AlphaFold
競賽 > CASP14

在2020年11月，DeepMind的新版本AlphaFold 2在CASP14競賽中獲得了第一名。該程式對97個目標中的88個做出了最佳預測。根據競賽中的全球距離測試（GDT）衡量，AlphaFold 2的中位數分數達到了92.4（滿分為100），這意味著超過一半的預測結果在原子位置上的準確度超過92.4%，這一準確度被認為與X光晶體學等實驗技術相當。
AlphaFold 2在這次競賽中的表現遠遠超過了2018年AlphaFold 1的成績，當時只有2個預測達到相同的準確度。88%的預測結果在GDT_TS得分上超過了80，其中在最困難的目標群體中，AlphaFold 2的中位數得分為87。
此外，AlphaFold 2在蛋白質骨架主鏈的α碳原子位置的根均方誤差（RMSD）方面也表現出色，88%的預測結果在RMSD上小於4Å，76%的預測結果達到3Å以下，46%的預測結果更是精確到2Å以下。
AlphaFold 2的模型準確度被描述為「非常非常出色」，尤其是在表面側鏈的建模上。為了進一步驗證AlphaFold 2的準確性，競賽主辦方邀請了四個領先的實驗小組來測試那些他們無法確定結構的蛋白質，並且AlphaFold 2生成的三維模型在這些蛋白質結構的確定中足夠準確，這些結構被用於分子替代法。
儘管如此，AlphaFold 2在三個結構的預測上表現較差，其中兩個來自於蛋白質NMR（核磁共振）技術，這些結構是直接在水溶液中確定的，而AlphaFold大多數是基於X光晶體學數據進行訓練的。另外一個則是多結構複合體，包含52個相同的蛋白質結構單元，這樣的情況AlphaFold並未專門設計來處理。對於單一結構的所有目標（除了一個非常大的蛋白質和兩個NMR結構），AlphaFold 2的GDT_TS得分均超過80。
== 看法 == AlphaFold 2 在 CASP 的 全球距離測試 (GDT) 中得分超過 90 被認為是 計算生物學 領域的重大成就，也是邁向生物學數十年來的重大挑戰的一大進步。諾貝爾化學獎 得主、結構生物學家 Venki Ramakrishnan 稱這一結果是「在蛋白質摺疊問題上的驚人進展」， 並補充道：「這一進展發生得比很多領域內的專家預測的要早幾十年。看到它將如何根本改變生物學研究，將是非常令人興奮的。」
Propelled by press releases from CASP and DeepMind, AlphaFold 2's success received wide media attention.  As well as news pieces in the specialist science press, such as Nature, Science, MIT Technology Review, and New Scientist, the story was widely covered by major national newspapers, as well as general news-services and weekly publications, such as Fortune, The Economist, Bloomberg, Der Spiegel, and The Spectator. In London The Times made the story its front-page photo lead, with two further pages of inside coverage and an editorial.  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. 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."
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".
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, it has been described as "exceedingly high-level, heavy on ideas and insinuations, but almost entirely devoid of detail".Template:Unreliable source 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 paper giving an account of AlphaFold 2 in the proceedings volume[何时？] 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 science." 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 wholesale compute time. 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 DNA sequence repositories, without which the data to train AlphaFold 2 would not have existed.
Of note, on June 18th, 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!"
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.   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.
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.  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.
On its potential as a tool for drug discovery, Stephen Curry notes that while the resolution of AlphaFold 2's 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 0.3 Å margin, but the predicted protein structure only have at best an RMSD of 0.9 Å for all atoms. So AlphaFold 2's structures may only be a limited help in such contexts.  Moreover, according to Science columnist 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". 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. 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.
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. However, the authors highlighted that many AlphaFold models were accurate enough to allow for the introduction of post-predictional modifications.
Finally, some have noted that even a perfect answer to the protein prediction problem would still leave questions about the protein folding problem—understanding in detail how the folding process actually occurs in nature (and how sometimes they can also misfold).
But even with such caveats, AlphaFold 2 was described as a huge technical step forward and intellectual achievement.