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发表于 2005-12-18 09:46:43
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http://boinc.equn.com/rosetta/rah_welcome.htm
Welcome to the Rosetta@home distributed computing project and thank you for joining!
欢迎参与Rosetta@Home分布式计算
You will be helping us to solve one of the longest standing problems in molecular biology: the "protein folding" problem.
您的参与将帮助我们解决蛋白质折叠这个分子生物学难题.这需要海量的计算.
Proteins are the miniature machines that carry out almost all the important functions in your body. As with any machine, understanding how proteins work requires understanding what their structures are. It has been known for over 40 years that the structures of proteins are completely determined by their amino acid sequences, and we know the amino acid sequences of all proteins in the human genome thanks to the recently completed human genome project. However, until very recently, it has seemed nearly impossible to compute the structures of proteins from their amino acid sequences, and solving this problem has been something of a scientific "Holy Grail".
尽管蛋白质十分微小,但它们却构成了您的身体. 要了解蛋白质的工作原理就得了解它们的构成. 我们知道,蛋白质的氨基酸排列次序决定了它们的结构. 由于科学家们的探索,我们确定了人类基因的排列次序. 但是,我们几乎不可能从蛋白质的基因排序中准确分析或计算出他们的结构. 这个问题就像是科学界中的”圣杯”.
As you can read in the news releases and in Science magazine, we have in the past six months made significant progress and for the first time it appears possible to compute protein structures from their sequences. Success with this would have a huge impact on our understanding of how biology works, and even more importantly, could lead to new therapies and vaccines to help cure disease. The major stumbling block is the very large amount of computing time required to solve the problem.
您或许在最近的科学读物或新闻中已经得知,在最近的6个月里,我们在这个问题上取得了很大的成就. 也就是说, 我们可能通过蛋白质的排列次序准确地分析出他们的结构. 假如我们真的找到了分析的方法,那么这将是人类医学、生物学上的一个伟大成就. 更重要的是,人类由此就找到了预防和对付重大疾病的方法! 但是,解决这个问题需要进行长期大量的计算.
I can explain the computing problem with an analogy. Suppose you are a space explorer and have found a new planet, and have been told that what you have always been looking for lies at the bottom of the deepest valley on the surface of the planet. How do you find this lowest point? One possibility would be to land somewhere on the planet, and search from there. However, if the planet is very large, you are unlikely to have landed close enough to this deep valley to find it. For example, if you landed on earth, you are unlikely to land close enough to the shore of the Dead Sea to stumble across it during your exploration--you would most likely be on a different continent, perhaps exploring the Himalayas or the Sahara desert. But what if you had 10,000 dedicated explorers, who would each parachute down to a random position on the planet, search around for the lowest elevation point in the region they landed, and report back to you the elevation of the lowest point they found. Your chances of finding what you are looking for would be very much larger, and the more explorers you can send out, the greater the chance of success.
让我来解释一下这个项目的大致计算原理. 打个比方, 假如您被派作宇宙探险者,您需要找到指定星球表面的最低点. 那您怎么找呢? 一个可能是降落到那个星球上,然后从降落点开始搜寻. 但是您未必能准确找到最接近目标的地方降落. 又比如,假如您在地球上降落,很难说您一定就能降落在死海岸附近的地方. 您可能在喜马拉雅山脉或撒哈拉沙漠降落. 但如果您有10000个探测器呢? 情况将大为不同. 这些探测器随机降落,并且向您汇报他们各自所找到的海拔最低点. 那么,您找到真正最低点的可能性将会大大提高. 而且,您送出的探测器越多,成功的机率越大.
Now in our case, the space being searched is not the surface of a planet, but the set of all possible structures that a protein can have. There are a very large number of possible structures because there are over one hundred different places where the protein can bend or twist in different ways. Remarkably, despite the very large number of possibilities, proteins fold up into single, well defined structures which allow them to carry out their biological functions. The special property of these "folded" structures is that they have lower energy than any other structures the protein could adopt. So rather than searching for the lowest elevation location, we are searching for the lowest energy structure, but conceptually the problem is very similar to the example I gave in the preceding paragraph.
而我们现在所要寻找的不是太空中行星表面的最低点,而是所有可能构成蛋白质的结构. 可能构成蛋白质的结构很多. 因为蛋白质可以通过上百种不同的方式发生折叠. 但是不管它们怎样折叠, 只有当他们的结构符合”被允许的条件”时,才能发挥它们的生物职能. 而这些被折叠的蛋白质有一个明显特点,那就是:它们的耗能比其它蛋白质更低. 所以我们采取一种更有效的方法,那就是以最低能耗蛋白质作为寻找目标. 可惜这种方法所存在的问题与前文所提及的相类似. 那就是要搜索的范围太广了.
So you can think of what your computer will be doing in the following way. At the beginning of the calculation, it will parachute onto a randomly chosen region of the energy landscape, and then hunt for the lowest energy point in the neighborhood. At the end, it will send the lowest energy structure that it found back to our server, along with the energy of this structure. Our server will compare the energies of all the low energy structures found by all of the participating processors, and the lowest energy structure overall will be identified.
那您会问,我的电脑接下来会做什么? 计算的初始阶段,rosetta会在一块随即选择的能量带”降落”,然后在附近寻找能量最低点. 当您电脑上的”探测器”—rosetta在它附近找到了最低能耗的蛋白质结构时,会把结果传送回服务器. 服务器将会比较和检验所有送上来的结果,从而找到最低能耗的蛋白质结构.
Initially, we will take advantage of the fact that the lowest energy structures have already been determined for some proteins using complicated, expensive, and laborious experimental techniques I won't try to explain here. We will compare the lowest energy structure found overall to the experimentally determined structure, and see if they are the same. Once we have figured out how much we need to search (how many processors for how long) to be sure to find the lowest energy structure, we will use the approach to compute structures of important proteins with unknown structures. You will have helped to achieve this "Holy Grail" of biological research.
起初,我们会利用一些通过非常复杂的试验方法所找出来的蛋白质结构. 将计算出来的蛋白质结构和试验中找到的结构相比较. 如果它们是相同的话,那么我们就得出了准确找到最低能耗蛋白质结构的方法,我们会转向使用这条新的捷径来确定一些重要的,带有未知结构蛋白质的构成. 这项研究被誉为生物界中的”圣杯”. 而有了您的帮助,我们能更快地摘取它.
Now, if you have followed this illustration, you will realize that the ultimate solution--the lowest energy structure--will have been found by a single processor. This is like winning a lottery, since the space is so big and there are so many possibilities. Like a lottery, the more time your computer spends searching the more likely it will win. We will be keeping track of the lucky winning computer for each of the prediction problems, and the owner will get special notice and credits. See our Top Predictions page for more information.
您知道,这个最低能耗的蛋白质结构只能有一台机器找到. 这就像买彩票中奖一样,机率是很小的. 您的电脑投入的时间越多,成功的机率就越大. 我们将跟踪报道这些幸运电脑. 而电脑的拥有者也就是那位分布式计算者会得到特殊的积分和祝贺通告. 详见我们的 Top Predictions 页
So have fun, and tell all your friends and relations to join up--this is one of the most important open questions in science today that can potentially be solved by large scale distributed computing.
祝您”计算愉快”! 还请您告知您的朋友和亲戚关于我们和分布式计算的消息. 让更多的人加入进来. Rosetta@Home 是一项最重要的科学探索项目. 而这可以通过分布式计算来解决.
Thanks again for helping with our project!! 再次感谢您的参与!
David Baker
Professor of Biochemistry at the University of Washington
Howard Hughes Medical Institute investigator
华盛顿大学生物化学系教授 Howard Hughes医学协会调查员 David Baker先生 |
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发表于 2005-12-10 23:12:03
