AlphaFold（直譯：阿爾法折疊）是 Alphabet 旗下 Google 旗下 DeepMind 開發的一款蛋白質結構預測程式。該程式被設計為一個深度學習系統。 AlphaFold 人工智慧有 2 個主要版本：AlphaFold 1（2018）和 AlphaFold 2（2020）。前者使用 AlphaFold 1 在 2018 年 12 月的第 13 屆 CASP（，直譯：蛋白質結構預測的關鍵評估）的排名中第一。該程式特別成功地預測了被競賽組織者評為最困難的目標的最準確結構，其中沒有來自具有部分相似序列的蛋白質的現有模板結構。 蛋白質 zhcn 透過；zhtw 透過； 捲曲摺疊會構成三維結構，蛋白質的功能正由其結構決定。瞭解蛋白質結構有助於開發治療疾病的藥物。DeepMind 稱，AlphaFold 能在數天內識別蛋白質的形狀，而此前學界識別蛋白質形狀經常需花費數年時間。2020 年 11 月，在第 14 屆 CASP（，直譯：蛋白質結構預測的關鍵評估）競賽中，AlphaFold 2 2020 表現良好，中位分數為 924（滿分 100 分）。它的準確度遠遠高於其他任何 zhcn 程式；zhtw 程式； 。 2021 年 7 月 15 日，AlphaFold 2 論文在《自然》雜誌上作為高階訪問出版物與開源軟體和可搜尋的物種蛋白質組資料庫一起發表。 2024 年 5 月 8 日，AlphaFold 3 釋出。它可以預測蛋白質與 DNA、RNA、各種配體和離子形成的複合物的結構。 蛋白質折疊問題 蛋白質由蛋白質一級結構組成，蛋白質折疊的過程中蛋白質會自發折疊形成蛋白質三級結構。蛋白質結構對蛋白質生物學功能至關重要。然而，瞭解氨基酸序列如何確定蛋白質三級結構極具挑戰性，這被稱為「蛋白質折疊問題」。「蛋白質折疊問題」涉及折疊穩定結構的原子間力熱力學、蛋白質以極快速達到其最終折疊狀態的機制和途徑，以及如何從氨基酸序列預測蛋白質天然結構。 蛋白質結構過去透過諸如 X 射線晶體學、低溫電子顯微鏡和核磁共振等技術進行實驗確定，這些技術既昂貴又耗時。 過去 60 年努力只確定了約 170000 種蛋白質結構，而所有生命形式中已知蛋白質超過 2 億種。 如果可以僅從氨基酸序列預測蛋白質結構，將極大地促進科學研究。然而利文索爾佯謬表明，雖蛋白質可在幾毫秒內折疊，但隨機計算所有可能的結構以確定真正的天然結構所需的時間比已知宇宙的年齡要長，這使得預測蛋白質為科學家們構建了生物學中的一項重大挑戰。 多年來，研究人員應用了許多計算方法來解決蛋白質結構預測問題，但除了小而簡單的蛋白質外，它們準確性還遠遠遠沒有接近實驗技術，從而限制了科學研究。 CASP 於 1994 年發起，旨在挑戰科學界做出最好的蛋白質結構預測，結果對於最困難的到 2016 年的蛋白質發現分數也只能達到 100 滿分的 40 分。 2018 年，AlphaFold 使用人工智慧深度學習技術參加 CASP。 演算法 DeepMind is known to have trained the program on over 170000 proteins from a public repository of protein sequences and structures The program uses a form of attention network a deep learning technique that focuses on having the AI algorithm 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 AlphaFold 1（2018） 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 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 beturning 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 freeenergy of the configuration the team was then able to use gradient descent to a solution that best fitted bothMohammed AlQuraishi May 2019 AlphaFold at CASP13 Bioinformatics 35 22 48624865 See also Mohammed AlQuraishi December 9 2018 AlphaFold CASP13 What just happened blog postMohammed AlQuraishi 15 January 2020 A watershed moment for protein structure prediction Nature 577 627628 AlphaFold Machine learning for protein structure prediction Foldit 31 January 2020 More technically Torrisi et al summarised in 2019 the approach of AlphaFold version 1 as followsTorrisi Mirko et al 22 Jan 2020 Deep learning methods in protein structure prediction Computational and Structural Biotechnology Journal vol 18 13011310 CCBY40 Central to AlphaFold is a distance map predictor implemented as a very deep residual neural networks with 220 residual blocks processing a representation of dimensionality 6464128 corresponding to input features calculated from two 64 amino acid fragments Each residual block has three layers including a 33 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 coevolution features Alongside a distance map in the form of a very finelygrained histogram of distances AlphaFold predicts 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 29000 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 AlphaFold 2（2020） 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 DeepMindJeremy Kahn Lessons from DeepMinds breakthrough in proteinfolding AI Fortune 1 December 2020 The DeepMind team had identified that its previous approach combining local physics with a guide potential derived from pattern recognition had a tendency to overaccount 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 secondary structure alpha helices and beta sheets than was the case in reality a form of overfittingJohn Jumper et al conference abstract December 2020 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 physicsbased energy potential AlphaFold 2 replaced this with a system of subnetworks coupled together into a single differentiable endtoend model based entirely on pattern recognition which was trained in an integrated way as a single integrated structureSee block diagram Also John Jumper et al 1 December 2020 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 A key part of the 2020 system are two modules believed to be based on a transformer design which are used to progressively refine a vector of information for each relationship or edge in graphtheory 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 contextdependent 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 residueresidue information feeding into the update of the residuesequence information and then the improved residuesequence information feeding into the update of the residueresidue 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 clumpsin this case clusters of amino acidsand then searching for ways to join the clumps in a larger whole The output of these iterations then informs the final structure prediction module which also uses transformersThe structure module is stated to use a 3d equivariant transformer architecture John Jumper et al 1 December 2020 AlphaFold 2 presentation slide 12 One design for a transformer network with SE 3equivariance was proposed in Fabian Fuchs et al SE 3Transformers 3D RotoTranslation Equivariant Attention Networks NeurIPS 2020 also website It is not known how similar this may or may not be to what was used in AlphaFold See also the blog post by AlQuaraishi on this or the 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 ie 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 zeroJohn Jumper et al 1 December 2020 AlphaFold 2 presentation slides 12 to 20 The AlphaFold team stated in November 2020 that they believe AlphaFold can be further developed with room for further improvements in accuracy The training data was originally restricted to single peptide trains However the October 2021 update named AlphaFoldMultimer included protein complexes in its training data DeepMind stated this update succeeded about 70 of the time at accurately predicting proteinprotein interactions 競賽 CASP13 In December 2018 DeepMinds AlphaFold placed first in the overall rankings of the 13th Critical Assessment of Techniques for Protein Structure Prediction CASPGroup performance based on combined zscores CASP 13 December 2018 AlphaFold Team 043 A7D The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by the competition organisers where no existing template structures were available from proteins with a partially similar sequence AlphaFold gave the best prediction for 25 out of 43 protein targets in this class achieving a median score of 589 on the CASPs global distance test GDT score ahead of 525 and 524 by the two next bestplaced teamsSee CASP 13 data tables for 043 A7D 322 Zhang and 089 MULTICOM who were also using deep learning to estimate contact distancesWei Zheng et alDeeplearning contactmap guided protein structure prediction in CASP13 Proteins Structure Function and Bioinformatics 87 12 11491164 and slides Overall across all targets the program achieved a GDT score of 685 In January 2020 implementations and illustrative code of AlphaFold 1 was released opensource on GitHub but as stated in the Read Me file on that website This code cant 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 opensource 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 CASP14 In November 2020 DeepMinds new version AlphaFold 2 won CASP14 Overall AlphaFold 2 made the best prediction for 88 out of the 97 targets On the competitions preferred global distance test GDT measure of accuracy the program achieved a median score of 924 out of 100 meaning that more than half of its predictions were scored at better than 924 for having their atoms in moreorless the right placeFor the GDT_TS measure used each atom in the prediction scores a quarter of a point if it is within of the experimental position half a point if it is within 4 Å threequarters of a point if it is within 2 Å and a whole point if it is within 1 ÅTo achieve a GDT_TS score of 925 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 Xray crystallography 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 Measured by the rootmeansquare deviation RMSD of the placement of the alphacarbon atoms of the protein backbone chain which tends to be dominated by the performance of the worstfitted outliers 88 of AlphaFold 2s predictions had an RMS deviation of less than 4 Å for the set of overlapped Calpha atoms 76 of predictions achieved better than 3 Å and 46 had a Calpha atom RMS accuracy better than 2 ÅMohammed AlQuraishi CASP14 scores just came out and theyre astounding Twitter 30 November 2020 with a median RMS deviation in its predictions of 21 Å for a set of overlapped CA atoms AlphaFold 2 also achieved an accuracy in modelling surface side chains described as really really extraordinary To additionally verify AlphaFold2 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 threedimensional 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 Of the three structures that AlphaFold 2 had the least success in predicting two had been obtained by protein NMR methods which define protein structure directly in aqueous solution whereas AlphaFold was mostly trained on protein structures in crystals The third exists in nature as a multidomain complex consisting of 52 identical copies of the same 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 Responses AlphaFold 2 scoring more than 90 in CASPs global distance test GDT is considered a significant achievement in computational biology and great progress towards a decadesold grand challenge of biology Nobel Prize winner and 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 Propelled by press releases from CASP and DeepMindArtificial intelligence solution to a 50yearold science challenge could revolutionise medical research press release CASP organising committee 30 November 2020 AlphaFold 2s success received wide media attentionBrigitte Nerlich 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 Science MIT Technology Review and New ScientistMichael Le Page DeepMinds AI biologist can decipher secrets of the machinery of life New Scientist 30 November 2020The predictions of DeepMinds latest AI could revolutionise medicine New Scientist 2 December 2020 the story was widely covered by major national newspapersCade Metz London AI Lab Claims Breakthrough That Could Accelerate Drug Discovery New York Times 30 November 2020Ian SampleDeepMind AI cracks 50yearold problem of protein folding The Guardian 30 November 2020Lizzie Roberts Once in a generation advance as Google AI researchers crack 50yearold biological challenge Daily Telegraph 30 November 2020 as well as general newsservices and weekly publications such as FortuneJeremy Kahn In a major scientific breakthrough AI predicts the exact shape of proteins Fortune 30 November 2020 The Economist Bloomberg Der SpiegelJulia Merlot Forscher hoffen auf Durchbruch für die Medikamentenforschung Researchers hope for a breakthrough for drug research Der Spiegel 2 December 2020 and The SpectatorBissan AlLazikani The solving of a biological mystery The Spectator 1 December 2020 In London The Times made the story its frontpage photo lead with two further pages of inside coverage and an editorialTom Whipple Deepmind computer solves new puzzle life The Times 1 December 2020 front page image via TwitterTom Whipple Deepmind finds biologys holy grail with answer to protein problem The Times online 30 November 2020In all science editor Tom Whipple wrote six articles on the subject for The Times on the day the news broke 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 diseasesTim Hubbard The secret of life part 2 the solution of the protein folding problem mediumcom 30 November 2020 Writing about the event the MIT Technology Review noted that the AI had solved a fiftyyear 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 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 dont even know how they do itChristian Stöcker Google greift nach dem Leben selbst Google is reaching for life itself Der Spiegel 6 December 2020 Although a 30minute presentation about AlphaFold 2 was given on the second day of the CASP conference December 1 by project leader John JumperJohn Jumper et al 1 December 2020 AlphaFold 2 Presentation given at CASP 14 it has been described as exceedingly highlevel heavy on ideas and insinuations but almost entirely devoid of detail Unlike other research groups presenting at CASP14 DeepMinds presentation was not recorded and is not publicly available DeepMind is expected to publish a scientific paper giving an account of AlphaFold 2 in the proceedings volume of the CASP conference but it is not known whether it will go beyond what was said in the presentation 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 scienceNuño Dominguez 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 blackbox subscription service Convergence for a single sequence has been estimated to require on the order of 10000 worth of wholesale compute timeCarlos Outeiral CASP14 what Google DeepMinds 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 2s 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 DNA sequence repositories without which the data to train AlphaFold 2 would not have existedAled Edwards The AlphaFold2 success It took a village via mediumcom 5 December 2020David Briggs If Googles Alphafold2 really has solved the protein folding problem they need to show their working The Skeptic 4 December 2020The Guardian view on DeepMinds brain the shape of things to come The Guardian 6 December 2020 Of note on June 18th 2021 Demis Hassabis tweeted Brief update on some exciting progress on AlphaFold Weve 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 soonDemis Hassabis Brief update on some exciting progress on AlphaFold tweet via twitter 18 June 2021 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 moleculesTom Ireland 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 2s 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 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 databasesStephen Curry 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 Xray 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 2s difficulties with structures obtained by protein NMR methods may not be a good sign On its potential as a tool for drug discovery Stephen Curry notes that while the resolution of AlphaFold 2s structures may be very good the accuracy with which binding sites are modelled needs to be even higher typically molecular docking studies require the atomic positions to be accurate within a 03 Å margin but the predicted protein structure only have at best an RMSD of 09 Å for all atoms So AlphaFold 2s structures may only be a limited help in such contexts Moreover according to Science columnist Derek Lowe because the prediction of smallmolecule 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 discoveryso protein structure determination simply isnt a ratelimiting step in drug discovery in generalDerek Lowe In the Pipeline Whats Crucial And What Isnt 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 challengingPhilip Ball Behind the Screens of AlphaFold Chemistry World 9 December 2020 See also 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 improvementsDerek Lowe In the Pipeline The Big Problems Science Translational Medicine 1 December 2020 Also because AlphaFold processes proteinonly sequences by design other associated biomolecules are not considered On the impact of absent metals cofactors and most visibly co and posttranslational 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 UniProtKB for likely missing components as these can play an important role not just in folding but in protein function However the authors highlighted that many AlphaFold models were accurate enough to allow for the introduction of postpredictional modifications Finally some have noted that even a perfect answer to the protein prediction problem would still leave questions about the protein folding problemunderstanding in detail how the folding process actually occurs in nature and how sometimes they can also misfoldeg Greg Bowman Protein folding and related problems remain unsolved despite AlphaFolds advance Foldinghome blog 8 December 2020 But even with such caveats AlphaFold 2 was described as a huge technical step forward and intellectual achievementCristina Sáez 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 DeepMinds AlphaFold 2An Impressive Advance With Hyperbolic Coverage Skynet today blog Stanford 9 December 2020 AlphaFold 蛋白質結構資料庫 AlphaFold 蛋白質結構資料庫於 2021 年 7 月 22 日啟動，這是 AlphaFold 和歐洲分子生物學實驗室的歐洲生物資訊研究所的共同努力。AlphaFold 提供對超過 2 億個蛋白質結構預測的開放訪問，以加速科學研究。在啟動時，該資料庫包含人類和 20 種模式生物的幾乎完整 UniProt 蛋白質組的 AlphaFold 預測蛋白質結構模型，總計超過 365000 種蛋白質（該資料庫不包括少於 16 個或多於 2700 個氨基酸殘基蛋白質，但對人類而言，殘基蛋白質可在檔案中獲得。）。 AlphaFold 目標是覆蓋 UniRef90 中 1 億個蛋白質大部分集合。截至 2022 年 5 月 15 日，已有 992316 個可用。 應用 AlphaFold 已被用於預測 SARSCoV2（COVID19 的病原體）的蛋白質結構。 這些蛋白質的結構在 2020 年初有待實驗檢測。在將結果釋出到更大的研究界之前，英國 Francis Crick Institute 的科學家們對結果進行了檢查。該團隊還證實了對實驗確定的 SARSCoV2 刺突蛋白的準確預測，該蛋白在國際開放存取資料庫蛋白質資料庫 Protein Data Bank 中共享，然後發布了計算確定的未充分研究的蛋白質分子的結構。 參見 Foldinghome Rosettahome AlphaZero AlphaGo Foldit 參考文獻 外部連結 AlphaFold 2018 年 AlphaFold The making of a scientific breakthrough DeepMind via YouTube AlphaFold 2 2020 年 Open access to protein structure predictions for the human proteome and 20 other key organisms at 歐洲生物資訊研究所 EMBLEBI CASP 14 website AlphaFold The making of a scientific breakthrough DeepMind via YouTube ColabFold version for homooligomeric prediction and complexes Category 深度學習 Category 應用機器學習 Category 人工智慧應用 Category2020 年軟體