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[独立平台] [生命科学类] Folding@Home

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发表于 2014-3-19 07:58:39 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-19 09:15 编辑

Working to fix the shortage of GPU WUs
March 18, 2014 by Vijay Pande ·

We’ve had a large influx of GPU clients, including a big donation of time from a corporate partner, and we’ve run a bit low on GPUs late last night (pacific time).  The team got new WUs going last night and we expect they should be online shortly.
While this shortage is naturally annoying for donors (hopefully just briefly annoying, with the new WUs coming online soon), there is an interesting upside to this––we’ve been really blown away with all the GPU resources donors have been running and look forward to the exciting research we can accomplish with this great outpouring of support.
大意:GPU客户端增速过快,导致‘地主家也没有余粮’了。昨晚我们紧急上传了一批新任务,希望这能缓解任务紧张的问题。


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 楼主| 发表于 2014-3-20 10:00:11 | 显示全部楼层
The release of our latest Folding@home desktop clientMarch 19, 2014 by Mark Piercy ·
Here is a guest post by Joseph Coffland, Folding@home Developer:
We are very happy to announce the release of our latest Folding@home desktop client. This version sports a new streamlined Web interface as well as numerous improvements to the user experience and folding performance. Both our internal and beta testing teams have worked hard to ensure that this is one of the best tested and most stable Folding@home clients to date.
The new Web interface was designed to be easier than ever to use. It now displays your user and team points and has a very clear start and stop button and a power slider bar which allows you to quickly control how much of your computing resources are contributed. Power users can continue to use the FAHControl advanced interface which enables monitoring and control of entire folding farms.
Of course Folding@home would not be possible if not for its contributors. The Folding@home network currently consists of about a quarter of a million active computers and is nearing a top speed of 40 PetaFLOPs. That is faster than any scientific computing system in the world. Although these achievements are impressive, we are aiming to push the envelope even further with innovations such as the recently released NaCl folding client which allows you to run Folding@home in the Google Chrome browserwithout installing any additional software. Upcoming innovations in this area will make it possible to rapidly grow your folding teams using social media.
To achieve both the technological and scientific goals we’ve aimed to make this client easier to use and more reliable than ever so you can feel confident about encouraging others to install Folding@home. If you do run into problems our ears are open. Join the discussion atfoldingforum.org where numerous Folding@home experts are ready to help. Happy folding!
Joseph Coffland
Folding@home Developer
Cauldron Development LLC
大意:发布7.4.4新版客户端。目前FAH大概有1百万活跃计算机,峰值计算速度达40P。新版谷歌浏览器的NaCl也在开发测试中,欢迎大家使用。


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 楼主| 发表于 2014-3-22 10:48:35 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-22 10:49 编辑

Major Milestone for FAH: breaking the 40-PetaFLOP barrier
March 21, 2014 by Vijay Pande ·
We’d like to thank and congratulate all of our donors for helping us break the 40-PetaFLOP barrier.  We’ve been working hard to make it easier to run the client and also making more of a push to get the word out about what FAH has done and what it’s doing.   It’s great to see this response.

We’re also grateful to some large corporate donors.  At the moment, they would like to stay anonymous, but we are expecting to make an announcement about their participation some time in the future.  We’ve also been working on new directions for FAH, with some of them recently released (such as the NaCl client) and others still in the works.

Finally, I’d like to note that we have a strategic plan for the types of new calculations we can do at the 100 PetaFLOP and 1000 PetaFLOP scale.  When/if we reach those levels, we’re excited to roll out those new, even more ambitious projects.
大意:
重大里程碑:FAH计算速度突破40P
感谢大家一直以来的无私奉献,也感谢某些大公司的贡献(译注:合作公司要求匿名,个人推测是不作恶的谷歌)。将来我们会继续开发新的客户端(比如NaCl客户端)。
最后,我们有一个战略性的大计划准备实施,它将使FAH的计算速度达到100~1000P的级别。让我们拭目以待吧。(译注:难道是搞垮比特币,然后推FAH币,把矿工勾引过来。呵呵!)

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发表于 2014-3-23 10:19:31 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-23 22:14 编辑

New team member: meet Jingcheng Wu
March 22, 2014 by Vijay Pande ·

Ms. Jingcheng Wu is a new team member, whose work is to help improve communications between FAH and its donors, especially explaining (in a non-technical way) what FAH has been able to do.   Ms. Wu was born and raised in China. She moved to the US as an exchange student at age 16 and has been living in numerous parts across the US. She is a past medical student with experience in a wide range of fields such as scientific research, healthcare, education, international business, media, journalism, retail, investment banking, and performing arts with proven success. She graduated Summa Cum Laude with a BS in chemistry from the George Washington University. Her multi-cultural background, interest in medicine and passion to make a positive influence drove her to serve as an intern in the Pande group and contribute to their mission. She is currently living on Stanford University campus.
大意:
欢迎团队新成员Jingcheng Wu
吴小姐主要负责和志愿者沟通(类似官方发言人的角色)。她自小在中国长大。16岁以交换学生身份来到美帝,至今已在许多州生活过。她是医学院的学生,目前住在斯坦福大学,兴趣爱好广泛,包括:科研、保健护理、教育、国际贸易、媒体、记者、销售、投行、艺术表演方面也有天赋。她以优异的成绩毕业于华盛顿大学。她多元的文化背景,对医学的兴趣和热情一定能使她胜任此职。

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发表于 2014-3-24 07:45:48 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-24 22:03 编辑

A discussion of recent FAH work on cancer: A brief overview
March 23, 2014 by Vijay Pande ·

Part I: A Brief Overview of The Study for The General Public
Guest post from Jingcheng Wu.
Cancer affects the general population in an extensive and intensive way. It accounts for 1 out of 4 deaths in the US.1 The global annual cancer cases are expected to rise to 22 million within the next two decades.2 Existing drugs used in chemotherapy on the market are not only ineffective 97% of the time,3,4 but also cause severe damage to the body as a whole due to the drugs’ high toxicity. We are all too familiar with the frightening adverse effects of chemotherapy such as hair-loss, holes in intestine, swelling of the body, feeling sick and tired, abnormal bleeding, to name a few. Many cancer patients choose death over going through the agony of chemotherapy by refusing the treatments.
The reason behind the severe toxicity of anti-cancer drugs lies in their low selectivity. Aiming at killing cancer cells, the drugs also massively destroy normal cells and impede the growth of new healthy cells. Thus come the tragic sufferings very often seen in the oncology wards. The current cancer drugs attempt to cure the patients while kill them at the same time. The solution, then, lies in finding a new way of targeting cancer cells with minimal harm to normal cells.
An ideal target for cancer treatments should be commonly found in a wide range of cancer cells and exists in far less amount in normal cells. Researchers found such a target, which is a protein called the “c-src.” Among half of the most common and most lethal human cancers, high c-src activity has been detected,5 and is found to be an essential element that causes cancer. Therefore, inhibiting c-src activity can contribute to a key breakthrough in cancer treatment, and will in turn benefit a potentially large world population that may be devastated by c-src dysfunction.
An ideal drug in this case should only inhibit c-src activity without interfering with normal cellular functions carried out by other proteins, including the ones that highly resemble c-src. As the famous quote from Art of War goes, “if you know both your enemy and yourself, you need not fear the results of a hundred battles. If you only know yourself but not the enemy, for every victory gained you will also suffer a defeat,” it is apparent that having a thorough understanding of c-src is key to designing the ideal drug, so that we can find out its potential “weak spots” and get a grip on them. It is an easy realization in theory but hard implementation in practice due to the immense computing power required to scrutinize the dynamic behavior and structure of c-src. All we had before were a few static snapshots of it. A detailed dynamic picture of c-src had not been available until Folding@Home6 came into the picture.
In order to do so, Folding@Home harness the unused computing power in personal electronic devices from volunteers worldwide. The combined computing power makes Folding@Home computing network the fastest super computer in the world.7 As a result of that collective effort, we simulated how each atom of c-src and surrounding water molecules moves, and we discovered a major “weak spot” of c-src that can be exploited to suppress c-src’s cancer-causing activities.
This weak spot exists exclusively in an intermediate stage in c-src’s transformation between inactive and active states. These details on c-src structural changes in various states are difficult to study; yet they turn out to be very important to developing a drug with the desired characteristics. (Refer to Part II for details.) The newly discovered “weak spot” is a unique structure on c-src that binds to certain drugs. It makes an ideal drug possible by allowing certain drugs to only influence c-src but nothing else, which minimizes damage to normal cells.
This study is a fantastic starting point and template for future studies to build onto. We can add more complex interactions to future simulations. Also, multiple structures on c-src can be used together for a single drug to exert high potency. In addition, the same techniques can be used to study other cancer-related proteins at atomic level, and in turn their subtle structural differences can be used for future drug design with high specificity and selectivity.
The Folding@Home mission is a beautiful example of the world uniting to combat a common enemy of humanity. The collective power of our research group and our donors push the frontiers of biotechnology to new limits and redefine the impossible.

References
  • American Cancer Society. 2014 Cancer Facts and Figures.
  • World Health Organization. Cancer Key Facts.
  • Cairns, J. The treatment of diseases and the war against cancer. Scientific America.253(5): 51-59 (1985).
  • Morgan, G., Wardy, R. & Bartonz, M. The contribution of cytotoxic chemotherapy to 5-year survival in adult malignancies. Clinical Oncology. 16: 549-560 (2004).
  • Dehm, S. & Bonham, K. SRC gene expression in human cancer: the role of transcriptional activation. Biochem. Cell Biol. 82 (2): 263–74 (2004).
  • Shirts, M. & Pande, V. Screen savers of the world unite!. Science. 290, 1903-1904 (2000).
  • PS3 network enters record books. BBC News. 02 Nov 2007. Web. 15 Mar 2014. <http://news.bbc.co.uk/2/hi/7074547.stm>.
大意:
简述FAH最近在癌症方面的研究工作
第一章
癌症的影响范围广大。在米国致死率可达1/4。在接下来的二十年内,全球癌症的年发病病例可达2200万。现有的化疗方法无效率高达97%。而且因为化学药物的毒性,副作用非常大,比如:脱发、肠穿孔、肢体浮肿、易病、易累、异常出血。简而言之,许多癌症患者宁愿死也不愿意接受化疗。究其原因,就是化疗药物的低靶向性,它不但会杀死癌细胞,也会杀死大量的正常细胞。
理想的抗癌药物应该是高靶向性的,只杀死大部分癌细胞,而对正常细胞没有影响。科学家们找到了这样一种蛋白质名为“c-src”,在半数癌细胞中它都会异常活跃。所以如果能找到专门抑制此蛋白的药物,成了攻克癌症的关键。
正如孙子兵法中所言:“知己知彼,百战不殆”。当前我们的首要任务就是研究‘c-src’的机理,找到它的弱点。为此我们利用强大的FAH平台,对c-src进行了全面计算研究。终于找了它的一个弱点。该弱点只存在于c-src的某个特定的中间态,因此只要能找到能与该弱点发生作用的蛋白质,就可以找到副作用极小的癌症的特效药。
这是一个好的起点,它为我们将来的研究指明了方向。接下来我们将对c-src进行更详细和复杂的模拟,如果通过模拟找到更多的弱点,可以提高药物的疗效。此外此项研究技术也可以用于其他与癌症相关的药物研发,因为他们的蛋白质结构大都有相似性。
欲知后事且听下回分解!

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 楼主| 发表于 2014-3-25 10:32:03 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-26 16:42 编辑

A discussion of recent FAH work on cancer: More technical detailsMarch 24, 2014 by Vijay Pande ·
Part II: An In-depth Description of The Study For Readers With a Biomedical Background
A guest post by Jingcheng Wu
More on c-src: what it is and its relations to cancer
C-src is short for c-src tyrosine kinase. Kinase is a type of enzyme that removes a part (phosphate group) of the molecule (ATP) that is required for every energy-expending process in the body, and attaches it to a specific amino acid (tyrosine, threonine or serine) of a protein (substrate). C-src belongs to a family of kinases called the Src tyrosine kinase.
C-src stimulates the pathways that induce cell growth, generate new blood vessels, prevent cell suicide, and give cells ability to migrate1,2 – all necessary to give rise to proliferation of invasive cancer cells. When there is a mutation to the gene that encodes c-src, mutant c-srcs produced could mimic the functions of the normal signal transduction c-srcs.3 When there is over or mis-expression of the said gene, too many normal c-srcs would be produced. In both cases, it is like stepping on a gas pedal of a car. Once the aforementioned abnormality is coupled with the loss of tumor suppressor gene functions,4 it is like additional loss of the brake of a car, and the car takes off and wreaks havoc.

What is the mechanism of action of existing drugs on the market?
C-src holds the ATP and substrate to close proximity at a small, pocket-like region in itself called the active site. To do so, the c-src active site needs certain shape and atomic arrangement to attract or repulse certain atoms of the ATP and substrate, so that the ATP and substrate fit into the active site of c-src.
Existing small molecule inhibitor drugs like Gleevec (for treating a chronic blood cancer, CML) contain substances structurally similar to ATP.5 Such substances fit into the c-src active site, and disrupt its interaction with actual ATPs.5

Why are existing drugs on the market toxic and ineffective?
The strategy mentioned above is quite effective but has the following flaws:
1: C-src is just one member of one family of kinases, and the active sites of kinases have similar structures because they perform similar functions. Moreover, many kinases do not involve in cancer development and just carry out their benign and necessary tasks in the cells. As a result, such drugs not only inhibit c-src in cancer cells, but also the activities of other friendly kinases in normal cells.
2: Frequent mutations take place in c-src, and some of the mutations can vary the structure of the active site.6 When that happens, ATP might still fit, but the structurally resembling drug substance might not fit.
To tackle this low selectivity and drug resistance problem, we looked at finding a novel drug-binding site on c-src.

What novel drug-binding site on c-src did we find and how does it work?
Proteins including c-src are chains of amino acids. The chains fold, twist, vibrate and change shapes (conformations) all the time due to their internal interactions among the amino acids7 and interactions with surrounding environment.
Inactive c-src undergoes a series of conformational changes to open up its active site allowing substrate binding, and subsequently becomes active. This dynamic process pauses at two metastable conformations before reaching the end active state.16 These two metastable conformations are the intermediates I1 and I2.16 We were able to find a drug-binding site to trap c-src in the I2 conformation so it cannot move on to adopt the fully active conformation. Such task can be achieved by using any drug that binds to a particular region of c-src that is not the active site (hence allosteric site).16 This approach has lower toxicity, fewer side effects and longer lasting action compare to existing treatments on the market.
For the drug-bond region to inhibit c-src conformational change from I2 to active state, it has to communicate with the active site. It doe so via interactions within and between those clustered components of c-src (domain) that behave relatively independently of the rest of the protein. Some of the domains can be many amino acids away from another (long range) yet they communicate with each other and act cooperatively.16 The close-neighboring amino acids also communicate with each other extensively, forming a local network to act in concert to unfold during activation.16

What have we done that had not been done before with c-src?
Before, researchers could tell the static structural differences between active and inactive c-srcs (the two end states). They also identified the metastable intermediates I1 and I2. Yet they did not know how long it took and through what sequence of events and conformational changes to get from one end state to the other. On the other hand, we captured the entire dynamic process. We found out for the first time that the time (106 s) it takes to tansit from inactive state to active state (activation) is about five times longer than the time (21 s) it takes for the reverse process (deactivation). We also discovered the thermodynamic and kinetic features of c-src activation for the first time. Moreover, we found out more details about the two intermediates. In addition, this is the first study that used Markov State Model (MSM) on large-scale complex conformational changes of enzymes, whereas previously MSM had been only used on simulating protein-folding re-arrangements.16 Last but not least, we found all the conformations of the “A loop” (a loop structure critical for c-src activation) of the protein chain, whereas other research groups found only one (which is the open conformation).8

What approaches did we use to conduct our research?
We combined MSM based, massively distributed computational method, statistical method and other algorithms and techniques to simulate c-src dynamic conformational changes at the atomic level.16 There are tens of thousands of atoms in the protein itself and in the surrounding water molecules to simulate. Atoms change their energetic, vibrational and kinetic properties within less than a trillionth of a second. The time it takes for c-src to transit from inactive state to active state is around one tenth of a thousandth of a second,16which is quite a long time on the atomic scale. The conformational transition also could follow numerous different paths to reach the end states.16 To simulate the entire c-src conformational landscape with surrounding water molecules at the atomic scale, while considering all the possible paths it could take over such a large timescale, it requires enormous computing power and vast amount of resources to carry out.
Luckily, we can break down the entirety of the computation into millions of small parts, and have donors from all over the world to each take one part and complete the computation on their personal electronic devices such as laptops, computers and Playstation3s. The previous studies had to employ simplification strategies that omitted key fine details on the kinetics of conformational transitions due to lack of computing power.

How would the methods outlined in this study potentially increase drug selectivity?
Some kinases, especially the ones in the same family with c-src, have amino acid sequences and structures highly resemble c-src. Such similarities pose difficulties of increasing drug selectivity. Fortunately, our detailed conformational landscapes help to distinguish subtle structural differences among proteins. For example, the Hck kinase (another member of the Src tyrosine kinase family), like c-src, also has two metastable intermediates I1 and I2.9 I1s of Hck and c-src are similar, but the “A loop” of Hck I2 is partially unfolded whereas the “A loop” of c-src I2 is fully unfolded.16 For drugs or fluorescent probes to bind to the novel allosteric binding site, the “A loop” has to be fully unfolded.16 Thus, the drugs or probes would only bind to c-src I2, not Hck I2. In other words, the subtle structural difference detected by our method between c-src I2 and Hck I2 allows drugs to selectively only inhibit c-src.

Other findings from the study
Diversity within intermediate states of c-src: When c-src goes through two intermediates (I1&I2) to switch between active and inactive states, the I1 does not always maintain exactly the same structure, and the same applies to I2. C-src I1 has a partially unfolded “A loop,”16which is like a loose ribbon flapping around in the wind. In addition, there are a few amino acids of the “A loop” that fluctuate and adopt not two, but multiple intermediate conformations along the activation pathway.16 When taking snapshots of the intermediate states at different times, the “A loop” would look different.

Slow rate of autophosphorylation: As mentioned earlier, c-src is a kinase that transfers a phosphate group (phosphorylation) from ATP to its substrate. It turns out that the substrate of c-src can be another c-src.16 The c-src can phosphorylate a member of its own kind, so this is called trans-autophosphorylation. Activating c-src is like turning on an old-fashioned light bulb. When you first turn on the switch, the light bulb flashes quickly alternating between on (active) and off (inactive) states. The way to lock the light bulb at the “on” state so that you get stable light source is for the c-src to either bind to its substrate (another c-src) or get trans-autophosphorylated by another c-src.10,16 It is important to note that for the substrate to be able to receive a phosphate group from ATP, it has to expose the site that the phosphate group attaches to.9 Initially when there are scarce active c-srcs floating around, the chance of encountering one and subsequently getting trans-autophosphorylated is small.16 As time goes on, more and more active c-srcs are available and the chance of encountering one is much greater, which speeds up the process exponentially.16 Our model predicts that the time evolution of active c-src population is sensitive to changes of concentrations of inactive c-src and briefly active c-src (not phosphorylated so not locked in the active state).

“A loop” has to unfold before C helix can change conformation: The different parts of c-src undergo conformational changes in specific orders. Also, certain parts of the protein like the C helix remains folded during activation,16 although this structure as a whole can rotate inward or outward.

Myristate-binding pocket in c-src could serve as another allosteric drug-binding site: A tyrosine kinase called c-ABL, which is a key component of a mutant fusion protein that causes a chronic blood cancer (CML), has similar fold as c-src.11 C-ABL and c-src both have an ATP binding pocket in the active site, a myristate-binding pocket not in the active site (thus allosteric) and an “A loop.” For c-ABL, binding of myristate to the myristate-binding pocket can be “felt” by the ATP binding pocket and the “A loop.” The ATP binding pocket and the “A loop” respond by changing their conformations, which lead to c-ABL activity supression.12 Due to the structural similarity of c-src with c-ABL, binding of a drug to the c-src myristate-binding site could produce a similar effect as observed in c-ABL.16

Future outlooks based on this study
Future studies on c-src can include its two regulatory domains (SH2, SH3) and its locked active state (trans-autophosphorylated state) in the simulations.16
Furthermore, the discovery of the new allosteric inhibitor drug-binding site can be potentially used simultaneously with the ATP binding pocket of the active site13 as binding sites for a group of drugs called “fragment-based inhibitors.”14 Such a drug has two tightly (covalently) linked fragments that bind to two different sites of the same target kinase.14 It is equivalent to a “super drug” that combines the effect of an existing small molecule inhibitor drug with the effect of a drug that traps c-src in I2.
In addition, a recent crystal structure of CDK2 (a serine/threonine kinase critical for G1 to S phase transition in the cell cycle.17 Inhibitors of it arrest cell cycle and prevent cancer development) is found to be similar to c-src I2.15 A fragment-based inhibitor (AT7519) has been designed to inhibit CDK2.14 Perhaps AT7519 can be slightly modified to inhibit c-src as well, since CDK2 and c-src I2 are structural analogues.
The same methodology and techniques used in this study can be applied to the other members of the Src tyrosine kinase family besides Hck and c-src to find out their subtle structural differences.16 Then these differences can be harnessed for future design of selective drugs that target each individual member like what we did for Hck and c-src.

For more technical details, please refer to the original paper.

References
  • Blume-Jensen, P. et al. Oncogenic kinase signaling. Nature. 411, 355-365 (2001).
  • Wheeler, D., Lida, M. & Dunn, E. The role of Src in solid tumors. Oncologist. 14 (7): 667-678 (2009).
  • Chial, H. Proto-oncogenes to oncogenes to cancer. Nature Education 1(1):33 (2008).
  • Knudson, A. Mutations and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci USA. 68(4): 821-823 (1971).
  • Fausel, C. Targeted chronic myeloid leukemia therapy: Seeking a cure. Am J Health Syst Pharm 64, S9-15 (2007).
  • Daub, H., Specht, K. & Ullrich, A. Strategies to overcome resistance to targeted protein kinase inhibitors. Nat. Rev. Drug Discov. 3, 1001-1010 (2004).
  • Bruce, A., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walters, P. The shape and structures of proteins. Molecular biology of the cell 4th edition (2002).
  • Meng, Y. & Roux, B. Locking the active conformation of c-Src kinase through the phosphorylation of the activation loop. J. Mol. Biol. 426, 423-435 (2014).
  • Yang, S., Banavali, N. & Roux, B. Mapping the conformational transition in src activation by cumulating the information from multiple molecular dynamics trajectories. Proc. Natl Acad. Sci. USA. 106, 3776-3781 (2009).
  • Roskoski, R. Src protein – tyrosine kinase structure and regulation. Biochem. Biophysics. Res. Commun. 324, 1155-1164 (2004).
  • Cowan-Jacob, S. et al. The crystal structure of a c-src complex in an active conformation suggests possible steps in c-src activation. Structure. 13, 861-871 (2005).
  • Zhang, J., Yang, P. & Gray, N. Targeting cancer with small molecule kinase inhibitors.Nat. Rev. Cancer. 9, 28-39 (2009).
  • Martin, M. P. et al. A novel approach to the discovery of small-molecule of cdk2. Chem biochem 13, 2128-2136 (2012).
  • Gill, A. New lead generation strategies for protein kinase inhibitors-fragment based screening approaches. Mini Rev. Med. Chem. 4, 301-311 (2004).
  • Betzi, S. et al. Discovery of a potential allosteric ligand binding site in cdk2. ACS Chem. Biol. 6, 492 (2011).
  • Shukla, D., Meng, Y., Roux, B. & Pande, V. Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat. Commun. 5:3397 doi: 10.1038/ncomms4397 (2014).
  • Tsai, L., Lees, E., Faha, B., Harlow, E. & Riabowol, K. The cdk2 kinase is required for the G1-to-S transition in mammalian cells. Oncogene. 8(6): 1593-


大意:
简述FAH最近在癌症方面的研究工作
第二弹(供有生物制药学背景人士阅读)
2.1 c-src和癌症的关系
c-src是c-src酪氨酸激酶的简称,在atp(三磷酸腺苷)释放能量时它可以替换一部分磷酸根,并附着在氨基酸分子上。c-src的作用包括:刺激细胞生长,再生,防止细胞死亡,细胞转移。这一切都与癌症的恶化与转移密切相关。当与c-src有关的基因发生紊乱时,细胞就会产生增生(就像你一脚踩下汽车油门一样),如果恰好这时机体的肿瘤抑制功能失常(类似于刹车失灵),后果将不堪设想。

2.2 目前市场上现售药物的工作机理?
C-src能与ATP结合使其正常工作。现有药物如Gleevec(格列卫,用于治疗慢性血癌)拥有类似于ATP的物质,能与C-src结合,从而减少了C-src与真正的ATP结合几率。

2.3 目前市场上现售药物的毒性和有效性?
现有药物比较有效,但是也有缺陷:
1、C-src只是激酶的一种,其他的激酶有些结构与C-src类似,但是对身体无害且有益。而现有药物靶向性较差,把其他激酶也误伤了。
2、C-src经常变异,所以癌细胞对现有药物很容易产生抗药性。
为了解决低靶向性和抗药性问题,我们需要在C-src找新的结合点。

2.4 我们找到新的结合点了吗?它是如何工作的?
所有的蛋白质(包括c-src)都是由许多氨基酸组成的,他们一直在在内部和外部的作用下不断的折叠、扭转、震动、变形。在c-src与ATP结合并激活它发挥作用前,它处于两种亚稳态构型中(I1、I2)。我们可以找到一个药物结合点,使c-src一直处于I2亚稳态,无法转为活跃态,从而使c-src失效。与现有疗法相比这种方法找到的药物具有低毒、低副作用、高效、高靶向性的特点。

2.5 c-src的前世今生
以前科学家只知道,c-src有活跃和不活跃,有I1、I2两个亚稳态,却不知道他们之间的联系。如今我们通过模拟知道了他们之间的先后顺序,也知道了c-src从不活跃到活跃需要106毫秒,而从活跃到不活跃只需要21毫秒。同时我们也获知了c-src的热力学和动力学特性。此外我们利用MSM模型研究了蛋白酶的复杂构型变化,了解了一个周期内所有的构型变化,而以前我们只知道其中一个构型。这是质的飞跃。

2.6 我们用什么方法进行研究?
我们以MSM为基础,结合了分布式计算,统计,各种算法和技术来模拟c-src的各种原子级的动态构型。蛋白质自身有成百上千个原子,它周围还有很多水分子也需要一同模拟。原子可以在万亿分之一秒内改变能量,震动和动力学特性。而c-src从不活跃转为活跃需要万分之一秒,相对原子而言,这时间非常长。而分子构型转变的路径也是千变万化的,所以要对他们进行模拟是一件非常巨大的工程。
幸运的是,我们利用分布式计算技术,把这个巨大的工程,分解成小WU,交由全球的志愿者进行计算。以前的研究由于缺少足够的计算资源,所以只能放弃动力学模拟的许多细节。

2.7 文中提到的方法可以提高药物靶向性吗?
很多激酶尤其是c-src族的结构都很相似,比如Hck激酶,也有I1和I2两个亚稳态,他们的I1几乎一模一样,而I2构型,c-src是完全展开,而Hck是部分展开。所以我们开发药物时就只针对c-src的I2构型,这样就不会误伤Hck激酶,提高了药物的靶向性。

2.8 展望未来
接下来我们将研究两个控制域(SH2, SH3),在模拟中他们可以锁定活跃状态。
研究共价结合抑制剂,把小的药物分子和c-src抑制剂结合起来,这样就可以制造超级药物。
最近发现CDK2(一种丝氨酸/苏氨酸激酶,可用于抑制癌细胞生长)的晶体结构与c-src类似。AT7519可以抑制CDK2,所以我想研究下能不能把它改改,用来抑制c-src。
项目的研究方法和技术也可以用于其他激酶的研究。

译注:好长的专业文章,翻译了一天多终于整完了,我吐血去了!

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 楼主| 发表于 2014-3-31 21:04:47 | 显示全部楼层
本帖最后由 vmzy 于 2014-3-31 22:18 编辑

Quantifying structural heterogeneity
March 31, 2014 by Greg Bowman ·
As part of Folding@home, you know that proteins change their structures (e.g. transitioning from unfolded to folded conformations).  Once a protein is folded, it is very common to think of it as adopting a single structure though.  To be fair, this is largely because scientists are reasonably good at solving a single, representative structure of a protein but its much harder to assess what sort of structural changes it undergoes with the same resolution.

In a recent paper from my lab, we set out to quantify how much structural heterogeneity there is in folded proteins (here).  We examined a set of proteins ranging from small proteins often used to study protein folding to much larger proteins that are more representative of what is typical in the cell.  We found that there is substantial heterogeneity in every case and demonstrated that our results are consistent with existing experimental data.  Our ability to capture this heterogeneity should be a powerful advantage given the myriad of ways it can affect a protein’s function.大意:
对结构性异质性进行量化
蛋白质在折叠过程中结构会不断变化,根据最新论文得出的数据,我对一系列蛋白质分子(从小分子到大分子),发现我们的模拟数据与现有的试验数据有着极高的一致性。

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 楼主| 发表于 2014-4-7 21:25:30 | 显示全部楼层
Progress on connecting computation with experiment
April 7, 2014 by Greg Bowman ·
Many biologically relevant conformational changes occur on milliseconds and slower timescales.  Furthermore, many experimental techniques are only sensitive to milliseconds and slower timescales.  Therefore, our ability to reliably capture millisecond timescale events through the use of Folding@home  and Markov state models opens up a host of exciting possibilities.

In one recent study, the Voelz and Pande labs teamed up with the Tokmakoff group at the University of Chicago to test the predicted folding mechanism of a protein referred to as NTL9 (here).  The Tokmakoff group specializes in using infrared spectroscopy to probe the details of molecular events, which complements the details we can access through simulation beautifully.  Importantly, these groups were able to demonstrate that a Markov state model for NTL9 correctly predicts details of the protein’s folding mechanism.  This is a great triumph for basic science, and also bodes well for the utility of our (Folding@home) results for proteins that are more closely tied to human disease.
大意:
最近Pande实验室和芝加哥大学合作,利用红外线光谱探测技术,通过实验室观测NTL9蛋白质数据和FAH的计算结果进行比对,证实了FAH准确预测了NTL9蛋白质折叠的细节。这是基础科学的重要突破。

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 楼主| 发表于 2014-4-17 10:24:25 | 显示全部楼层
Update to OS Stats
April 16, 2014 by Vijay Pande ·
We’ve taken a look at the stats script that generates the OS Stats page and fixed some under reporting issues we’ve been seeing.  The update script is now live.  The under reporting affected CPU stats (for Win, Lin, and OSX) but not GPU stats.
大意:
更新了操作系统统计脚本,现在cpu数量会变化不少。

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 楼主| 发表于 2014-4-28 09:16:27 | 显示全部楼层
Help vote Folding@home to the top of the NIH best videos
April 27, 2014 by Vijay Pande ·
The United State’s National Institutes of Health (NIH) is the primary funding agency for Folding@home.  They are having a competition for the best videos for NIH supported science.  Please help get the Folding@home community get recognition and support by upvoting our video!

Just click “like” on the youtube link here:

https://www.youtube.com/watch?v=oYm1IeS3fvw

You’ll see this is a short form version of our main video (there were length limits to the contest applicants).  You can see all of the applicants here.
大意:
NIH(美国国家卫生局)在票选最佳科研项目视频。求刷票!

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 楼主| 发表于 2014-5-7 09:12:40 | 显示全部楼层
24 core threshold for BA jobs
May 6, 2014 by Vijay Pande ·
Per our previous post, we have updated the core thresholds for the BA Work units to a 24 core threshold.
大意:
BA门槛上调至24T。


Designing good protein mimics by understanding their folding properties
May 6, 2014 by Vincent Voelz ·
Many drugs are small molecules that are able to block the active site of a protein, usually by binding to a small pocket. But for many proteins, especially signaling proteins overexpressed in cancer and other diseases, we would like to target their ability to make binding interactions with other proteins. To inhibit these so-called protein-protein interactions (PPIs), researchers have been trying to find good ways to design molecules that disrupt protein-protein binding interactions.

One way to find a good PPI disrupter is to design a mimic of the binding partner that binds better than the original. For example, so-called “stapled helix” mimics have been designed that mimic helix regions of particular protein binding partners. These mimics are small peptides that contain a hydrocarbon “staple” keeping them well structured in solution. Stapled helices bind very strongly because it takes less work for them to fold before they bind.

A stapling strategy could work well for designing beta-hairpin mimics too, but the synthetic chemistry is more difficult, and it is unclear exactly what kind of chemical “staples” would work best to use to produce a well-folded hairpin in solution.

In a new study from the Voelz Lab, we set out to see if computer simulation could help us pick the best designs. In collaboration with the Wuest Lab at Temple University, we simulated the folding of several different synthetic “stapled hairpins” designed to disrupt a PPI important in bacterial biofilm formation. Biofilms are dense aggregates of bacteria (like plaque on your teeth) that are hard for antibiotics to penetrate.

We evaluated each design using initial simulations on our computers at Temple, and then took the most promising designs to Folding@home, so we could get a very clear picture of how they are folding in solution. We found some surprising things that will help future design efforts. First, unlike helices, stapled hairpins can turn inside-out and fold all sorts of ways! This means it’s very important to have accurate models and lots of computing power to predict what shapes they assume in solution. Second, we found that even small chemical changes (like adding a –CH3 group) can have a big effect on conformational populations. In the future, it may be possible to useMarkov State Models (MSMs) of potential designs to figure out which chemical changes will help them fold the best. We think this is a really exciting direction for molecular simulation, and look forward to experimentally testing a new round of designs with the Wuest Lab. Ultimately, this work may lead to new chemical probes of the proteins involved in bacterial biofilm formation, as well as new classes of antibiotics.

Our results are described in a new paper (here).
大意:
现有的许多药物都是通过一个小分子与致病蛋白结合使其失效。而对于其他蛋白质(尤其是癌症和其他疾病中过多的信息素蛋白),这些蛋白一旦和致病蛋白结合就会激活它们。为了阻止这一过程,我们需要需要造出假的‘李鬼’蛋白中和掉这些信息素,降低他们与致病蛋白结合的几率。
不过通过化学合成这些假蛋白,非常不易。因此在最新的研究中,我们开始用计算机模拟假钩针蛋白(这种蛋白可以使细菌形成抗药性外膜,类似于我们比较熟悉的牙釉质)。
我们用FAH对设计的几个假蛋白进行了模拟,我们发现了更多细节信息。首先,不像螺旋蛋白,钩针蛋白的构型千变万化。想准确了解它在溶液中的形态必须依靠大规模计算进行模拟。其次,即使极小的化学结构变化(比如增加一个ch3甲基),也会导致构型发生巨变。将来我们将使用MSMs构型法,来优化我们的蛋白质设计。最终,这将会有助于新型抗菌素的研发。详情请看官方论文。

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 楼主| 发表于 2014-5-8 09:04:46 | 显示全部楼层
New AMD GPU driver improves performance
May 7, 2014 by Mark Piercy ·

We’re excited to announce that AMD has recently updated their GPU drivers from version 13.x series to 14.x series.  Version 14.x includes updated OpenCL code that helps Folding@home run faster on GPUs (using fahcore_17).  This results in a 5-10% improvement in performance. This should result in more points for some of the AMD GPU users.  The 14.4 drivers are available both for Windows and Linux.

14.4 driver download page: http://support.amd.com/en-us/download

For more detailed info and installer notes about the new drivers:

Windows-

http://support.amd.com/en-us/kb- ... t-windows-beta.aspx

Linux-

http://support.amd.com/en-us/kb- ... ux-beta-driver.aspx
大意:
AMD的14.x驱动更新了OpenCL代码,性能有5%到10%的提升,推荐大家更新。

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 楼主| 发表于 2014-5-23 10:46:48 | 显示全部楼层
Big Data Conference Live Streaming at Stanford
May 22, 2014 by Vijay Pande ·
At approximately 2:25pm, Prof. Pande will give a brief (12 minute) talk giving an overview of Folding@home and its results and then participate in a round table.  Live streaming at http://bigdata.stanford.edu.
大意:
斯坦福召开大数据会议,有现场直播,Pande老大在下午2点25分左右有个关于FAH的12分钟的演讲,之后会参加圆桌会议。有兴趣的可以去参观学习一下。

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 楼主| 发表于 2014-5-24 20:41:48 | 显示全部楼层
Webinar June 3, 2014: Next Steps for Folding@Home by Vijay Pande
May 23, 2014 by Vijay Pande ·
Please join us on June 3rd for a webinar presented by Vijay Pande, Professor of Chemistry, Structural Biology, and Computer Science at Stanford University and the founder of the Folding@Home project.  Professor Pande will give a brief introduction to Folding@home and successes in the project so far.  He will also discuss plans to greatly enhance Folding@home capabilities through new initiatives.

This webinar is planned for June 3rd, 2014 at 9.00 AM Pacific Time.    Register at: http://bit.ly/FolHome
大意:
6月3日开网络研讨会,Vijay Pande畅谈FAH的下一步计划。有兴趣的先去报名注册。

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 楼主| 发表于 2014-5-30 16:54:37 | 显示全部楼层
NaCl client points change
May 29, 2014 by Vijay Pande ·
Due to concerns brought up by donors that the short work units of our NaCl Folding@home client may negatively impact the bonus point system (by allowing donors to cherry pick WUs and run NaCl fast WUs to bring up their completion rate), we’ve decided to eliminate bonuses for NaCl work units. The bonus point formula can yield disproportionately high points for fast computers running short work units. To compensate for this change we’ve increased base points to 125 for these work units. This should result in a much more fair PPD for NaCl clients.
大意:
因为网页客户端的任务很短,所以奖励分相对较高,所以为了平衡积分系统,官方做了个艰难的决定——取消网页客户端任务的奖励分机制。同时把这类任务的基础分上调至125分。

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