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Economist:The shapes of things to come- 经济学人杂志在线阅读

翻译研究 2020-12-15

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一、经济学人杂志双语文章摘要

目前,分子生物学家可以用X射线晶体学等技术通过实验来探究蛋白质的形状。但这既费力又耗时。现在,事情可能要容易多了。11月30日,谷歌母公司Alphabet旗下的AI实验室DeepMind的研究人员发布的研究显示,他们在生物学中最严峻的一项挑战上取得了巨大进展,那就是仅凭一种蛋白质的氨基酸成分,用计算机预测它的形状。

二、经济学人杂志双语文章中英对照翻译

The shapes of things to come 未来的形态

2020.12


Computational biology 计算生物学
Artificial intelligence is solving one of biology’s biggest challenges
人工智能正在攻克生物学最大的挑战之一

经济学人杂志在线阅读

TO UNDERSTAND LIFE, you must understand proteins. These molecular chains, each assembled from a menu of 20 types of chemical links called amino acids, do biology’s heavy lifting. In the guise of enzymes they catalyse the chemistry that keeps bodies running. Actin and myosin, the proteins of muscles, permit those bodies to move around. Keratin provides their skin and hair. Haemoglobin carries their oxygen. Insulin regulates their metabolism. And a protein called spike allows coronaviruses to invade human cells, thereby shutting down entire economies.
要了解生命,必须了解蛋白质。这些分子链每条都由总共20种称为氨基酸的化学链中的某些连接组成,担负着大量的生理功能。它们以酶的形式催化着使身体保持运转的化学反应。肌肉中的肌动蛋白和肌球蛋白让身体可以自由活动。角蛋白是皮肤和头发的主要成分。血红蛋白携带氧气。胰岛素调节身体的新陈代谢。而一种叫做刺突的蛋白质让冠状病毒得以入侵人体细胞,最终令整个国家停摆。
Listing a protein’s amino acids is easy. Machines to do so have existed for decades. But this is only half the battle in the quest to understand how proteins work. What a protein does, and how it does it, depends also on how it folds up after its creation, into its final, intricate shape.
列出组成一种蛋白质的氨基酸很容易。具备这种功能的机器已经发明几十年了。但要了解蛋白质的作用机制,列出氨基酸只是完成了工作的一半。一种蛋白质的作用及其作用机制还取决于蛋白质在生成后如何折叠成最终的复杂形状。
At the moment, molecular biologists can probe proteins’ shapes experimentally, using techniques like X-ray crystallography. But this is fiddly and time-consuming. Now, things may be about to get much easier. On November 30th researchers from DeepMind, an artificial-intelligence (AI) laboratory owned by Alphabet, Google’s parent company, presented results suggesting that they have made enormous progress on one of biology’s grandest challenges—how to use a computer to predict a protein’s shape from just a list of its amino-acid components.
目前,分子生物学家可以用X射线晶体学等技术通过实验来探究蛋白质的形状。但这既费力又耗时。现在,事情可能要容易多了。11月30日,谷歌母公司Alphabet旗下的AI实验室DeepMind的研究人员发布的研究显示,他们在生物学中最严峻的一项挑战上取得了巨大进展,那就是仅凭一种蛋白质的氨基酸成分,用计算机预测它的形状。
Chain gangs 长链组合 To non-biologists, this may sound somewhere between arcane and prosaic. In fact, it is a big achievement. Replacing months of experiments with a few hours of computing time could shed new light on the inner workings of cells. It could speed up drug development. And it could in particular suggest treatments for diseases like Alzheimer’s, in which misshapen proteins are thought to play a role.
对除生物学家以外的人来说,这听起来可能有些晦涩和单调。实际上这是一项重大成就。用几小时的计算替代几个月的实验可能会进一步揭示细胞的内部运作机制。它可以加速药物研发,尤其是可以为阿尔兹海默症这类与畸形蛋白质有关的疾病提出疗法。
But there is yet more to it than that. Until now, the machine-learning techniques which DeepMind’s team used to attack the protein-folding problem have been best known for powering things like face-recognition cameras and voice assistants, and for defeating human beings at tricky games like Go. But Demis Hassabis, DeepMind’s boss, who founded in 2010 what was then an independent firm, did so hoping that they could also be employed to accelerate the progress of science. This result demonstrates how that might work in practice.
不止于此。DeepMind团队用机器学习技术来攻克蛋白质折叠问题,而这种技术到目前为止最广为人知的应用包括面部识别摄像头和语音助手,以及在围棋等复杂比赛中击败人类。但DeepMind的老板戴米斯·哈萨比斯(Demis Hassabis)在2010年创建公司时(当时还是一家独立公司)希望机器学习还可以用来加速科学的发展。现在这项成果显示了这可能如何付诸实践。
The idea of using computers to predict proteins’ shapes is half a century old. Progress has been real, but slow, says Ewan Birney, deputy director of the European Molecular Biology Laboratory, a multinational endeavour with headquarters in Germany. And it has been marked by a history of wrong turns and premature declarations of victory.
用计算机预测蛋白质形状的想法在半个世纪前就有了。总部位于德国的政府间组织欧洲分子生物学实验室(European Molecular Biology Laboratory)的副主管伊万·伯尼(Ewan Birney)说,之前有实质性的进展,但很慢。过程中还走了不少弯路,也曾过早地宣布胜利。
These days a humbler field, protein-shape prediction now measures its progress by how well algorithms perform in something called Critical Assessment of Protein Structure Prediction (CASP). This is a biennial experiment-cum-competition which started in 1994 and is jokingly dubbed the “Olympics of protein-folding”. In it, algorithms are subjected to blind tests of their ability to predict the shapes of several proteins of known structure.
如今这个领域已变得更谦逊。其进展由“蛋白质结构预测关键评估”(CASP)中算法的表现来衡量。CASP是一项始于1994年的实验兼比赛,两年举办一次,被戏称为“蛋白质折叠的奥林匹克竞赛”。它通过盲测检验算法预测几种已知结构的蛋白质的能力。
DeepMind’s first entry to CASP, two years ago, was dubbed AlphaFold. It made waves by performing much better than any other then-existing program. The current version, AlphaFold 2, has stretched that lead still further (see chart). One measure of success within CASP is the global-distance test. This assigns algorithms a score between zero and 100 by comparing the predicted locations of atoms in a molecule’s structure with their location in reality. AlphaFold 2 had an average score of 92.4—an accuracy that CASP’s founder, John Moult, who is a biologist at the University of Maryland, says is roughly comparable with what can be obtained by techniques like X-ray crystallography.
DeepMind两年前首次参加CASP的程序名为“阿尔法否”(AlphaFold)。它的表现远胜于当时任何其他程序,引起轰动。现在这版阿尔法否2进一步扩大了领先优势(见图表)。CASP衡量成功的一个标准是全局距离测试。通过比较算法预测出分子结构中原子的位置及其实际位置,按百分制给算法打分。阿尔法否2的平均得分为92.4。CASP的创始人、马里兰大学的生物学家约翰·穆尔特(John Moult)说,它的准确性与X射线晶体学等技术大致相当。
Until now, DeepMind was probably best known for its success in teaching computers to play games—particularly Go, a pastime of deceptively simple rules but fiendish strategy that had been a totem of AI researchers since the field began. In 2016 a DeepMind program called AlphaGo defeated Lee Sedol, one of the world’s best players. Superficially, this may seem of little consequence. But Dr Hassabis says that more similarities exist between protein-folding and Go than might, at first, appear.
以前,DeepMind最出名的可能是教计算机玩游戏,特别是下围棋。围棋看似规则简单,但策略极其复杂,自AI研究兴起以来一直是研究人员的一种图腾。2016年,DeepMind名为阿尔法狗(AlphaGo)的程序击败了世界顶级棋手之一李世石。从表面上看这似乎无关紧要。但哈萨比斯说,蛋白质折叠和围棋之间的相似之处可比乍看上去要多。 One is the impracticality of attacking either problem with computational brute force. There are thought to be around 10^170 legal arrangements of stones on a Go board. That is much greater than the number of atoms in the observable universe, and it is therefore far beyond the reach of any computer unless computational shortcuts can be devised.
一个相似之处是这两个问题都不能靠蛮力运算解决。据估计,围棋的棋局大约有10^170种。这个数字远大于可观察宇宙中的原子数,远远超出了任何计算机的运算能力,除非设计出计算捷径。
Proteins are even more complicated than Go. One estimate is that a reasonably complex protein could, in principle, take any of as many as 10^300 different shapes. The shape which it does eventually settle into is a result of a balance of various atom-scale forces that act within its amino-acid building blocks, between those building blocks, and between the building blocks and other, surrounding, molecules, particularly those of water. These are all matters of considerable complexity which are difficult to measure. It is therefore clear that, as with playing Go, the only way to perform the trick of predicting protein-folding is to look for shortcuts.
蛋白质比围棋还要复杂。一种估计是,一个不算太复杂的蛋白质原则上可以有多达10^300种不同形状。它最终的形状是在其氨基酸结构单元的内部、之间,以及氨基酸与周围其他分子(尤其是水分子)之间各种原子级作用力平衡的结果。这些过程都相当复杂而难以测量。因此很显然,和下围棋一样,预测蛋白质折叠的唯一方法就是寻找捷径。
The progress that computers have made on the problem over the years demonstrates that these shortcuts do exist. And it also turns out that even inexpert humans can learn such tricks by playing around. Dr Hassabis recalls being struck by the ability of human amateurs to achieve good results with FoldIt, a science-oriented video game launched in 2008 that invites its players to try folding proteins themselves, and which has generated a clutch of papers and discoveries.
多年来计算机在此课题上取得的进步表明捷径确实存在。而且事实证明,即使非专业人士也可以通过游戏来学习折叠蛋白质。哈萨比斯回忆说,业余爱好者玩科学电子游戏“叠它”(FoldIt)取得的佳绩曾让他倍感震惊,这款2008年推出的游戏邀请玩家尝试自己折叠蛋白质,催生了许多论文和发现。
Alpha-helix dogs 阿尔法螺旋狗 Getting players of FoldIt to explain exactly what they have been up to, though, is tricky. This is another parallel with Go. Rather than describing step by step what they are thinking, players of both games tend to talk in vaguer terms of “intuition” and “what feels right”. This is where the machine learning comes in. By feeding computers enough examples, they are able to learn and apply shortcuts and rules-of-thumb of the sort that human beings also exploit, but struggle to articulate. Sometimes, the machines come up with insights that surprise human experts. As Dr Moult observes, “In general, the detail of the backbone [the molecular scaffolding that joins amino acids together] is extraordinary. [AlphaFold 2] has decided that if you don’t get the details right, you won’t get the big things right. This is a school of thought that’s been around for some time, but I thought it wasn’t correct.”
但是,让“叠它”的玩家确切解释他们在做什么很难。这是与围棋的另一个相似之处。两种游戏的玩家都不会一步一步地描述他们的想法,而是用“直觉”和“觉得这样是对的”等模糊的说法表述。这就有了机器学习的用武之地。给计算机提供足够的示例,它们就可以学习和应用人类也会利用却说不清道不明的捷径和经验法则。有时,机器会得出让人类专家感到惊讶的见解。正如穆尔特所观察到的,“总的来说,主链(将氨基酸连接在一起的分子基架)的细节非常特别。(阿尔法否2)的结论是如果这些细节没弄对,那么大方向也会错。这种看法已经存在了一段时间,但以前我认为这是不对的。”
As an achievement in AI, AlphaFold 2 is not quite so far ahead of the field as was AlphaGo. Plenty of other research groups have applied machine learning to the protein-structure problem, and have seen encouraging progress. Exactly what DeepMind has done to seize the lead remains unclear, though the firm has promised a technical paper that will delve into the details. For now, John Jumper, the project’s leader, points out that machine learning is a box which contains a variety of tools, and says the team has abandoned the system it used to build the original AlphaFold in 2018, after it became clear that it had reached the limits of its ability.
作为AI的一项成就,阿尔法否2的领先优势远没有阿尔法狗那么大。其他许多研究小组也已经将机器学习应用于蛋白质结构问题,并且看到了令人鼓舞的进展。DeepMind具体做了些什么而取得领先地位仍不得而知,不过它承诺会发表一篇技术论文来深入探讨细节。目前,该项目的负责人约翰·姜普(John Jumper)指出机器学习是一个装有各种工具的盒子,他还说,在2018年用于打造最初版阿尔法否的系统显示能力达到极限后,他们就放弃了它。
The current version, says Dr Jumper, has more room to grow. He thinks space exists to boost the software’s accuracy still further. There are also, for now, things that remain beyond its reach, such as how structures built from several proteins are joined together.
现在的版本有更大的发展空间,姜普说。他认为有空间来进一步提高软件的准确性。到目前为止,也有一些事情是这个版本无法确知的,例如由几种蛋白质构建的结构是如何结合在一起的。
Moreover, as Ken Dill, a biologist at Stony Brook University in New York state, who is the author of a recent overview of the field, points out, what AlphaFold 2, its rivals and, indeed, techniques like X-ray crystallography discover are static structures. Action in biology comes, by contrast, from how molecules interact with each other. “It is”, he puts it, “a bit like someone asking how a car works, so you open the hood [bonnet] and take a picture and say, ‘There, that’s how it works!’” Useful, in other words, but not quite the entire story.
此外,正如纽约州立大学石溪分校的生物学家肯·迪尔(Ken Dill)在最近发表的一篇有关该领域的综述中所指出的,阿尔法否2、它的竞争对手,甚至还有X射线晶体学等技术所发现的都是静态结构。相反,生理活动源于分子之间的相互作用方式。他说:“这有点像有人问汽车的工作原理,你打开引擎盖拍了张照片说,‘喏,就是这样子的!’”换句话说,这也并非无用,但没完全说清楚。
Nonetheless—and depending on how DeepMind decides to license the technology—an ability to generate protein structures routinely in this way could have a big impact on the field. Around 180m amino-acid sequences are known to science. But only some 170,000 of them have had their structures determined. Dr Moult thinks that boosting this number could help screen drug candidates to see which are likely to bind well to a particular protein. It could be used to reanalyse existing drugs to see what else they might do. And it could boost synthetic biology, by speeding up the creation of human-designed proteins intended to catalyse chemical reactions.
但是,能常规化地以这种方式得出蛋白质的结构可能会对这个领域产生重大影响,当然这也要看DeepMind决定以何种方式授权使用这项技术。科学上已知的氨基酸序列有约1.8亿个,但其中只有约17万个的结构得以确定。穆尔特认为,确定更多的序列结构可以帮助筛选候选药物,看哪些药物可能与特定的蛋白质很好地结合。也可以对现有药物重新分析,看它们还有什么其他功效。还可以通过加快创造人工设计的用于催化化学反应的蛋白质,促进合成生物学的发展。
Some promising successes have, indeed, already happened. For example, AlphaFold 2 was able to predict the structures of several of the proteins used by the new coronavirus, including spike. As for Dr Birney, he says, “We’re definitely going to want to spend some time kicking the tyres. But when I first saw these results, I nearly fell off my chair.”
一些具有应用潜力的发现实际上已经出现了。例如,阿尔法否2能够预测刺突等几种被新冠病毒利用的蛋白质的结构。伯尼说:“我们肯定会要花一些时间做检验。但当我第一次看到这些结果时,我激动得差点从椅子上摔下来。”

经济学阅读

三、翻译园地-“大声地”用英语怎么说 

aloud, loudly, loud
(1) aloud常与动词read和think连用,表示“出声地”。
She has a very good pronunciation when she reads aloud.
她朗读时发音非常好。
A: What did you say? 你说什么?
B: Oh, nothing, I was just thinking aloud. 哦,没什么,我只是在自言自语。
(2) loudly和loud一样,用来指声音的音量。
When they are arguing, they talk so loudly that the people in the next flat can hear 
every word.
他们争论时,讲话声音那么大,隔壁房间里的人都能听清每一个词。
(3) loud在日常会话中常用在动词后,代替loudly,常一起用的动词是talk, speak, shout, laugh等。另外,loud常用在loud and clear短语中。
Don't talk so loud — you'll wake the whole street!
不要这么大声讲话——你会把整条街上的人都吵醒的!


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