup>71</sup></a> </p><p>Of the three structures that AlphaFold 2 had the least success in predicting, two had been obtained by <a href="/facts/Nuclear_magnetic_resonance_spectroscopy_of_proteins/lCIXAFzQ">protein NMR</a> methods, which define protein structure directly in aqueous solution, whereas AlphaFold was mostly trained on <a href="/facts/X-ray_crystallography/WtI0F5DN">protein structures in crystals</a>. The third exists in nature as a <a href="/facts/Protein_domain/oUpFcSsl">multidomain complex</a> consisting of 52 identical copies of the same <a href="/facts/Protein_domain/oUpFcSsl">domain</a>, a situation AlphaFold was not programmed to consider. For all targets with a single domain, excluding only one very large protein and the two structures determined by NMR, AlphaFold 2 achieved a GDT_TS score of over 80. </p> <h3>CASP15</h3> <p>In 2022, DeepMind did not enter CASP15, but most of the entrants used AlphaFold or tools incorporating AlphaFold.<a class="footnote-ref" id="fnref:72" href="#fn:72"><sup>72</sup></a> </p> <h2 id="reception">Reception</h2> <p>AlphaFold 2 scoring more than 90 in <a href="/facts/CASP/A7fWoph9">CASP</a>'s <a href="/facts/Global_distance_test/pXwm1nkI">global distance test</a> (GDT) is considered a significant achievement in <a href="/facts/Computational_biology/GxgNfwcX">computational biology</a><a class="footnote-ref" id="fnref:73" href="#fn:73"><sup>73</sup></a> and great progress towards a decades-old grand challenge of biology.<a class="footnote-ref" id="fnref:74" href="#fn:74"><sup>74</sup></a> <a href="/facts/Nobel_Prize_in_Chemistry/8Nb8Ajjy">Nobel Prize</a> winner and <a href="/facts/Structural_biology/XRkPtHYv">structural biologist</a> <a href="/facts/Venki_Ramakrishnan/pUKfp6GN">Venki Ramakrishnan</a> called the result "a stunning advance on the protein folding problem",<a class="footnote-ref" id="fnref:75" href="#fn:75"><sup>75</sup></a> adding that "It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research."<a class="footnote-ref" id="fnref:76" href="#fn:76"><sup>76</sup></a> </p><p>Propelled by press releases from CASP and DeepMind,<a class="footnote-ref" id="fnref:77" href="#fn:77"><sup>77</sup></a><a class="footnote-ref" id="fnref:78" href="#fn:78"><sup>78</sup></a> AlphaFold 2's success received wide media attention.<a class="footnote-ref" id="fnref:79" href="#fn:79"><sup>79</sup></a> As well as news pieces in the specialist science press, such as <i><a href="/facts/Nature_(journal)/XmlJXkFx">Nature</a></i>,<a class="footnote-ref" id="fnref:80" href="#fn:80"><sup>80</sup></a> <i><a href="/facts/Science_(journal)/uTyzjkwD">Science</a></i>,<a class="footnote-ref" id="fnref:81" href="#fn:81"><sup>81</sup></a> <i><a href="/facts/MIT_Technology_Review/tk8zUzg2">MIT Technology Review</a></i>,<a class="footnote-ref" id="fnref:82" href="#fn:82"><sup>82</sup></a> and <i><a href="/facts/New_Scientist/GE0SBrn0">New Scientist</a></i>,<a class="footnote-ref" id="fnref:83" href="#fn:83"><sup>83</sup></a><a class="footnote-ref" id="fnref:84" href="#fn:84"><sup>84</sup></a> the story was widely covered by major national newspapers,.<a class="footnote-ref" id="fnref:85" href="#fn:85"><sup>85</sup></a><a class="footnote-ref" id="fnref:86" href="#fn:86"><sup>86</sup></a><a class="footnote-ref" id="fnref:87" href="#fn:87"><sup>87</sup></a><a class="footnote-ref" id="fnref:88" href="#fn:88"><sup>88</sup></a> A frequent theme was that ability to predict protein structures accurately based on the constituent amino acid sequence is expected to have a wide variety of benefits in the life sciences space including accelerating advanced drug discovery and enabling better understanding of diseases.<a class="footnote-ref" id="fnref:89" href="#fn:89"><sup>89</sup></a><a class="footnote-ref" id="fnref:90" href="#fn:90"><sup>90</sup></a> Some have noted that even a perfect answer to the protein <i><a href="/facts/Protein_structure_prediction/sj6AbLjI">prediction</a></i> problem would still leave questions about the protein <i><a href="/facts/Protein_folding/rf3nR2Y1">folding</a></i> problem—understanding in detail how the folding process actually occurs in nature (and how sometimes they can also <a href="/facts/Proteopathy/woWrXMXM">misfold</a>).<a class="footnote-ref" id="fnref:91" href="#fn:91"><sup>91</sup></a> </p><p>In 2023, <a href="/facts/Demis_Hassabis/Cua93qw0">Demis Hassabis</a> and <a href="/facts/John_M._Jumper/lhCqYdJw">John Jumper</a> won the <a href="/facts/Breakthrough_Prize_in_Life_Sciences/QJCb2m8F">Breakthrough Prize in Life Sciences</a><a class="footnote-ref" id="fnref:92" href="#fn:92"><sup>92</sup></a> as well as the <a href="/facts/Albert_Lasker_Award_for_Basic_Medical_Research/xYxF5z22">Albert Lasker Award for Basic Medical Research</a> for their management of the AlphaFold project.<a class="footnote-ref" id="fnref:93" href="#fn:93"><sup>93</sup></a> Hassabis and Jumper proceeded to win the <a href="/facts/Nobel_Prize_in_Chemistry/8Nb8Ajjy">Nobel Prize in Chemistry</a> in 2024 for their work on “protein structure prediction” with <a href="/facts/David_Baker_(biochemist)/LXWk3UDn">David Baker</a> of the University of Washington.<a class="footnote-ref" id="fnref:94" href="#fn:94"><sup>94</sup></a><a class="footnote-ref" id="fnref:95" href="#fn:95"><sup>95</sup></a> </p> <h2 id="source-code">Source code</h2> <p>Open access to source code of several AlphaFold versions (excluding AlphaFold 3) has been provided by DeepMind after requests from the scientific community.<a class="footnote-ref" id="fnref:96" href="#fn:96"><sup>96</sup></a><a class="footnote-ref" id="fnref:97" href="#fn:97"><sup>97</sup></a><a class="footnote-ref" id="fnref:98" href="#fn:98"><sup>98</sup></a> The source code of AlphaFold 3<a class="footnote-ref" id="fnref:99" href="#fn:99"><sup>99</sup></a> was made available for non-commercial use to the scientific community upon request in November 2024. </p> <h2 id="database-of-protein-models-generated-by-alphafold">Database of protein models generated by AlphaFold</h2> <p>The AlphaFold Protein Structure Database, a joint project between AlphaFold and <a href="/facts/EMBL-EBI/g1GsqPXd">EMBL-EBI</a>, was launched on July 22, 2021. At launch, the database contained AlphaFold-predicted <a href="/facts/Protein_structure_prediction/sj6AbLjI">models</a> for nearly the complete <a href="/facts/UniProt/lfDDHjAl">UniProt</a> <a href="/facts/Proteome/02gupgJW">proteome</a> of humans and 20 <a href="/facts/Model_organisms/vNv6q4yb">model organisms</a>, totaling over 365,000 proteins. The database does not include proteins with fewer than 16 or more than 2700 <a href="/facts/Amino_acid_residues/BptnTs9X">amino acid residues</a>,<a class="footnote-ref" id="fnref:100" href="#fn:100"><sup>100</sup></a> but for humans they are available in the whole batch file.<a class="footnote-ref" id="fnref:101" href="#fn:101"><sup>101</sup></a> AlphaFold's initial goal (as of early 2022) was to expand the database to cover most of the UniRef90 set, which contains over 100 million proteins. As of May 15, 2022, the database contained 992,316 predictions.<a class="footnote-ref" id="fnref:102" href="#fn:102"><sup>102</sup></a> </p><p>In July 2021, UniProt-KB and <a href="/facts/InterPro/pKwNm8t7">InterPro</a><a class="footnote-ref" id="fnref:103" href="#fn:103"><sup>103</sup></a> has been updated to show AlphaFold predictions when available.<a class="footnote-ref" id="fnref:104" href="#fn:104"><sup>104</sup></a> </p><p>On July 28, 2022, the team uploaded to the database the structures of around 200 million proteins from 1 million species, covering nearly every known protein on the planet.<a class="footnote-ref" id="fnref:105" href="#fn:105"><sup>105</sup></a> </p> <h2 id="limitations">Limitations</h2> <p>AlphaFold has various limitations: </p> <ul><li>AlphaFold DB provides models of individual protein chains (monomers), rather than their biologically relevant <a href="/facts/Protein_complexes/GW11Ee34">complexes.</a><a class="footnote-ref" id="fnref:106" href="#fn:106"><sup>106</sup></a></li> <li>Many protein regions are predicted with low confidence score, including the <a href="/facts/Intrinsically_disordered_protein/KrnRkv7K">intrinsically disordered protein</a> regions.<a class="footnote-ref" id="fnref:107" href="#fn:107"><sup>107</sup></a></li> <li>Alphafold-2 was validated for predicting structural effects of mutations with a limited success.<a class="footnote-ref" id="fnref:108" href="#fn:108"><sup>108</sup></a></li> <li>The model relies, to some extent, on co-evolutionary information from similar proteins. Therefore, it may not perform as well on synthetic proteins or proteins with very low homology to those in the training database.<a class="footnote-ref" id="fnref:109" href="#fn:109"><sup>109</sup></a></li> <li>The model's ability to predict multiple <a href="/facts/Native_state/uBHLassN">native</a> conformations of proteins is limited.</li> <li>AlphaFold 3 version can predict structures of protein complexes with a very limited set of selected <a href="/facts/Cofactor_(biochemistry)/AGo0PCUU">cofactors</a> and co- and <a href="/facts/Post-translational_modification/KMUayTIc">post-translational modifications</a>.<a class="footnote-ref" id="fnref:110" href="#fn:110"><sup>110</sup></a> Between 50% and 70% of the structures of the human proteome are incomplete without covalently-attached glycans.<a class="footnote-ref" id="fnref:111" href="#fn:111"><sup>111</sup></a><a class="footnote-ref" id="fnref:112" href="#fn:112"><sup>112</sup></a> AlphaFill, a derived database, adds cofactors to AlphaFold models where appropriate.<a class="footnote-ref" id="fnref:113" href="#fn:113"><sup>113</sup></a></li> <li>In the algorithm, the residues are moved freely, without any restraints. Therefore, during modeling the integrity of the chain is not maintained. As a result, AlphaFold may produce topologically wrong results, like structures with an arbitrary number of knots.<a class="footnote-ref" id="fnref:114" href="#fn:114"><sup>114</sup></a></li></ul> <h2 id="applications">Applications</h2> <p class="note">See also: <a href="/facts/Earth_BioGenome_Project/BulC3nSE">Earth BioGenome Project</a></p> <p>AlphaFold has been used to predict structures of proteins of <a href="/facts/Severe_acute_respiratory_syndrome_coronavirus_2/DxF8rDc1">SARS-CoV-2</a>, the causative agent of <a href="/facts/COVID-19_pandemic/0xTsh7Ut">COVID-19</a>. The structures of these proteins were pending experimental detection in early 2020.<a class="footnote-ref" id="fnref:115" href="#fn:115"><sup>115</sup></a><a class="footnote-ref" id="fnref:116" href="#fn:116"><sup>116</sup></a> Results were reviewed by scientists at the <a href="/facts/Francis_Crick_Institute/Q1kp12jo">Francis Crick Institute</a> in the United Kingdom before being released to the broader research community. The team also confirmed accurate prediction against the experimentally determined SARS-CoV-2 <a href="/facts/Coronavirus_spike_protein/JTcbvUn2">spike protein</a> that was shared in the <a href="/facts/Protein_Data_Bank/oUhq4ABD">Protein Data Bank</a>, an international open-access database, before releasing the computationally determined structures of the under-studied protein molecules.<a class="footnote-ref" id="fnref:117" href="#fn:117"><sup>117</sup></a> 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.<a class="footnote-ref" id="fnref:118" href="#fn:118"><sup>118</sup></a> Specifically, AlphaFold 2's prediction of the structure of the <i><a href="/facts/ORF3a/e2v3GrvG">ORF3a</a></i> protein was very similar to the structure determined by researchers at <a href="/facts/University_of_California,_Berkeley/MXVEjklr">University of California, Berkeley</a> using <a href="/facts/Cryo-Electron_Microscopy/Hjbcf4Ec">cryo-electron microscopy</a>. 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.<a class="footnote-ref" id="fnref:119" href="#fn:119"><sup>119</sup></a> </p> <h2 id="published-works">Published works</h2> <ul><li>Andrew W. Senior <i>et al.</i> (December 2019), <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.25834">"Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)"</a>, <i>Proteins: Structure, Function, Bioinformatics</i> 87(12) 1141–1148 <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1002%2Fprot.25834">10.1002/prot.25834</a></li> <li>Andrew W. Senior <i>et al.</i> (15 January 2020), <a href="https://www.nature.com/articles/s41586-019-1923-7">"Improved protein structure prediction using potentials from deep learning"</a>, <i><a href="/facts/Nature_(magazine)/XmlJXkFx">Nature</a></i> 577 706–710 <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fs41586-019-1923-7">10.1038/s41586-019-1923-7</a></li> <li>John Jumper <i>et al.</i> (December 2020), "High Accuracy Protein Structure Prediction Using Deep Learning", in <i><a href="https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf">Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book)</a></i>, pp. 22–24</li> <li>John Jumper <i>et al.</i> (December 2020), "<a href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf">AlphaFold 2</a>". Presentation given at CASP 14.</li></ul> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Folding@home/Kuuo4ISC">Folding@home</a></li> <li><a href="/facts/IBM_Blue_Gene/y4EvbyS4">IBM Blue Gene</a></li> <li><a href="/facts/Foldit/ogVIC4pD">Foldit</a></li> <li><a href="/facts/Rosetta@home/IfxGBjNU">Rosetta@home</a></li> <li><a href="/facts/Human_Proteome_Folding_Project/IzQTQv6a">Human Proteome Folding Project</a></li> <li><a href="/facts/AlphaZero/6uujYyM5">AlphaZero</a></li> <li><a href="/facts/AlphaGo/wJ9P2bBf">AlphaGo</a></li> <li><a href="/facts/AlphaGeometry/bKDaLbbY">AlphaGeometry</a></li> <li>Predicted Aligned Error</li></ul> <h2 id="further-reading">Further reading</h2> <ul><li>Carlos Outeiral, <a href="https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/">CASP14: what Google DeepMind's AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics</a>, Oxford Protein Informatics Group. (3 December)</li> <li>Mohammed AlQuraishi, <a href="https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/">AlphaFold2 @ CASP14: "It feels like one's child has left home."</a> (blog), 8 December 2020</li> <li>Mohammed AlQuraishi, <a href="https://moalquraishi.wordpress.com/2021/07/25/the-alphafold2-method-paper-a-fount-of-good-ideas/">The AlphaFold2 Method Paper: A Fount of Good Ideas</a> (blog), 25 July 2021</li></ul> <h2 id="external-links">External links</h2> <ul><li><a href="https://golgi.sandbox.google.com/about">AlphaFold-3 web server</a></li> <li><a href="https://github.com/deepmind/alphafold">AlphaFold v2.1 code and links to model</a> on <a href="/facts/GitHub/n1hNCsc3">GitHub</a></li> <li><a href="https://alphafold.ebi.ac.uk/">Open access to protein structure predictions for the human proteome and 20 other key organisms</a> at <a href="/facts/European_Bioinformatics_Institute/g1GsqPXd">European Bioinformatics Institute</a> (AlphaFold Protein Structure Database)</li> <li><a href="https://predictioncenter.org/casp14/index.cgi">CASP 14</a> website</li> <li><a href="https://www.youtube.com/watch?v=gg7WjuFs8F4">AlphaFold: The making of a scientific breakthrough</a>, DeepMind, via YouTube.</li> <li><a href="https://github.com/sokrypton/ColabFold">ColabFold</a> (Mirdita, Milot; Schütze, Konstantin; Moriwaki, Yoshitaka; Heo, Lim; Ovchinnikov, Sergey; Steinegger, Martin (2022-05-30). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184281">"ColabFold: Making protein folding accessible to all"</a>. <i>Nature Methods</i>. 19 (6): 679–682. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fs41592-022-01488-1">10.1038/s41592-022-01488-1</a>. <a href="/facts/PMC_(identifier)/dX1zMt71">PMC</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184281">9184281</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/35637307">35637307</a>.), <a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb">version</a> for homooligomeric prediction and complexes</li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>"AlphaFold". 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(1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 10 <a href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf" target="_blank">https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf</a> <a href="#fnref:35" class="footnote-back-ref">↩</a></p></li> <li id="fn:36"><p>See block diagram. Also John Jumper et al. (1 December 2020), AlphaFold 2 presentation Archived 2022-07-03 at the Wayback Machine, slide 10 <a href="https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf" target="_blank">https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf</a> <a href="#fnref:36" class="footnote-back-ref">↩</a></p></li> <li id="fn:37"><p>Robert F. Service, 'The game has changed.' 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