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[已完成翻译] 开始翻译 Rosetta@home 官方站点

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发表于 2006-1-17 13:40:39 | 显示全部楼层
引用 ericfung 在 2006-1-17 13:18 时的帖子:
Docking在FightAIDS@Home那里有很详细的解释了,
http://fightaidsathome.scripps.edu/glossary.html#docking
-------------------------
Docking
A procedure where computer modeling is used to ...


谢谢!那就是跟21楼的解释差不多啦?~^_^
ps Rosetta有术语表吗?^_^

[ Last edited by Grё@thΙll on 2006-1-17 at 14:03 ]
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发表于 2006-1-18 19:09:13 | 显示全部楼层

http://boinc.equn.com/rosetta/rah_medical_relevance.htm

Disease Related Research
Rosetta有关疾病的研究
Comments from David Baker 来自David Baker的介绍
My research group is involved both in fundamental methods development research and in trying to fight disease more directly. Most of the information on this site focuses on basic research, but I thought you might be interested in hearing about some of the disease related work we are doing that you will be contributing to at Rosetta@home.
我们主要有两个研究目标:1.做有关发展基本预测方法的研究;2.尝试更直接更快捷地找到抵御重大疾病的方法.本站的内容大多都是介绍Rosetta的工作流程和基础研究的.但我想,您可能对现实一点的问题更感兴趣.比如Rosetta怎样找到有效抗击疾病的方法等.了解下文将有助您了解Rosetta的工作方向和它所发挥的作用.
Malaria: We are part of a collaborative project headed by Austin Burt at Imperial College in London that is one of the Gates Foundation "Grand Challenge Projects in Global Health". Malaria is caused by a parasite that spends part of its life cycle inside the mosquito, and is passed along to humans by mosquito bites. The idea behind the project is to make mosquitoes resistant to the parasite by eliminating genes required in the mosquito for the parasite to live. Our part of the project is to use our computer based design methods (ROSETTA) to engineer new enzymes that will specifically target and inactivate these genes.
对疟疾的研究是由英国Imperial大学的Austin Burt发起的"Grand Challenge Projects in Global Health"项目的研究板块之一.此项目是由Gates基金成立的.疟疾的引起来源于一种在蚊子体内寄生的虫子.这些可恶的寄生虫通过蚊子对我们的叮咬跑到了人类的血液里面.而这种寄生虫在蚊子体内的生存依赖的就是蚊子的其中某几条条基因.本项目的意义和目的就是要从蚊子的DNA里把那条基因”去掉”.通过Rosetta,我们可以得到一种新的酶来使这类为寄生虫所依靠的基因被”封存起来”,阻止他们发挥效能.
Anthrax: We are using ROSETTA to help John Collier's research group at Harvard build models of anthrax toxin that should contribute to the development of treatments. You can read the abstract of a paper describing some of this work at http://www.pnas.org/cgi/content/abstract/102/45/16409.
John Collier的科研小组正利用Rosetta来帮助建立炭疽毒素的模型.研究成果将捐献给公共医疗体系.有关于Rosetta在炭疽方面的研究进展的概述可在http://www.pnas.org/cgi/content/abstract/102/45/16409页面得知.
HIV: One of the reasons that HIV is such a deadly virus is that it has evolved to trick the immune system. We are collaborating with researchers in Seattle and at the NIH to try to develop a vaccine for HIV. Our role in this project is central--we are using ROSETTA to design small proteins that display the small number of critical regions of the HIV coat protein in a way that the immune system can easily recognize and generate antibodies to. Our goal is to create small stable protein vaccines that can be made very cheaply and shipped all over the world.
获得性人体免疫缺陷病毒(HIV或AIDS)为什么那么致命和骇人听闻?是因为它们能骗过人类的免疫系统,使人体彻底失去保护.在西雅图和NIH(美国国家卫生研究所),我们与研究员们正共同研制HIV的疫苗.我们利用Rosetta来设计一种蛋白质.从而找出HIV病毒表层蛋白质的结构.那样我们就可以找出对付HIV的方法了.本项目的目标是制造出一些功效稳定而且价格低廉的疫苗,然后把疫苗推广到全世界!
Other viruses: We have been collaborating with Pam Bjorkman's laboratory at Cal Tech to use the ROSETTA protein-protein docking methodology to build models of herpes simplex virus proteins in complex with human proteins.
其他的疾病研究项目通过Rosetta来制作相应病毒或细菌的蛋白质模型来找到对付这些疾病的有效方法.例如与Pam Bjorkman的实验室合作的研究员们通过Rosetta来构造疱疹病毒蛋白质的模型.通过疱疹病毒蛋白质与人类细胞蛋白质的复合体来做相关的研究.
Alzheimer's disease: Alzheimer's and many other diseases are likely to be caused by aberrant protein folding in which proteins form large aggregated structures called amyloids rather than folding up into their normal biologically active states. A big advance was made recently by David Eisenberg's research group at UCLA in solving the first structure of an amyloid. We are collaborating with their research group to use the structure to predict which parts of proteins are likely to form amyloids, which will be a first step to blocking amyloid formation and hopefully disease.
阿尔茨海默氏症的起因和很多疾病的一样都是由淀粉蛋白质的异常折叠引起的. 最近,David Eisenberg的研究小组在这一问题研究上取得了很大的进展.他们成功地解析了一个淀粉体的结构.我们正与他们合作,预测与淀粉体蛋白质结构相似的其他蛋白质.这将是我们向蛋白质编队化迈下的第一步.
Cancer: Cancer can be caused by mutations in key genes that disrupt normal cellular control processes. We are developing methods for cutting DNA at specific sites in the genome, and we will be targeting sites that are implicated in cancer. After these sites are cut, they should be repaired by the cell using a second, unmutated copy of the gene and the cell should no longer be cancerous. This is a very specific form of gene therapy that, if successful, will circumvent one the main objections to current gene therapy methods; namely, current methods insert the unmutated copy of a gene randomly into the genome, and if the insertion point happens to be near an oncogene, the gene therapy will cure one disease but cause another. Because our methods will target specific sites instead of random sites, they should avoid this pitfall.
癌症可能由某些关键基因的突变引起,而这直接破坏了动物体细胞的正常运作.我们正试图寻找能从DNA中准确找到引发基因突变的诱因.找到这些基因片段后把它们除掉.细胞会自动”把备份基因信息补上去”,完成修复工作.这些细胞便不再是癌细胞了.这种基因疗法是非常非常精确的.如果成功,现行的所有基因疗法将”报废”.现在的基因疗法是将正常的基因备份随机的加入到基因组中.如果切入点不对,疾病仍然可能发生.通过Rosetta研究出来的方法可以避免这一弊端.我们可以将正常的基因片段放入并取代病灶片断.
Prostate Cancer: The androgen receptor (AR) binds testosterone and is responsible for normal male development. When the AR becomes hypersensitive to testosterone, prostate cancer is the result. The current treatment for prostate cancer, called "hormone therapy", involves lowering the amount of testosterone available (sometimes by castration). Many malignant tumors are resistant to this therapy, however, so we are applying our protein design methodology to find different ways to inhibit the AR and to treat prostate cancer. Specifically, we are trying to design proteins that will disable the AR even in the presence of testosterone. We are doing this by designing proteins that will prevent the AR from entering the nucleus of the cell (which is where it does its dirty work), and also preventing it from binding DNA and activating tumor-specific genes even if it does get into the nucleus.
男性荷尔蒙受体(AR)可以对睾丸激素进行约束,且对男性的生长和发展起着关键作用.一旦AR对睾丸激素变得非常敏感,那么前列腺细胞就有可能癌变.现行的前列腺癌疗法是所谓的”激素疗法”.其目的是降低睾丸激素的效能(有时候可能通过阉割!).这种疗法对很多恶性的肿瘤不起太大的效果.然而我们可以通过设计蛋白质来抑制AR和治疗前列腺癌.很明显,我们的目的就是要使AR对睾丸激素的敏感程度降低,这将可以通过设计新的蛋白质来完成.我们正尝试设计一种可以有效防止AR进入细胞核.即便进入了,我们也可以通过新设计出来的蛋白质来阻止癌变特异基因的启动.
The above projects are not currently running on BOINC because we don't yet have an efficient queuing system which lets people submit jobs easily, but look for them soon! Also, rest assured that the structure prediction calculations currently running on your computers will have direct bearing on treating disease. There is a three-fold explanation for this direct relationship between structure prediction and disease treatment:
我们还没有一个使参与者能有秩序并便捷地申请工作的系统,所以以上的项目都还没能在BOINC上运作.不过这只是暂时的,很快这些项目都会加入BOINC的大家庭中来.当然,现在您电脑上运行所的Rosetta对于防治疾病来说也是具有重大意义的.以下三点对蛋白质结构预测与抵御重大疾病之间的紧密关系做了详细的阐释.
1.        Structure prediction and protein design are closely related.
1.蛋白质设计和蛋白质结构预测是紧密相关的.
Improvements in structure prediction lead to improvements in protein design, which in turn can be directly translated into making new enzymes, vaccines, etc. For more information on protein design you might be interested in looking at the review we recently wrote in science which is available at our home page (http://depts.washington.edu/bakerpg).
蛋白质预测技术的进步引领着蛋白质设计技术的前进.蛋白质的设计以蛋白质结构的预测技术作为基础,在此基础上可以设计出新的酶和疫苗等等.更多您可能感兴趣的咨询请参阅我们的主页http://depts.washington.edu/bakerpg,这里面有我们最新得出的科技成就.
Schueler-Furman, O., Wang, C., Bradley, P., Misura, K., Baker, D. (2005). Progress in modeling of protein structures and interactions Science 310, 638-642.
2.        Structure prediction identifies targets for new drugs.
2.蛋白质结构预测,研发新药物.
When we predict structures for proteins in the human genome on a large scale, we learn about the functions of many proteins, which will help in understanding how cells work and how disease occurs. More directly, we will be able to identify many new potential drug targets for which small molecule inhibitors (drugs) can be designed. To put this in context, one major road-block to developing new treatments for human disease is identifying new "drugable" protein targets. Most new drugs these days interact with the same targets as the old drugs, so these drugs lead to only small improvements in disease treatment. Structure prediction helps us identify new drug targets, and so will help us find innovative, perhaps even breakthrough, treatments for disease.
当我们在广阔的蛋白质天地中预测人类基因蛋白质结构时,我们对许多其他蛋白质的功能有新的了解.这帮助我们了解细胞的工作原理和疾病时如何发生的.更直接的说,我们将可能找到一些分子抑制剂.联系上下文,您应得知,我们研发对付重大疾病的药物的路上,当务之急就是要找出一些”治疗性的”蛋白质样本.目前,大多数的新药物都是由通过与旧药物一样的蛋白质样本相互作用而成的.预测蛋白质结构有助于我们发现新的更有效的药物.通过蛋白质结构预测和设计技术来医治疾病将会是一种改革,一种突破!
3.        Structure prediction allows us to use "rational design" to create new drugs.
3.掌握预测蛋白质结构变化的逻辑规律使我们能创造新药物.
If we know the structure of a protein, we can determine its functional sites, and specifically target those sites to be inactivated by a new drug. Calculation of whether a small molecule (drug) will bind to and inactivate a protein target is similar in many ways to the structure prediction calculations we are doing here--it is basically a problem of finding the lowest energy structure of the protein plus drug system--and we have recently developed a new module in ROSETTA to do this docking problem. Results are very promising, and in the near future your machines will likely be running drug docking calculations along with the vaccine and therapeutic protein design projects described above, in addition to the protein folding calculations you are doing now.
如果我们对一种蛋白质的结构了解透彻了的话,我们可以确定它每一个位点的功能,并能通过新药物来阻止一些不利于我们的功能的正常启用.在许多方面,对新药物的计算和对蛋白质结构预测的计算使基本相同的.不过这只是基础的.计算目的就是要在蛋白质+药物系统中找到能量最低的蛋白质结构.而且最近我们在Rosetta也添加了一个新的模块来完成蛋白质停靠实验.结果使十分令人鼓舞的.在不远的将来,您的Rosetta除了做关于蛋白质结构预测的计算外,还可能帮助我们研发新的药物和疫苗呢!

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发表于 2006-2-16 13:13:07 | 显示全部楼层
http://boinc.equn.com/rosetta/rah_welcome.htm

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”...

随即->随机

另外,“耗能”这个词我不清楚是不是专业的术语,感觉上直接译成“能量”什么的更好些?
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 楼主| 发表于 2006-2-16 13:42:56 | 显示全部楼层
我这就改去,前面几帖里没有转到服务器上的几个页面,我等会就转上去。
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发表于 2006-2-16 14:43:20 | 显示全部楼层
大仙动作好快!

我刚把18楼那篇更新了一下,除了那句红色的,其它的都翻译了,还修改了一些原来的翻译,有不对的地方,大家帮忙修改:)

[ Last edited by Youth on 2006-2-16 at 14:47 ]
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 楼主| 发表于 2006-2-16 15:44:24 | 显示全部楼层
麻烦 Youth 斑竹把修改过的地方加粗一下,在您编辑修改之前,我已经转到页面中了,上传后服务器缓存还没更新。我再把您修改过的地方再修改一下。
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发表于 2006-2-16 15:55:11 | 显示全部楼层
凭记忆标了一下,另外,原来没有翻译的那一段中有个地方我写错了:CAPSI->CAPRI。
辛苦大仙了:)
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发表于 2006-2-17 11:47:30 | 显示全部楼层

http://boinc.equn.com/rosetta/rah_graphics.htm

Quick guide to Rosetta and its graphics

Rosetta及其屏保的简介

About Rosetta

关于Rosetta

One of the major goals of Rosetta is to predict the shapes that proteins fold up into in nature. Proteins are linear polymer molecules made up of amino acid monomers and are often refered to as "chains." Amino acids can be considered as the "links" in a protein "chain". Here is a simple analogy. When considering a metal chain, it can have many different shapes depending on the forces exerted upon it. For example, if you pull it's ends, the chain will extend to a straight line and if you drop it on the floor, it will take on a unique shape. Unlike metal chains that are made of identical links, proteins are made of 20 different amino acids that each have their own unique properties (different shapes, and attractive and repulsive forces, for example), and in combination, the amino acids exert forces on the chain to make it take on a specific shape, which we call a "fold." The order in which the amino acids are linked determines the protein's fold. There are many kinds of proteins that vary in the number and order of their amino acids.

Rosetta的主要目标之一就是对蛋白质的折叠进行预测。蛋白质是由氨基酸组成的线性聚合物分子,常被称之为“链”。氨基酸可以看作是蛋白质“链”上的“节”。这只是一个简单的类比。先来看一条金属链,我们可以任意改变它的形状。如果拉住两边,金属链会变成直线;如果扔在地上,它会随意地变成一个形状。金属链中的每一节都是相同的,而蛋白质却是由二十多种属性(形状,吸引和排斥的作用力等等)各不相同的氨基酸组成的,组合在一起后,各个氨基酸将会相互作用并使得蛋白质链呈现某个特定的形状,我们称之为“折叠”。蛋白质的折叠是由其链中氨基酸的连接顺序所决定的。蛋白质的种类有很多,包含的氨基酸的数量及次序各不相同。

To predict the shape that a particular protein adopts in nature, what we are really trying to do is find the fold with the lowest energy. The energy is determined by a number of factors. For example, some amino acids are attracted to each other so when they are close in space, their interaction provides a favorable contribution to the energy. Rosetta's strategy for finding low energy shapes looks like this:

要预测特定蛋白质的形状,我们其实是在寻找具有最低能量的折叠。能量是由很多因素决定的。例如有些氨基酸会由于互相吸引而在空间中靠得近,它们之间的相互作用将导致总体能量的降低。Rosetta寻找最低能量形状的过程大致如下:

Start with a fully unfolded chain (like a metal chain with it's ends pulled).
Move a part of the chain to create a new shape.
Calculate the energy of the new shape.
Accept or reject the move depending on the change in energy.
Repeat 2 through 4 until every part of the chain has been moved a lot of times.
We call this a trajectory. The end result of a trajectory is a predicted structure. Rosetta keeps track of the lowest energy shape found in each trajectory. Each trajectory is unique, because the attempted moves are determined by a random number. They do not always find the same low energy shape because there are so many possibilities.

从没有任何折叠的链开始(就像一条被拉直的金属链)
移动链中的一部分,产生一个新的形状
计算新形状的能量
根据能量上的变化来决定是否接受这次的移动(否则就放弃)
重复2到4的步骤直到链中每一部分都移动了足够多的次数
我们称上面的过程为一条轨迹,每条轨迹的最终结果就是预测出来的一个结构。Rosetta会保存每条轨迹中找到的最低能量形状。每条轨迹都是独一无二的,因此每次尝试的移动都是随机决定的。过程中的可能性是如此之多以至于不可能找到同样的最低能量形态。

A trajectory may consist of two stages. The first stage uses a simplified representation of amino acids which allows us to try many different possible shapes rapidly. This stage is regarded as a low resolution search and on the screen saver you will see the protein chain jumping around a lot. In the second stage, Rosetta uses a full representation of amino acids. This stage is refered to as "relaxation." Instead of moving around a lot, the protein tries smaller changes in an attempt to move the amino acids to their correct arrangment. This stage is regarded as a high resolution search and on the screen saver, you will see the protein chain jiggle around a little. Rosetta can do the first stage in a few minutes on a modern computer. The second stage takes longer because of the increased complexity when considering the full representation (all atoms) of amino acids.

每条轨迹中都包含两个阶段。第一个阶段中使用简化表示的氨基酸序列以便我们可以快速地尝试各种可能性。本阶段是低分辨率的搜索,在屏保上你会看到蛋白质链大幅度的改变形状。在第二个阶段,Rosetta使用完全表示的氨基酸序列。这个阶段称之为“弛豫”。蛋白质链不再大幅度移动,为了找到正确的氨基酸排布,它只会尝试小幅度的移动。本阶段是高分辨率的搜索,在屏保上你只会看到蛋白质链的轻微摆动。在一台主流的计算机上,Rosetta完成第一阶段仅需要几分钟,而第二阶段因为完全表示的氨基酸序列导致的复杂性,将需要花费更长的时间。

Your computer typically generates 5-20 of these trajectories (per work unit) and then sends us back the lowest energy shape seen in each one. We then look at all of the low energy shapes, generated by all of your computers, to find the very lowest ones. This becomes our prediction for the fold of that protein.

对于每一个任务包,你的计算机将要包含5到20次的轨迹,然后在完成后将发送给我们每条轨迹中的最低能量形状。我们将检查所有用户计算得到的低能量形状以找到最低的能量形态。这也就是我们对该蛋白质折叠的预测。

Screen Saver
The screen saver shows the progress of each trajectory while it is happening:
There are 4 boxes showing the shape of the protein chain.
"Searching..." shows the moves that Rosetta is trying to make to the chain. You can see the shape of the chain by following the rainbow colors from blue to red.  
"Accepted" shows the most recently accepted move.  
"Low Energy" shows the lowest energy shape seen in the current trajectory.  
"Native" shows the experimentally determined true shape, if known.  

屏保
屏保会显示出每条轨迹正在进行时的进度:
有四个框将显示蛋白质链的形状
“Searching...”显示的是当前蛋白质链正在尝试的移动。你可能看到链的形状以及由蓝到红的彩虹颜色。
“Accepted”显示最近被接受的移动。
“Low Enery”显示的是当前轨迹中的最低能量形状。
“Native”显示的是在实验中得到的真实形状(如果已知的话)。

There are also two graphs and a plot that track the energy and rmsd of each accepted move.
"Accepted Energy" is a graph showing the energy of each accepted move in this trajectory. (x-axis is progress in the trajectory, y-axis is energy.)  
"RMSD" shows how close the currently accepted structure is to the right answer. (x-axis is RMSD, y-axis is progress.)  
The final box, in the lower right hand corner, plots the energy and RMSD of each accepted move. These are the same kind of plots shown on the top predictions page. Except now you are seeing them for every *accepted* move during the trajectory. The plots on the top predictions page are made up of only low energy folds from each trajectory.  

还有两条曲线和一张图即时地显示每次被接受的移动的能量及RMSD。
“Accepted Energy”显示的是当前轨迹中每次被接受的移动的能量。(横轴为当前轨迹的进度,纵轴为能量。)
“RMSD”表示时当前被接受的结构与真实结构的差距。(横轴为RMSD,纵轴为进度。)
最后一个在屏保右边中间的方框,显示的是每次被接受的移动的能量以及RMSD。这张图和网站上“Top Predictions”页面显示的图相类似。不同的是你在屏保里看到的是每条轨迹中每次“被接受”的移动的情况。而在“Top Predictions”页面仅显示每条轨迹的最低能量折叠。

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 楼主| 发表于 2006-2-17 13:19:57 | 显示全部楼层
楼上的 http://boinc.equn.com/rosetta/rah_graphics.htm 页面已上传,另外,主页 http://boinc.equn.com/rosetta/index.htm 也修改过了了,旧新闻全部转入存档新闻页面 http://boinc.equn.com/rosetta/old_news.htm 内。

另外,官方新加了一个页面:http://boinc.equn.com/rosetta/rah_science_faq.htm 新增的相关背景科学知识问答。

另外,目前急待翻译的页面如下:
技术新闻:http://boinc.equn.com/rosetta/rah_technical_news.htm
科学新闻:http://boinc.equn.com/rosetta/rah_science_news.htm

上面的几个页面翻译时建议直接看官方的对应页面(将 htm 改为 php,将 equn.com 改为 bakerlab.org 就是的了),我们服务器缓存好厉害啊,我传上去几天了的页面到现在还是老样子,怎么刷新、清空 IE 临时文件夹都不行。
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发表于 2006-2-17 19:29:48 | 显示全部楼层
我也经常碰到页面缓存的问题,不过一般按f5刷新就好了

技术和科学新闻页面,翻译了后就还是贴在rosetta的版面吧,大家都能看看:)
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发表于 2006-2-18 13:32:48 | 显示全部楼层
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发表于 2006-2-18 15:53:08 | 显示全部楼层

http://boinc.bakerlab.org/rosetta/rah_science_faq.php

Rosetta@home Science FAQ - by Vanita Sood

Rosetta@home 常见科学问答 - 由Vanita Sood编写

What is Rosetta?
Rosetta is a protein structure prediction and design program.

什么是Rosetta?
Rosettta是一个蛋白质结构预测及设计软件。

What is a protein?
A protein is a polymer of amino acids that is encoded by a gene.

什么是蛋白质?
蛋白质是由氨基酸组成的聚合物,由基因进行编码。

What are amino acids?
Amino acids are chemical moieties that form the basic building blocks of proteins. There are 20 different amino acids that are specified by the genetic code. These 20 amino acids fall into different groups based on their chemical properties: acidic or alkaline, hydrophilic (water-loving) or hydrophobic (greasy).

什么是氨基酸?
氨基酸是组成蛋白质的化学根。总共有20多种有特定基因的氨基酸。这20多种氨基酸依据属性不同可分成不同的类别:酸性或是碱性,亲水或是亲油。

What do proteins do?
Proteins perform many essential functions in the cells of living organisms. They replicate and maintain the genome (DNA), they help cells grow and divide, and stop them from growing too much, they give a cell its identity (eg liver, neuron, pancreatic, etc.), they help cells communicate with each other. Proteins, when mutated or when affected by toxins can also cause disease, such as cancer or alzheimer's. Bacterial and viral proteins can hijack a cell and kill it. In short, proteins do everything.

蛋白质有什么用途?
生物体中细胞的基本功能都是由蛋白质完成的。它们通过DNA对基因进行复制和保持,它们帮助细胞生长和分裂,并防止细胞生长过多,它们赋予细胞特定的身份(比如肝脏、神经元、胰腺等等),它们也帮助细胞间的交换。当蛋白质发生变异或者被毒素感染,就会引发诸如癌症或阿尔茨海默氏痴呆等疾病。细菌或病毒的蛋白质会攻击并杀死细胞。总之,蛋白质可以做任何事情:)

How do proteins perform all their different functions?
Each protein folds into a unique 3-dimensional shape, or structure. This structure specifies the function of the protein. For example, a protein that breaks down glucose so the cell can use the energy stored in the sugar, will have a shape that recognizes the glucose and binds to it (like a lock and key). It will have chemically reactive amino acids that will react with the glucose and break it down, to release the energy.

蛋白质如何完成所有这些各不相同的功能?
每种蛋白质都会折叠成独一无二的三维形状或结构。这个结构决定了蛋白质的功能。比如用来打破葡萄糖以便细胞可以从中吸取能量的蛋白质将会呈现一个能识别并附着在葡萄糖上面(就像锁和钥匙)的形状。它将具有能够和葡萄糖相互作用并打破它以释放能量的氨基酸。

Why do proteins fold into unique structures?
It's long been recognized that most for most proteins the native state is at a thermodynamic minimum. In English, that means the unique shape of a protein is the most stable state it can adopt. Picture a ball in a funnel - the ball will always roll down to the bottom of the funnel, because that is the most stable state.

为什么蛋白质会折叠成独一无二的结构?
很早人们就已经认识到自然界中绝大多数的蛋白质都是热力学最小的。通俗的说,蛋白质的形状是所有它能呈现的形状中最稳定的。想像一个漏斗里面的球,它总是会滚回漏斗的底部,因为那是最稳定的状态。

What forces determine the unique native (most stable) structure of a protein?
The sequence of amino acids is sufficient to determine the native state of a protein. By virtue of their different chemical properties, some amino acids are attracted to each other (for example, oppositely charged amino acids) and so will associate; other amino acids will try to avoid water (because they are greasy) and so will drive the protein into a compact shape that excludes water from contacting most of the amino acids that "hide" in the core of this compacted protein.

是什么作用力决定了蛋白质的独一无二的(最稳定的)自然结构?
蛋白质中氨基酸的序列就决定了它的自然状态。由于氨基酸各不相同的化学属性,有些氨基酸会因为互相吸引(比如电荷相反的氨基酸)会连接在一起;还有些氨基酸因为不亲水(因为它们是油性的),它们就会驱使蛋白质形成一个紧凑的形状,以至于不让水碰到藏在蛋白质核心中的氨基酸。

Why is it so difficult to determine the native structure of a protein?
Even small proteins can consist of 100 amino acids. The number of potential conformations available to even such a (relatively) small protein is astronomical, because there are so many degrees of freedom. To calculate the energy of every possible state (so we can figure out which state is the most stable) is a computationally intractable problem. The problem grows exponentially with the size of a protein. Some human proteins can be huge (1000 amino acids).

为什么很难决定蛋白质的自然结构?
即使是很小的蛋白质也包含100多个氨基酸。它所可能形成的构造的数目也将是一个天文数字,因为其中的自由度相当之多。要计算每一个可能状态的能量(以便我们可以找出哪一个是最稳定的)也是一个很难处理的。这个问题的复杂度与蛋白质的大小呈指数对应关系。而一些人类的蛋白质更是巨大(由上千个氨基酸组成)。

So how does Rosetta approach this problem?
The rosetta philosophy is to use both an understanding of the physical chemical properties different types of amino acid interactions, and a knowledge of what local conformations are probable for short stretches of amino acids within a protein to adopt, to limit the search space, and to evaluate the energy of different possible conformations. By sampling enough conformations, Rosetta can find the lowest energy, most stable native structure of a protein.

那么Rosetta是怎么试图解决这个问题的?
Rosetta的理论基础,一是不同类型氨基酸间相互作用的物理及化学属性,二是对于每段氨基酸分支来说怎样的局部构造是可以接受的。如此就可以限制搜寻的范围,并评估各种可能构造的能量。只要对足够多的构造进行采样,Rosetta就能找到蛋白质的能量最低、最稳定的自然结构。

Why is distributed computing required for structure prediction by Rosetta?
In many cases where the native structure of a protein is already known, we have noticed that Rosetta's energy function can recognize the native state as more stable than any other sampled state. When starting from a random conformation, however, we've observed that the native state is never sampled. By applying more computing power to the problem, we can sample many more conformations, and try different search strategies to see which is the most effective.

为什么Rosetta的结构预测需要通过分布式计算来完成?
在许多蛋白质的自然结构已知的情况下,我们注意到Rosetta的能量函数能够证明自然状态比其它任何采样状态都要更为稳定。我们也注意到在随机构造时从来没有采样到过自然状态。因此如果能够提供给这个问题更多的计算资源,我们就能进行更多的采样并尝试不同的搜寻策略以找到最有效的方法。

How will Rosetta@home benefit medical science?
Please see our Disease Related Research page for information on how Rosetta is being applied to medical problems.

Rosetta@home会对医学研究有帮助吗?
请看我们网站上的“疾病相关的研究”部分以了解Rosetta是如何应用于医学问题的。

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参与人数 2基本分 +90 维基拼图 +70 收起 理由
BiscuiT + 90 + 35
霊烏路 空 + 35

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发表于 2006-2-18 22:49:30 | 显示全部楼层
技术新闻贴已开:
http://www.equn.com/forum/viewthread.php?tid=11916

[ Last edited by Youth on 2006-2-19 at 10:04 ]
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发表于 2006-2-19 12:19:53 | 显示全部楼层
Youth大哥辛苦咯~^_^
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 楼主| 发表于 2006-3-9 10:56:01 | 显示全部楼层
上面三个页面已经上传,感谢 Youth 大哥翻译!

看这里:
科学问答 http://boinc.equn.com/rosetta/rah_science_faq.htm
科学新闻 http://boinc.equn.com/rosetta/rah_science_news.htm
技术新闻 http://boinc.equn.com/rosetta/rah_technical_news.htm

因为服务器缓存的原因,所以可能仍然需要多刷新几次才能看到中文页面。

不好意思,一直都在忙毕业设计的开题报告和四万字符的专业文献翻译,这边的工作到现在才转到服务器上,辛苦 Youth 大哥了!
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