Account App Integration - Responsive Website Template

Array ( [0] => {{Short description|Artificial intelligence program by DeepMind}} [1] => {{Artificial intelligence}} [2] => '''AlphaFold''' is an [[artificial intelligence]] (AI) program developed by [[DeepMind]], a subsidiary of [[Alphabet Inc.|Alphabet]], which performs [[Protein structure prediction|predictions of protein structure]].{{cite web |title=AlphaFold |url=https://deepmind.com/research/case-studies/alphafold |website=Deepmind |access-date=30 November 2020}} The program is designed as a [[deep learning]] system.{{Cite web|title=DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology|url=https://www.technologyreview.com/2020/11/30/1012712/deepmind-protein-folding-ai-solved-biology-science-drugs-disease/|access-date=2020-11-30|website=MIT Technology Review|language=en}} [3] => [4] => 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 [[Threading (protein sequence)|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.{{Cite news |last=Shead|first=Sam |date=2020-11-30 |title=DeepMind solves 50-year-old 'grand challenge' with protein folding A.I. |url=https://www.cnbc.com/2020/11/30/deepmind-solves-protein-folding-grand-challenge-with-alphafold-ai.html |access-date=2020-11-30|website=CNBC|language=en}} The team achieved a level of accuracy much higher than any other group.{{cite journal |last1=Stoddart |first1=Charlotte |title=Structural biology: How proteins got their close-up |journal=Knowable Magazine |date=1 March 2022 |doi=10.1146/knowable-022822-1|s2cid=247206999 |doi-access=free |url=https://knowablemagazine.org/article/living-world/2022/structural-biology-how-proteins-got-their-closeup |access-date=25 March 2022}} It scored above 90 for around two-thirds of the proteins in CASP's [[Global distance test|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.Robert F. Service, [https://www.science.org/content/article/game-has-changed-ai-triumphs-solving-protein-structures 'The game has changed.' AI triumphs at solving protein structures], ''[[Science (magazine)|Science]]'', 30 November 2020 [5] => [6] => 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.{{cite web |url=https://www.chemistryworld.com/opinion/behind-the-screens-of-alphafold/4012867.article |title=Behind the screens of AlphaFold |first= Phillip |last= Balls|date=9 December 2020|work=Chemistry World }} 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|open source software]] and a searchable database of species [[Proteome|proteomes]].{{Cite journal|last1=Jumper|first1=John|last2=Evans|first2=Richard|last3=Pritzel|first3=Alexander|last4=Green|first4=Tim|last5=Figurnov|first5=Michael|last6=Ronneberger|first6=Olaf|last7=Tunyasuvunakool|first7=Kathryn|last8=Bates|first8=Russ|last9=Žídek|first9=Augustin|last10=Potapenko|first10=Anna|last11=Bridgland|first11=Alex|last12=Meyer|first12=Clemens|last13=Kohl|first13=Simon A A|last14=Ballard|first14=Andrew J|last15=Cowie|first15=Andrew|last16=Romera-Paredes|first16=Bernardino|last17=Nikolov|first17=Stanislav|last18=Jain|first18=Rishub|last19=Adler|first19=Jonas|last20=Back|first20=Trevor|last21=Petersen|first21=Stig|last22=Reiman|first22=David|last23=Clancy|first23=Ellen|last24=Zielinski|first24=Michal|last25=Steinegger|first25=Martin|last26=Pacholska|first26=Michalina|last27=Berghammer|first27=Tamas|last28=Bodenstein|first28=Sebastian|last29=Silver|first29=David|last30=Vinyals|first30=Oriol|last31=Senior|first31=Andrew W|last32=Kavukcuoglu|first32=Koray|last33=Kohli|first33=Pushmeet|last34=Hassabis|first34=Demis|date=2021-07-15|title=Highly accurate protein structure prediction with AlphaFold|journal=Nature|volume=596|issue=7873|pages=583–589|language=en|doi=10.1038/s41586-021-03819-2|pmid=34265844|pmc=8371605|bibcode=2021Natur.596..583J|doi-access=free}}{{Cite web|title=GitHub - deepmind/alphafold: Open source code for AlphaFold.|url=https://github.com/deepmind/alphafold|access-date=2021-07-24|website=GitHub|language=en}}{{Cite web|title=AlphaFold Protein Structure Database|url=https://alphafold.ebi.ac.uk/|access-date=2021-07-24|website=alphafold.ebi.ac.uk}} 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.[https://deepmind.google/discover/blog/a-glimpse-of-the-next-generation-of-alphafold/ A glimpse of the next generation of AlphaFold], 31 October 2023, by Google DeepMind AlphaFold team and Isomorphic Labs team [7] => [8] => == Protein folding problem == [9] => {{See also|Levinthal's paradox}} [10] => [[File:Protein folding figure.png|300px|thumb|alt=three individual polypeptide chains at different levels of folding and a cluster of chains|Amino-acid chains, known as [[polypeptide]]s, fold to form a protein.]] [11] => [12] => [[Protein]]s consist of [[Protein primary structure|chains of amino acid]]s which spontaneously fold, in a process called [[protein folding]], to form the [[Protein tertiary structure|three dimensional (3-D) structures]] of the proteins. The 3-D structure is crucial to the biological function of the protein. However, understanding how the amino acid sequence can determine the 3-D structure is highly challenging, and this is called the "protein folding problem".{{Cite web|title=AlphaFold: Using AI for scientific discovery|url=https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery|access-date=2020-11-30|website=Deepmind}} The "protein folding problem" involves understanding the thermodynamics of the interatomic forces that determine the folded stable structure, the mechanism and pathway through which a protein can reach its final folded state with extreme rapidity, and how the native structure of a protein can be predicted from its amino acid sequence.{{cite journal |title=The Protein Folding Problem |author=Ken A. Dill |author2=S. Banu Ozkan |author3=M. Scott Shell |author4=Thomas R. Weikl |journal =Annual Review of Biophysics|date= 2008|volume=37|pages=289–316| doi=10.1146/annurev.biophys.37.092707.153558 |pmid = 18573083 |pmc=2443096 }} [13] => [14] => Protein structures are currently determined experimentally by means of techniques such as [[X-ray crystallography]], [[Cryo-Electron Microscopy|cryo-electron microscopy]] and [[nuclear magnetic resonance]], techniques which are both expensive and time-consuming. Such efforts 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. If it is possible to predict protein structure from the amino-acid sequence alone, it would greatly help to advance scientific research. However, the [[Levinthal's paradox]] shows that while a protein can fold in milliseconds, the time it takes to calculate all the possible structures randomly to determine the true native structure is longer than the age of the known universe, which made predicting protein structures a grand challenge in biology for scientists. [15] => [16] => Over the years, researchers have applied numerous computational methods to resolve the issue of [[protein structure prediction]], but their accuracy has not been close to experimental techniques except for small simple proteins, thus limiting their value. [[CASP]], which was launched in 1994 to challenge the scientific community to produce their best protein structure predictions, found that [[Global distance test|GDT]] scores of only about 40 out of 100 can be achieved for the most difficult proteins by 2016. AlphaFold started competing in the 2018 CASP using an [[artificial intelligence]] (AI) [[deep learning]] technique. [17] => [18] => == Algorithm == [19] => DeepMind is known to have trained the program on over 170,000 proteins from a public repository of protein sequences and structures. The program uses a form of [[Attention (machine learning)|attention network]], a [[deep learning]] technique that focuses on having the [[AI]] identify parts of a larger problem, then piece it together to obtain the overall solution. The overall training was conducted on processing power between 100 and 200 [[GPUs]]. Training the system on this hardware took "a few weeks", after which the program would take "a matter of days" to converge for each structure. [20] => [21] => === AlphaFold 1, 2018 === [22] => [23] => '''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 [[Residue (chemistry)#Biochemistry|residues]] 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 [[contact map]] to be estimated. Building on recent work prior to 2018, AlphaFold 1 extended this to estimate a probability distribution for just ''how'' close the residues might be likely to be—turning the contact map into a likely distance map. It also used more advanced learning methods than previously to develop the inference. Combining a [[statistical potential]] based on this probability distribution with the calculated local [[Gibbs free energy|free-energy]] of the configuration, the team was then able to use [[gradient descent]] to a solution that best fitted both.{{clarify|date=December 2020}}[[Mohammed AlQuraishi]] (May 2019), [https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf AlphaFold at CASP13], ''Bioinformatics'', '''35'''(22), 4862–4865 {{doi|10.1093/bioinformatics/btz422}}. See also Mohammed AlQuraishi (December 9, 2018), [https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ AlphaFold @ CASP13: "What just happened?"] (blog post).
Mohammed AlQuraishi (15 January 2020), [https://www.nature.com/articles/d41586-019-03951-0 A watershed moment for protein structure prediction], ''[[Nature (journal)|Nature]]'' '''577''', 627–628 {{doi|10.1038/d41586-019-03951-0}}[http://fold.it/portal/node/2008706 AlphaFold: Machine learning for protein structure prediction], [[Foldit]], 31 January 2020 [24] => [25] => More technically, Torrisi ''et al'' summarised in 2019 the approach of AlphaFold version 1 as follows:Torrisi, Mirko et al. (22 Jan. 2020), [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305407/ Deep learning methods in protein structure prediction]. ''Computational and Structural Biotechnology Journal'' vol. '''18''' 1301–1310. {{doi|10.1016/j.csbj.2019.12.011}} (CC-BY-4.0) [26] =>

Central to AlphaFold is a distance map predictor implemented as a very deep [[residual neural network]] with 220 residual blocks processing a representation of dimensionality 64×64×128 – corresponding to input features calculated from two 64 amino acid fragments. Each residual block has three layers including a 3×3 dilated convolutional layer – the blocks cycle through dilation of values 1, 2, 4, and 8. In total the model has 21 million parameters. The network uses a combination of 1D and 2D inputs, including [[evolutionary profiles]] from different sources and co-evolution features. Alongside a distance map in the form of a very finely-grained histogram of distances, AlphaFold predicts [[Ramachandran plot|Φ and Ψ angles]] for each residue which are used to create the initial predicted 3D structure. The AlphaFold authors concluded that the depth of the model, its large crop size, the large training set of roughly 29,000 proteins, modern Deep Learning techniques, and the richness of information from the predicted histogram of distances helped AlphaFold achieve a high contact map prediction precision.

[27] => [28] => === AlphaFold 2, 2020 === [29] => [[File:AlphaFold 2.png|thumb|upright=1.3|AlphaFold 2 performance, experiments, and architecture{{cite journal |last1=Jumper |first1=John |last2=Evans |first2=Richard |last3=Pritzel |first3=Alexander |last4=Green |first4=Tim |last5=Figurnov |first5=Michael |last6=Ronneberger |first6=Olaf |last7=Tunyasuvunakool |first7=Kathryn |last8=Bates |first8=Russ |last9=Žídek |first9=Augustin |last10=Potapenko |first10=Anna |last11=Bridgland |first11=Alex |last12=Meyer |first12=Clemens |last13=Kohl |first13=Simon A. A. |last14=Ballard |first14=Andrew J. |last15=Cowie |first15=Andrew |last16=Romera-Paredes |first16=Bernardino |last17=Nikolov |first17=Stanislav |last18=Jain |first18=Rishub |last19=Adler |first19=Jonas |last20=Back |first20=Trevor |last21=Petersen |first21=Stig |last22=Reiman |first22=David |last23=Clancy |first23=Ellen |last24=Zielinski |first24=Michal |last25=Steinegger |first25=Martin |last26=Pacholska |first26=Michalina |last27=Berghammer |first27=Tamas |last28=Bodenstein |first28=Sebastian |last29=Silver |first29=David |last30=Vinyals |first30=Oriol |last31=Senior |first31=Andrew W. |last32=Kavukcuoglu |first32=Koray |last33=Kohli |first33=Pushmeet |last34=Hassabis |first34=Demis |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |date=August 2021 |volume=596 |issue=7873 |pages=583–589 |doi=10.1038/s41586-021-03819-2 |pmid=34265844 |language=en |issn=1476-4687 |display-authors=1|pmc=8371605 |bibcode=2021Natur.596..583J }}]] [30] => [[File:Architectural details of AlphaFold 2.png|thumb|upright=1.3|Architectural details of AlphaFold 2]] [31] => 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.Jeremy Kahn, [https://fortune.com/2020/12/01/lessons-from-deepminds-a-i-breakthrough-eye-on-ai/ Lessons from DeepMind's breakthrough in protein-folding A.I.], ''[[Fortune (magazine)|Fortune]]'', 1 December 2020 [32] => [33] => The DeepMind team had identified that its previous approach, combining local physics with a guide potential derived from pattern recognition, had a tendency to over-account for interactions between residues that were nearby in the sequence compared to interactions between residues further apart along the chain. As a result, AlphaFold 1 had a tendency to prefer models with slightly more [[Protein structure prediction#Protein structure and terminology|secondary structure]] ([[Alpha helix|alpha helices]] and [[beta sheet]]s) than was the case in reality (a form of [[overfitting]]).John Jumper et al., conference abstract (December 2020) [34] => [35] => The software design used in AlphaFold 1 contained a number of modules, each trained separately, that were used to produce the guide potential that was then combined with the physics-based energy potential. AlphaFold 2 replaced this with a system of sub-networks coupled together into a single differentiable end-to-end model, based entirely on pattern recognition, which was trained in an integrated way as a single integrated structure.See block diagram. Also John Jumper ''et al.'' (1 December 2020), [https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf AlphaFold 2 presentation], slide 10 Local physics, in the form of energy refinement based on the [[AMBER]] model, is applied only as a final refinement step once the neural network prediction has converged, and only slightly adjusts the predicted structure. [36] => [37] => A key part of the 2020 system are two modules, believed to be based on a [[Transformer (machine learning model)|transformer]] design, which are used to progressively refine a [[word embedding|vector of information]] for each relationship (or "[[Connectivity (graph theory)|edge]]" in graph-theory terminology) between an [[amino acid residue]] 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 [[sequence alignment]] (these relationships are represented by the array shown in red). 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. 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." [38] => [39] => The output of these iterations then informs the final structure prediction module, which also uses transformers,The structure module is stated to use a "3-d equivariant transformer architecture" (John Jumper ''et al.'' (1 December 2020), [https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf AlphaFold 2 presentation], slide 12).
One design for a transformer network with [[Euclidean group|SE(3)]]-[[Equivariant map|equivariance]] was proposed in Fabian Fuchs ''et al'' [https://arxiv.org/pdf/2006.10503.pdf SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks], [[NeurIPS]] 2020; also [https://fabianfuchsml.github.io/se3transformer/ website]. It is not known how similar this may or may not be to what was used in AlphaFold.
See also [https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/#s3.3 the blog post] by AlQuaraishi on this, or the [https://fabianfuchsml.github.io/alphafold2/ more detailed post] by Fabian Fuchs 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.John Jumper ''et al.'' (1 December 2020), [https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf AlphaFold 2 presentation], slides 12 to 20 [40] => [41] => The AlphaFold team stated in November 2020 that they believe AlphaFold can be further developed, with room for further improvements in accuracy. A recent analysis suggests that the current version of AlphaFold2 is already accurate enough to predict even single-mutation effects.{{Cite journal |last=McBride |first=John M. |last2=Polev |first2=Konstantin |last3=Abdirasulov |first3=Amirbek |last4=Reinharz |first4=Vladimir |last5=Grzybowski |first5=Bartosz A. |last6=Tlusty |first6=Tsvi |date=2023-11-20 |title=AlphaFold2 Can Predict Single-Mutation Effects |url=https://link.aps.org/doi/10.1103/PhysRevLett.131.218401 |journal=Physical Review Letters |language=en |volume=131 |issue=21 |doi=10.1103/PhysRevLett.131.218401 |issn=0031-9007|arxiv=2204.06860 }} [42] => [43] => 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.{{cite journal |last1=Callaway |first1=Ewen |title=What's next for AlphaFold and the AI protein-folding revolution |journal=Nature |date=13 April 2022 |volume=604 |issue=7905 |pages=234–238 |language=en |doi=10.1038/d41586-022-00997-5|pmid=35418629 |bibcode=2022Natur.604..234C |s2cid=248156195 |doi-access=free }} [44] => [45] => ==Competitions== [46] => [[File:CASP results 2020.png|thumb|right|500px|Results achieved for protein prediction by the best reconstructions in the CASP 2018 competition (small circles) and CASP 2020 competition (large circles), compared with results achieved in previous years.
The crimson trend-line shows how a handful of models including AlphaFold 1 achieved a significant step-change in 2018 over the rate of progress that had previously been achieved, particularly in respect of the protein sequences considered the most difficult to predict.
(Qualitative improvement had been made in earlier years, but it is only as changes bring structures within 8 [[Angstrom|Å]] of their experimental positions that they start to affect the CASP GDS-TS measure).
The orange trend-line shows that by 2020 online prediction servers had been able to learn from and match this performance, while the best other groups (green curve) had on average been able to make some improvements on it. However, the black trend curve shows the degree to which AlphaFold 2 had surpassed this again in 2020, across the board.
The detailed spread of data points indicates the degree of consistency or variation achieved by AlphaFold. Outliers represent the handful of sequences for which it did not make such a successful prediction.]] [47] => [48] => ===CASP13=== [49] => In December 2018, DeepMind's AlphaFold placed first in the overall rankings of the 13th [[Critical Assessment of Techniques for Protein Structure Prediction]] (CASP).[https://predictioncenter.org/casp13/zscores_final.cgi Group performance based on combined z-scores], CASP 13, December 2018. (AlphaFold = Team 043: A7D) [50] => [51] => The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing [[Threading (protein sequence)|template structures]] were available from proteins with a partially similar sequence. AlphaFold gave the best prediction for 25 out of 43 protein targets in this class,{{cite news|last1=Sample|first1=Ian|date=2 December 2018|title=Google's DeepMind predicts 3D shapes of proteins|work=The Guardian|url=https://www.theguardian.com/science/2018/dec/02/google-deepminds-ai-program-alphafold-predicts-3d-shapes-of-proteins|access-date=30 November 2020}}{{cite web|title=AlphaFold: Using AI for scientific discovery|url=https://deepmind.com/blog/article/alphafold-casp13|access-date=30 November 2020|website=Deepmind}}{{Cite journal|last=Singh|first=Arunima|date=2020|title=Deep learning 3D structures|journal=Nature Methods|language=en|volume=17|issue=3|pages=249|doi=10.1038/s41592-020-0779-y|pmid=32132733|s2cid=212403708|issn=1548-7105|doi-access=free}} achieving a median score of 58.9 on the CASP's [[Global distance test|global distance test (GDT)]] score, ahead of 52.5 and 52.4 by the two next best-placed teams,See [https://predictioncenter.org/casp13/results.cgi?view=tb-sel CASP 13 data tables] for 043 A7D, 322 Zhang, and 089 MULTICOM who were also using deep learning to estimate contact distances.Wei Zheng ''et al'',[https://pubmed.ncbi.nlm.nih.gov/31365149/ Deep-learning contact-map guided protein structure prediction in CASP13], ''Proteins: Structure, Function, and Bioinformatics'', '''87'''(12) 1149–1164 {{doi|10.1002/prot.25792}}; and [https://www.predictioncenter.org/CASP13/doc/presentations/Pred_CASP13_TS_YZhang-Groups_Redacted.pdf slides]{{cite journal | last1=Hou | first1=Jie | last2=Wu | first2=Tianqi | last3=Cao | first3=Renzhi | last4=Cheng | first4=Jianlin | title=Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 | journal=Proteins: Structure, Function, and Bioinformatics | publisher=Wiley | volume=87 | issue=12 | date=2019-04-25 | issn=0887-3585 | doi=10.1002/prot.25697 | pmc=6800999 | pages=1165–1178| pmid=30985027 |biorxiv=10.1101/552422}} Overall, across all targets, the program achieved a GDT score of 68.5.{{Cite news|date=2020-11-30|title=DeepMind Breakthrough Helps to Solve How Diseases Invade Cells|language=en|work=Bloomberg.com|url=https://www.bloomberg.com/news/articles/2020-11-30/deepmind-s-alphafold-crosses-threshold-in-solving-protein-riddle|access-date=2020-11-30}} [52] => [53] => In January 2020, implementations and illustrative code of AlphaFold 1 was released [[Open-source software|open-source]] on [[GitHub]].{{Cite web|title=deepmind/deepmind-research|url=https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13|access-date=2020-11-30|website=GitHub|language=en}} 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. [54] => [55] => ===CASP14=== [56] => In November 2020, DeepMind's new version, AlphaFold 2, won CASP14.{{cite web |title=AlphaFold: a solution to a 50-year-old grand challenge in biology |url=https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology |website=Deepmind |access-date=30 November 2020}}{{cite web |title=DeepMind's protein-folding AI has solved a 50-year-old grand challenge of biology |url=https://www.technologyreview.com/2020/11/30/1012712/deepmind-protein-folding-ai-solved-biology-science-drugs-disease/ |website=MIT Technology Review |access-date=30 November 2020 |language=en}} Overall, AlphaFold 2 made the best prediction for 88 out of the 97 targets. [57] => [58] => On the competition's preferred [[Global distance test|global distance test (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,For the GDT_TS measure used, each atom in the prediction scores a quarter of a point if it is within {{cvt|8|Å|nm}} of the experimental position; half a point if it is within 4 Å, three-quarters of a point if it is within 2 Å, and a whole point if it is within 1 Å.To achieve a GDT_TS score of 92.5, mathematically at least 70% of the structure must be accurate to within 1 Å, and at least 85% must be accurate to within 2 Å, a level of accuracy reported to be comparable to experimental techniques like [[X-ray crystallography]].{{Cite news |date=2020-11-30 |title=DeepMind is answering one of biology's biggest challenges|newspaper=The Economist|url=https://www.economist.com/science-and-technology/2020/11/30/deepmind-is-answering-one-of-biologys-biggest-challenges |access-date=2020-11-30 |issn=0013-0613}} In 2018 AlphaFold 1 had only reached this level of accuracy in two of all of its predictions. 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. [59] => [60] => Measured by the [[root-mean-square deviation of atomic positions|root-mean-square deviation]] (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 [[Angstrom|Å]] for the set of overlapped C-alpha atoms. 76% of predictions achieved better than 3 Å, and 46% had a C-alpha atom RMS accuracy better than 2 Å,Mohammed AlQuraishi, [https://twitter.com/MoAlQuraishi/status/1333383634649313280 CASP14 scores just came out and they're astounding], Twitter, 30 November 2020. with a median RMS deviation in its predictions of 2.1 Å for a set of overlapped CA atoms. AlphaFold 2 also achieved an accuracy in modelling surface [[side chain]]s described as "really really extraordinary". [61] => [62] => To additionally verify AlphaFold-2 the conference organisers approached four leading experimental groups for structures they were finding 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 [[molecular replacement]]. These included target T1100 (Af1503), a small [[membrane protein]] studied by experimentalists for ten years. [63] => [64] => Of the three structures that AlphaFold 2 had the least success in predicting, two had been obtained by [[Nuclear magnetic resonance spectroscopy of proteins|protein NMR]] methods, which define protein structure directly in aqueous solution, whereas AlphaFold was mostly trained on [[X-ray crystallography|protein structures in crystal]]s. The third exists in nature as a [[Protein domain#Multidomain proteins|multidomain complex]] consisting of 52 identical copies of the same [[protein domain|domain]], 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. [65] => [66] => === CASP15 === [67] => In 2022 DeepMind did not enter CASP15, but most of the entrants used AlphaFold or tools incorporating AlphaFold.{{Cite journal |last=Callaway |first=Ewen |date=2022-12-13 |title=After AlphaFold: protein-folding contest seeks next big breakthrough |journal=Nature |volume=613 |issue=7942 |pages=13–14 |language=en |doi=10.1038/d41586-022-04438-1|pmid=36513827 |s2cid=254660427 |doi-access=free }} [68] => [69] => == Responses == [70] => AlphaFold 2 scoring more than 90 in [[CASP]]'s [[Global distance test|global distance test (GDT)]] is considered a significant achievement in [[computational biology]] and great progress towards a decades-old grand challenge of biology. [[Nobel Prize in Chemistry|Nobel Prize]] winner and [[Structural biology|structural biologist]] [[Venki Ramakrishnan]] called the result "a stunning advance on the protein folding problem", 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." [71] => [72] => Propelled by press releases from CASP and DeepMind,[https://predictioncenter.org/casp14/doc/CASP14_press_release.html Artificial intelligence solution to a 50-year-old science challenge could 'revolutionise' medical research] (press release), [[CASP]] organising committee, 30 November 2020 AlphaFold 2's success received wide media attention.Brigitte Nerlich, [https://blogs.nottingham.ac.uk/makingsciencepublic/2020/12/04/protein-folding-and-science-communication-between-hype-and-humility/ Protein folding and science communication: Between hype and humility], [[University of Nottingham]] blog, 4 December 2020 As well as news pieces in the specialist science press, such as ''[[Nature (journal)|Nature]]'', ''[[Science (journal)|Science]]'', ''[[MIT Technology Review]]'', and ''[[New Scientist]]'',Michael Le Page, [https://www.newscientist.com/article/2261156-deepminds-ai-biologist-can-decipher-secrets-of-the-machinery-of-life/ DeepMind's AI biologist can decipher secrets of the machinery of life], ''[[New Scientist]]'', 30 November 2020[https://www.newscientist.com/article/2261613-the-predictions-of-deepminds-latest-ai-could-revolutionise-medicine/ The predictions of DeepMind's latest AI could revolutionise medicine], ''[[New Scientist]]'', 2 December 2020 the story was widely covered by major national newspapers,[[Cade Metz]], [https://www.nytimes.com/2020/11/30/technology/deepmind-ai-protein-folding.html London A.I. Lab Claims Breakthrough That Could Accelerate Drug Discovery], ''[[New York Times]]'', 30 November 2020Ian Sample,[https://www.theguardian.com/technology/2020/nov/30/deepmind-ai-cracks-50-year-old-problem-of-biology-research DeepMind AI cracks 50-year-old problem of protein folding], ''[[The Guardian]]'', 30 November 2020Lizzie Roberts, [https://www.telegraph.co.uk/news/2020/11/30/google-ai-researchers-crack-50-year-old-protein-folding-problem/ 'Once in a generation advance' as Google AI researchers crack 50-year-old biological challenge]. ''[[Daily Telegraph]]'', 30 November 2020 as well as general news-services and weekly publications, such as ''[[Fortune (magazine)|Fortune]]'',Jeremy Kahn, [https://fortune.com/2020/11/30/deepmind-protein-folding-breakthrough/ In a major scientific breakthrough, A.I. predicts the exact shape of proteins], ''[[Fortune (magazine)|Fortune]]'', 30 November 2020 ''[[The Economist]]'', [[Bloomberg LP|Bloomberg]], ''[[Der Spiegel]]'',Julia Merlot, [https://www.spiegel.de/wissenschaft/medizin/kuenstliche-intelligenz-sagt-faltung-von-proteinen-praezise-voraus-a-c52705ef-d3b0-440b-b325-acb6da0bd50b Forscher hoffen auf Durchbruch für die Medikamentenforschung] (Researchers hope for a breakthrough for drug research), ''[[Der Spiegel]]'', 2 December 2020 and ''[[The Spectator]]''.Bissan Al-Lazikani, [https://www.spectator.co.uk/article/the-solving-of-a-biological-mystery The solving of a biological mystery], ''[[The Spectator]]'', 1 December 2020 In London ''[[The Times]]'' made the story its front-page photo lead, with two further pages of inside coverage and an editorial.Tom Whipple, "Deepmind computer solves new puzzle: life", ''[[The Times]]'', 1 December 2020. [https://twitter.com/UNSNUK/status/1333711800676835329 front page image], via Twitter.Tom Whipple, [https://www.thetimes.co.uk/edition/news/deepmind-finds-biology-s-holy-grail-with-answer-to-protein-problem-htg6s7qlq Deepmind finds biology's 'holy grail' with answer to protein problem], ''[[The Times]]'' (online), 30 November 2020.
In all science editor Tom Whipple wrote six articles on the subject for ''The Times'' on the day the news broke. ([https://twitter.com/whippletom/status/1333494448420958210 thread]). 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.{{Cite journal|last=Callaway|first=Ewen|date=2020-11-30|title='It will change everything': DeepMind's AI makes gigantic leap in solving protein structures|journal=Nature|volume=588|issue=7837|pages=203–204|language=en|doi=10.1038/d41586-020-03348-4|pmid=33257889|bibcode=2020Natur.588..203C|s2cid=227243204 |doi-access=}}[[Tim Hubbard]], [https://timjph.medium.com/the-secret-of-life-part-2-the-solution-of-the-protein-folding-problem-c544f3a77ee3 The secret of life, part 2: the solution of the protein folding problem.], [[medium.com]], 30 November 2020 Writing about the event, the ''[[MIT Technology Review]]'' noted that the AI had "solved a fifty-year old grand challenge of biology." The same article went on to note that the AI algorithm could "predict the shape of proteins to within the width of an atom." [73] => [74] => As summed up by ''[[Der Spiegel]]'' reservations about this coverage have focussed in two main areas: "There is still a lot to be done" and: "We don't even know how they do it".Christian Stöcker, [https://www.spiegel.de/wissenschaft/lernende-maschinen-google-greift-nach-dem-leben-selbst-a-47424e26-a39d-4d62-9a89-bc06cd4647c0 Google greift nach dem Leben selbst] (Google is reaching for life itself), ''[[Der Spiegel]]'', 6 December 2020 [75] => [76] => Although a 30-minute presentation about AlphaFold 2 was given on the second day of the CASP conference (December 1) by project leader John Jumper,John Jumper ''et al.'' (1 December 2020), [https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf AlphaFold 2]. Presentation given at CASP 14. it has been described as "exceedingly high-level, heavy on ideas and insinuations, but almost entirely devoid of detail".{{Cite web|last=AlQuraishi|first=Mohammed|date=2020-12-08|title=AlphaFold2 @ CASP14: "It feels like one's child has left home." The Method|url=https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/#s3|url-status=live|archive-url=https://web.archive.org/web/20201208164545/https://moalquraishi.wordpress.com/2020/12/08/alphafold2-casp14-it-feels-like-ones-child-has-left-home/ |archive-date=2020-12-08 |access-date=2020-12-15|website=Some Thoughts on a Mysterious Universe|language=en}}{{unreliable source|date=February 2021}} Unlike other research groups presenting at CASP14, DeepMind's presentation was not recorded and is not publicly available. DeepMind is expected to publish a scientific article giving an account of AlphaFold 2 in the proceedings volume{{when|date=December 2020}} of the CASP conference; but it is not known whether it will go beyond what was said in the presentation. [77] => [78] => Speaking to ''[[El País]]'', researcher [[Alfonso Valencia]] said "The most important thing that this advance leaves us is knowing that this problem has a solution, that it is possible to solve it... We only know the result. Google does not provide the software and this is the frustrating part of the achievement because it will not directly benefit science."Nuño Dominguez, [https://elpais.com/ciencia/2020-12-02/la-inteligencia-artificial-arrasa-en-uno-de-los-problemas-mas-importantes-de-la-biologia.html La inteligencia artificial arrasa en uno de los problemas más importantes de la biología] (Artificial intelligence takes out one of the most important problems in biology), ''[[El País]]'', 2 December 2020 Nevertheless, as much as Google and DeepMind do release may help other teams develop similar AI systems, an "indirect" benefit. In late 2019 DeepMind released much of the code of the first version of AlphaFold as open source; but only when work was well underway on the much more radical AlphaFold 2. Another option it could take might be to make AlphaFold 2 structure prediction available as an online black-box subscription service. Convergence for a single sequence has been estimated to require on the order of $10,000 worth of [[Tensor Processing Unit|wholesale compute time]].Carlos Outeiral, [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], Oxford Protein Informatics Group. (3 December) But this would deny researchers access to the internal states of the system, the chance to learn more qualitatively what gives rise to AlphaFold 2's success, and the potential for new algorithms that could be lighter and more efficient yet still achieve such results. Fears of potential for a lack of transparency by DeepMind have been contrasted with five decades of heavy public investment into the open [[Protein Data Bank]] and then also into open [[List of biological databases#Nucleic acid databases|DNA sequence repositories]], without which the data to train AlphaFold 2 would not have existed.Aled Edwards, [https://structural-genomics-consortium.medium.com/the-alphafold2-success-fc8f54998f29 The AlphaFold2 success: It took a village], via [[medium.com]], 5 December 2020David Briggs, [https://www.skeptic.org.uk/2020/12/if-googles-alphafold2-really-has-solved-the-protein-folding-problem-they-need-to-show-their-working If Google's Alphafold2 really has solved the protein folding problem, they need to show their working], ''[[The Skeptic (UK magazine)|The Skeptic]]'', 4 December 2020[https://www.theguardian.com/commentisfree/2020/dec/06/the-guardian-view-on-deepminds-brain-the-shape-of-things-to-come The Guardian view on DeepMind's brain: the shape of things to come], ''[[The Guardian]]'', 6 December 2020 [79] => [80] => Of note, on June 18, 2021, Demis Hassabis tweeted: "Brief update on some exciting progress on #AlphaFold! We've been heads down working flat out on our full methods paper (currently under review) with accompanying open source code and on providing broad free access to AlphaFold for the scientific community. More very soon!"[[Demis Hassabis]], [https://twitter.com/demishassabis/status/1405922961710854144 "Brief update on some exciting progress on #AlphaFold!"] (tweet), via [[twitter]], 18 June 2021 [81] => [82] => However it is not yet clear to what extent structure predictions made by AlphaFold 2 will hold up for proteins bound into complexes with other proteins and other molecules.Tom Ireland, [https://thebiologist.rsb.org.uk/biologist/158-biologist/features/2550-how-will-alphafold-change-bioscience-research How will AlphaFold change bioscience research?], ''The Biologist'', 4 December 2020 This was not a part of the CASP competition which AlphaFold entered, and not an eventuality it was internally designed to expect. Where structures that AlphaFold 2 did predict were for proteins that had strong interactions either with other copies of themselves, or with other structures, these were the cases where AlphaFold 2's predictions tended to be least refined and least reliable. As a large fraction of the most important biological machines in a cell comprise such complexes, or relate to how protein structures become modified when in contact with other molecules, this is an area that will continue to be the focus of considerable experimental attention. [83] => [84] => 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.Stephen Curry, [http://occamstypewriter.org/scurry/2020/12/02/no-deepmind-has-not-solved-protein-folding/ No, DeepMind has not solved protein folding], Reciprocal Space (blog), 2 December 2020 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. [85] => [86] => So AlphaFold 2's structures may only be a limited help in such contexts. Moreover, according to ''Science'' columnist [[Derek Lowe (chemist)|Derek Lowe]], because the prediction of small-molecule binding even then is still not very good, computational prediction of drug targets is simply not in a position to take over as the "backbone" of corporate drug discovery—so "protein structure determination simply isn't a rate-limiting step in drug discovery in general".[[Derek Lowe (chemist)|Derek Lowe]], [https://www.science.org/content/blog-post/s-crucial-and-isn-t In the Pipeline: What's Crucial And What Isn't], ''Science Translational Medicine'', 25 September 2019 It has also been noted that even with a structure for a protein, to then understand how it functions, what it does, and how that fits within wider biological processes can still be very challenging.[[Philip Ball]], [https://www.chemistryworld.com/opinion/behind-the-screens-of-alphafold/4012867.article Behind the Screens of AlphaFold], ''[[Chemistry World]]'', 9 December 2020. See also [https://twitter.com/philipcball/status/1333822943630069760 tweets], 1 December Nevertheless, if better knowledge of protein structure could lead to better understanding of individual disease mechanisms and ultimately to better drug targets, or better understanding of the differences between human and animal models, ultimately that could lead to improvements.[[Derek Lowe (chemist)|Derek Lowe]], [https://www.science.org/content/blog-post/big-problems In the Pipeline: The Big Problems], ''Science Translational Medicine'', 1 December 2020 [87] => [88] => 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.{{Cite journal|last1=Bagdonas|first1=Haroldas|last2=Fogarty|first2=Carl A.|last3=Fadda|first3=Elisa|last4=Agirre|first4=Jon|date=2021-10-29|title=The case for post-predictional modifications in the AlphaFold Protein Structure Database|journal=Nature Structural & Molecular Biology|volume=28|issue=11|language=en|pages=869–870|doi=10.1038/s41594-021-00680-9|pmid=34716446|s2cid=240228913|issn=1545-9985|doi-access=free|url=https://mural.maynoothuniversity.ie/17434/1/ElisaFaddaAlpha2021.pdf}} However, the authors highlighted that many AlphaFold models were accurate enough to allow for the introduction of ''post-predictional'' modifications. [89] => [90] => Finally, some have noted that even a perfect answer to the protein ''[[protein structure prediction|prediction]]'' problem would still leave questions about the protein ''[[protein folding|folding]]'' problem—understanding in detail how the folding process actually occurs in nature (and how sometimes they can also [[proteopathy|misfold]]).e.g. Greg Bowman, [https://foldingathome.org/2020/12/08/protein-folding-and-related-problems-remain-unsolved-despite-alphafolds-advance/ Protein folding and related problems remain unsolved despite AlphaFold's advance], [[Folding@home]] blog, 8 December 2020 [91] => [92] => But even with such caveats, AlphaFold 2 was described as a huge technical step forward and intellectual achievement.Cristina Sáez, [https://www.lavanguardia.com/ciencia/20201202/49848364758/alfonso-valencia-inteligencia-artificial-estructura-proteinas-deepmind.html El último avance fundamental de la biología se basa en la investigación de un científico español], ''[[La Vanguardia]]'', 2 December 2020. ([[Alfonso Valencia]] overall view)Zero Gravitas and Jacky Liang, [https://www.skynettoday.com/briefs/alphafold2 DeepMind's AlphaFold 2—An Impressive Advance With Hyperbolic Coverage], ''Skynet today'' (blog), Stanford, 9 December 2020 [93] => [94] => == Protein Structure Database == [95] => {{Infobox biodatabase [96] => |title = AlphaFold Protein Structure Database [97] => |scope = protein structure prediction [98] => |organism = all UniProt proteomes [99] => |center = EMBL-EBI [100] => |citation = [101] => |url = https://www.alphafold.ebi.ac.uk/ [102] => |download = yes [103] => |webapp = yes [104] => |license = [[CC-BY 4.0]] [105] => |versioning = [106] => |frequency = [107] => |curation = automatic [108] => |bookmark = [109] => |version = [110] => }} [111] => 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 [[Protein structure prediction|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]],{{Cite web|title=AlphaFold Protein Structure Database|url=https://alphafold.ebi.ac.uk/faq|access-date=2021-07-29|website=alphafold.ebi.ac.uk}} but for humans they are available in the whole batch file.{{cite web |title=AlphaFold Protein Structure Database |url=https://alphafold.ebi.ac.uk/download |website=alphafold.ebi.ac.uk |access-date=27 July 2021}} 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.{{cite web |title=AlphaFold Protein Structure Database |url=https://www.alphafold.ebi.ac.uk |website=www.alphafold.ebi.ac.uk}} [112] => [113] => In July 2021, UniProt-KB and [[InterPro]]{{Cite web|last=InterPro|title=Alphafold Structure Predictions Available In Interpro|url=https://proteinswebteam.github.io/interpro-blog/2021/07/22/AlphaFold-structure-predictions-available-in-InterPro/|access-date=2021-07-29|website=proteinswebteam.github.io|date=22 July 2021 }} has been updated to show AlphaFold predictions when available.{{cite web |title=Putting the power of AlphaFold into the world's hands |url=https://deepmind.com/blog/article/putting-the-power-of-alphafold-into-the-worlds-hands |website=Deepmind}} [114] => [115] => 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.{{Cite journal |last=Callaway |first=Ewen |date=2022-07-28 |title='The entire protein universe': AI predicts shape of nearly every known protein |journal=Nature |volume=608 |issue=7921 |pages=15–16 |language=en |doi=10.1038/d41586-022-02083-2|pmid=35902752 |s2cid=251159714 |doi-access=free }} [116] => [117] => === Limitations === [118] => The AlphaFold DB uses a monomeric model similar to the CASP14 version. As a result, many of the same limitations are expected:{{cite web |title=What use cases does AlphaFold not support? |url=https://www.alphafold.ebi.ac.uk/faq#faq-8 |website=AlphaFold Protein Structure Database}} [119] => * The DB model only predicts monomers, missing some important context in the form of [[protein complexes]]. The AlphaFold Multimer model is published separately as open-source, but pre-run models are not available. [120] => * The model is unreliable for [[intrinsically disordered protein]]s, although it does convey the information via a low confidence score. [121] => * The model is not validated for mutational analysis. [122] => * The model relies to some extent upon co-evolutionary information across similar proteins, and thus may not perform well on synthetic proteins or proteins with very low homology to anything in the database.{{Cite magazine|magazine=Fast Company|url=https://www.fastcompany.com/90584816/deepmind-alphafold-alphabet-ai-proteins-drug-discovery|access-date=2023-01-24|title=DeepMind's latest AI breakthrough could turbocharge drug discovery|issn=1085-9241}} [123] => * The model can only output one conformation of proteins with multiple conformations, with no control of which. [124] => * The model only predicts protein structure without [[Cofactor (biochemistry)|cofactors]] and co- and post-translational modifications. This can be a significant shortcoming for a number of biologically-relevant systems: between 50% and 70% of the structures of the human proteome are incomplete without covalently-attached glycans.{{Cite journal|last1=An|first1=Hyun Joo|last2=Froehlich|first2=John W|last3=Lebrilla|first3=Carlito B|date=2009-10-01|title=Determination of glycosylation sites and site-specific heterogeneity in glycoproteins|journal=Current Opinion in Chemical Biology|series=Analytical Techniques/Mechanisms|language=en|volume=13|issue=4|pages=421–426| pmc=2749913 | doi=10.1016/j.cbpa.2009.07.022|pmid=19700364|issn=1367-5931}} On the other hand, since the model is trained from PDB models often with these modifications attached, the predicted structure is "frequently consistent with the expected structure in the presence of ions or cofactors". AlphaFill, a derived database, adds cofactors to AlphaFold models where appropriate.{{cite journal |last1=Hekkelman |first1=Maarten L. |last2=de Vries |first2=Ida |last3=Joosten |first3=Robbie P. |last4=Perrakis |first4=Anastassis |title=AlphaFill: enriching AlphaFold models with ligands and cofactors |journal=Nature Methods |date=February 2023 |volume=20 |issue=2 |pages=205–213 |doi=10.1038/s41592-022-01685-y |pmid=36424442 |pmc=9911346 |doi-access=free}} [125] => * 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.{{cite journal |last1=Dabrowski-Tumanski |first1=Pawel |last2=Stasiak |first2=Andrzej |title=AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins’ Topology |journal=Molecules |date=7 November 2023 |volume=28 |issue=22 |pages=7462 |doi=10.3390/molecules28227462|doi-access=free |pmc=10672856 }} [126] => [127] => == Applications == [128] => [129] => ===SARS-CoV-2=== [130] => AlphaFold has been used to predict structures of proteins of [[Severe acute respiratory syndrome coronavirus 2|SARS-CoV-2]], the causative agent of [[COVID-19 pandemic|COVID-19]]. The structures of these proteins were pending experimental detection in early 2020.{{Cite magazine|title=AI Can Help Scientists Find a Covid-19 Vaccine|language=en-us|magazine=Wired|url=https://www.wired.com/story/opinion-ai-can-help-find-scientists-find-a-covid-19-vaccine/|access-date=2020-12-01|issn=1059-1028}} 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 [[coronavirus spike protein|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.{{Cite web|title=Computational predictions of protein structures associated with COVID-19|url=https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19|access-date=2020-12-01|website=Deepmind}} 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. 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|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.{{Cite web|title=How DeepMind's new protein-folding A.I. is already helping to combat the coronavirus pandemic.|url=https://fortune.com/2020/11/30/covid-protein-folding-deepmind-ai/|access-date=2020-12-01|website=Fortune|language=en}} [131] => [132] => == Published works == [133] => * Andrew W. Senior ''et al.'' (December 2019), [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)"], ''Proteins: Structure, Function, Bioinformatics'' '''87'''(12) 1141–1148 {{doi|10.1002/prot.25834}} [134] => * Andrew W. Senior ''et al.'' (15 January 2020), [https://www.nature.com/articles/s41586-019-1923-7 "Improved protein structure prediction using potentials from deep learning"], ''[[Nature (magazine)|Nature]]'' '''577''' 706–710 {{doi|10.1038/s41586-019-1923-7}} [135] => * John Jumper ''et al.'' (December 2020), "High Accuracy Protein Structure Prediction Using Deep Learning", in ''[https://predictioncenter.org/casp14/doc/CASP14_Abstracts.pdf Fourteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstract Book)]'', pp. 22–24 [136] => * John Jumper ''et al.'' (December 2020), "[https://predictioncenter.org/casp14/doc/presentations/2020_12_01_TS_predictor_AlphaFold2.pdf AlphaFold 2]". Presentation given at CASP 14. [137] => [138] => ==See also== [139] => {{div col|colwidth=30em}} [140] => * [[Folding@home]] [141] => *[[IBM Blue Gene]] [142] => *[[Foldit]] [143] => *[[Rosetta@home]] [144] => *[[Human Proteome Folding Project]] [145] => * [[AlphaZero]] [146] => * [[AlphaGo]] [147] => * [[AlphaGeometry]] [148] => * [[Predicted Aligned Error]] [149] => * [https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1 ESMFold] [150] => {{div col end}} [151] => [152] => ==References== [153] => {{reflist}} [154] => [155] => == Further reading == [156] => * Carlos Outeiral, [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], Oxford Protein Informatics Group. (3 December) [157] => * Mohammed AlQuraishi, [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."] (blog), 8 December 2020 [158] => * Mohammed AlQuraishi, [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] (blog), 25 July 2021 [159] => [160] => == External links == [161] => *{{GitHub|deepmind/alphafold|AlphaFold v2.1 code and links to model}} [162] => * [https://alphafold.ebi.ac.uk/ Open access to protein structure predictions for the human proteome and 20 other key organisms] at [[European Bioinformatics Institute]] [163] => * [https://predictioncenter.org/casp14/index.cgi CASP 14] website [164] => * [https://www.youtube.com/watch?v=gg7WjuFs8F4 AlphaFold: The making of a scientific breakthrough], DeepMind, via YouTube. [165] => * [https://github.com/sokrypton/ColabFold ColabFold] ({{cite journal | last1=Mirdita | first1=Milot | last2=Schütze | first2=Konstantin | last3=Moriwaki | first3=Yoshitaka| last4=Heo | first4=Lim | last5=Ovchinnikov | first5=Sergey | last6=Steinegger | first6=Martin | title=ColabFold: Making protein folding accessible to all | journal=Nature Methods | date=2022-05-30 | volume=19 | issue=6 | pages=679–682 | doi=10.1038/s41592-022-01488-1 | pmid=35637307 | pmc=9184281 | doi-access=free | language=en }}), [https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb version] for homooligomeric prediction and complexes [166] => * [https://alphafold.ebi.ac.uk/ AlphaFold Protein Structure Database] website [167] => [168] => {{Google AI}} [169] => {{Differentiable computing}} [170] => [171] => [[Category:Bioinformatics software]] [172] => [[Category:Applications of artificial intelligence]] [173] => [[Category:Applied machine learning]] [174] => [[Category:Protein folding]] [175] => [[Category:Deep learning software applications]] [176] => [[Category:Molecular modelling software]] [177] => [[Category:Google DeepMind]] [] => )

good wiki

AlphaFold

AlphaFold is a computational method for predicting the 3D structure of proteins, developed by the DeepMind division of Alphabet Inc. The software uses deep learning algorithms to analyze the amino acid sequence of a protein and predict its most likely 3D structure.

More about us

About

The software uses deep learning algorithms to analyze the amino acid sequence of a protein and predict its most likely 3D structure. This breakthrough technology has been highly successful and has significantly advanced the field of protein folding, which is critical for understanding biological functions and developing new drugs. AlphaFold's predictions have been praised for their accuracy and have even outperformed other methods in recent assessments. The availability of this software has the potential to revolutionize research in various fields, including biochemistry, medicine, and biotechnology.

Expert Team

Vivamus eget neque lacus. Pellentesque egauris ex.

Award winning agency

Lorem ipsum, dolor sit amet consectetur elitorceat .

10 Year Exp.

Pellen tesque eget, mauris lorem iupsum neque lacus.

AlphaFold

About

Expert Team

Award winning agency

10 Year Exp.

You might be interested in

GitHub

Protein

UniProt

Service

About us

Technology

Locations