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[独立平台] [生命科学类] Folding@Home

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 楼主| 发表于 2020-11-25 10:09:37 | 显示全部楼层
UPDATE FOR THOSE USING ADVANCED REMOTE CLIENT MANAGEMENT CONFIGURATIONS
November 23, 2020
by Emma

Hello Folders! We’ve been made aware of a potential issue for users who are using certain advanced manual configuration options for remote client management using fah-control. We actively recommend against this sort of remote management, so the issue affects less than 1% of our user base, and only under very specific conditions. Even so, in the interest of being transparent and not alarming our users, we are making this blog post to prevent confusion.

The issue is limited to the small subset of users that manually configured the client and their network to allow remote client management, and are using fah-control to connect over an untrusted network to a remotely accessible client port.

In those specific circumstances, if an attacker on the untrusted network could perform a PITM (person-in-the-middle) attack and actively manipulate network traffic, they would be able to remotely execute code in the context of the user running the fah-control GUI. The actual Folding@home client on the remote machine would not be affected, but the system running the fah-control GUI itself could be affected.

If you currently perform the manual steps described and may be affected, we recommend you update to client version v7.6.20 or later. These versions have the fix applied and are no longer affected.

It is also important to point out that manually configuring fah-control to manage remote clients is not recommended when used over an untrusted network. If you need to do this remotely over the public internet, we recommend using a VPN or similar method of extending a trusted network between two locations.

We would also like to thank the researchers that brought this to our attention.

Thanks to Rutger Beltman:
http://www.linkedin.com/in/rabeltman

Also to Axel Koolhaas:
https://www.linkedin.com/in/axel-koolhaas-49980183/

We greatly appreciate you both taking time to review some of our open source code and help us through responsible and coordinated disclosure practices.

For anyone else out there who would like to report any potential security concerns, please refer to our contact page at the below link. We may be updating it in the near future with improved security contact information, and our policies and preferences around reporting security vulnerabilities.

Folding@home Security Contact Details:
https://foldingathome.org/contact-us/
大意:
老版本的fah-control远程管理存在安全漏洞,建议大家(由于是要使用fah自带远程管理功能的用户)尽早将客户端升级到最新版(7.6.20+)
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 楼主| 发表于 2021-11-15 09:37:07 | 显示全部楼层
本帖最后由 vmzy 于 2021-11-27 18:56 编辑

core22 0.0.18 widespread rollout on Mon 15 Nov
by JohnChodera » Sun Nov 14, 2021 1:22 pm

Hi all!

We're planning to roll out core22 0.0.18 to all FAH projects on Mon 15 Nov.

This version is built from OpenMM 7.6.0, the latest and most performant release of OpenMM: http://openmm.org

We have used CUDA Toolkit 11.2 for these builds in order to support the latest, fastest NVIDIA GPUs. This may result in older GPUs or drivers that are no longer supported by CUDA 11.2 to fall back to OpenCL instead; you may need to update your NVIDIA driver to one that supports CUDA 11.2 (linux >= 460.32.03, windows >=461.09) if this happens.

We broke CUDA support for the Linux core build in 0.0.16 by not including trailing version digits on shipped CUDA shared libraries. This has now been fixed.

Huge thanks to David Dotson, Peter Eastman, and all the FAH volunteers who helped us test this release!

~ John Chodera // MSKCC
大意:
GPU计算内核core22,将在11月15号(周一)正式升级至0.0.18版。该版本将OpenMM内核升级至最新的7.6.0版,支持CUDA Toolkit 11.2。
请大家注意升级驱动(linux >= 460.32.03,linux glibc>=2.17, windows >=461.09),否则计算内核会因为驱动问题,自动退出cuda模式切至OpenCL模式。
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 楼主| 发表于 2022-8-1 09:46:09 | 显示全部楼层
AMD RX6000+ family GPU owners, please update to 22.7.1 drivers for optimal F@H performanceby muziqaz » Wed Jul 27, 2022 5:42 pm
https://foldingforum.org/viewtop ... 673252c6c548bcaeec0
Hi,

as cs9k already reported in another thread, AMD finally fixed OpenCL performance with their latest driver version (22.7.1).
Until today majority of RX6000 series GPUs were folding at ~50% of it's capabilities and the last driver which was still offering good performance was 21.3.2, which was not compatible with majority of RX6000 series GPUs. Now your RDNA2 GPUs can match nVidia OpenCL performance with 22.7.1.

If you have older AMD GPUs, you can keep folding on 21.3.2 or older.

For those wondering how much you will gain? Your PPD pretty much will double, or even triple in certain scenarios.
If you are seeing 2.5-5m PPD with your 6800/6900xt GPUs, that means new driver is working for you.

Good luck, and thank you for your contribution
大意:
RX6000系列的新显卡,建议及时把显卡驱动更新至22.7.1以上的最新版,opencl性能翻倍。
老款显卡没加上buff,请酌情升级(建议版本不超过21.3.2)
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 楼主| 发表于 2023-12-1 10:59:07 | 显示全部楼层
UNRAVELING THE MYSTERY OF DRUG SPECIFICITY: THE CASE OF BLEBBISTATIN
November 30, 2023
by Greg Bowman

When it comes to designing novel drugs, achieving specificity is a major challenge. An effective drug must bind tightly to its target protein while avoiding unwanted side effects that can result from interactions with other proteins. This challenge becomes even more complex when targeting specific members of protein families with similar structures. Additionally, some enzymes share substrates, like ATP, across various protein families, making it difficult to design compounds that compete with endogenous ligands without causing off-target effects.

One innovative approach to drug design is targeting allosteric sites rather than active sites. Allosteric compounds can enhance desirable protein functions, offering a unique way to achieve specificity. These sites are often less conserved than active sites, making it easier to develop specific drugs. In recent years, highly specific allosteric compounds have been serendipitously discovered through high-throughput screens, targeting various proteins such as G-protein-coupled receptors, myosins, kinases, and β-lactamases. Despite these successes, designing drugs that target allosteric sites from scratch is challenging because experimental structural studies often provide limited insights into a protein’s conformational landscape.

One specific area of interest is myosins, a superfamily of ATPases that play crucial roles in various cellular processes. Myosins have the potential to be valuable drug targets for numerous diseases, but their complexity and the existence of multiple isoforms make targeting specific myosin variants extremely difficult. For instance, there are 38 myosin genes in the human genome, and individual cells express about 20 different myosin isoforms. Compounds like mavacamten have shown promise in clinical trials for heart-related conditions, but there is a need for more myosin modulators to address a broader range of diseases. However, the challenge lies in targeting specific myosin isoforms due to their highly conserved motor domain fold and active site structure.


Figure caption: Structure of a myosin protein highlighting the binding sites of some known allosteric modulators, including blebbistatin.
Blebbistatin, a myosin-II specific allosteric inhibitor, has been a subject of study to understand the molecular mechanisms governing drug specificity. It was discovered in a high-throughput screen targeting nonmuscle myosin IIs and was found to broadly inhibit various myosin-II isoforms while sparing other myosin families. The key to its selectivity lies in the dynamics of the blebbistatin pocket and the conformations myosin isoforms adopt in solution.

Through all-atom molecular dynamics simulations, this study has shown that the probability of the blebbistatin pocket opening is higher in more sensitive myosin isoforms, which explains differences in drug potency. This finding, along with differences in the pocket’s residue composition, provides insights into the factors contributing to drug specificity. These results demonstrate the role of pocket dynamics and conformational selection in achieving drug specificity and highlight the potential for precision medicine through computational modeling.

In conclusion, the study of blebbistatin sheds light on the intricate world of drug specificity in the realm of myosin inhibitors. It emphasizes the importance of understanding the dynamic interplay between drug molecules and protein structures. This knowledge has the potential to open doors to more precise drug design, allowing us to target specific isoforms and improve the effectiveness of therapeutic interventions. As the field of precision medicine advances, computational modeling and simulations like the ones used in this study offer promising opportunities to tailor treatments to individual patients and address a wide range of diseases with unprecedented specificity.

WCG Team
译文:
揭开药物特异性之谜:白比他汀案例
2023年11月30日
作者 格雷格·鲍曼

在设计新型药物时,实现特异性是一个重大挑战。一种有效的药物必须与它的目标蛋白紧密结合,同时避免与其他蛋白相互作用,以免可能产生的不必要的副作用。当针对具有类似结构的蛋白质家庭的特定成员时,这一挑战就变得更加复杂。此外,一些酶在不同的蛋白家族中具有像三磷酸腺苷一样的底物,因此很难设计出能与内源性配体竞争而又不会产生目标外效应的化合物。

一个创新的药物设计方法是针对异形部位而不是活跃部位。异体化合物能增强理想的蛋白质功能,为实现特异性提供了独特的途径。这些异形部位往往比活跃部位的更不保守,因此更容易开发出特定的药物。近年来,高特异性的异体化合物通过高通量筛选偶然被发现,其目标是各种蛋白质,如G蛋白耦合受体、肌蛋白、激酶和β-内酰胺酶。尽管取得了这些成功,但是从零开始设计针对异体部位的药物是很有挑战性的,因为实验结构研究通常对蛋白质的构型提供的见解有限。

一个特殊的关注领域是肌蛋白,一个在各种细胞过程中起关键作用的总科。肌球蛋白有可能成为许多疾病的重要药物靶点,但它们的复杂性和多种异形体的存在使针对特定的肌球蛋白变异体的靶向极其困难。例如,人类基因组中有38个肌球蛋白基因,单个细胞表达大约20个不同的肌球蛋白异型。在心脏病的临床试验中,像Mavacamten这样的化合物已经显示出了希望,但是需要更多的肌球蛋白调节剂来治疗更广泛的疾病。然而,由于其高度保守的运动域折叠和活跃的部位结构,目前的挑战在于如何定位特定的肌球蛋白异形。


图说明:肌球蛋白的结构,突显了一些已知的异体调节器的结合部位,包括布比他汀。
一种II型肌蛋白的特异性异体体抑制剂布利比他汀一直是研究的课题,以了解控制药物特异性的分子机制。在一个针对II型非肌肌球蛋白的高通量筛选中发现了它,并发现它广泛抑制各种II型肌蛋白异型,同时绕开了其他肌蛋白家族。选择性的关键在于布比司他汀口袋的动力学和溶液中采用的肌蛋白异型。

通过全原子分子动力学模拟, 这项研究结果表明,在更敏感的肌球蛋白异形体中,布比他汀口袋开口的概率更高,这解释了药物效力的差异。这个发现,以及口袋里残基成分的差异,提供了对药物特异性的因素的深入了解。这些结果说明了口袋动力学和构象选择在实现药物特异性方面的作用,并通过计算建模强调了精密医学的潜力。

总之,对布比他汀的研究揭示了肌球蛋白抑制剂领域药物特异性的复杂世界。它强调了理解药物分子和蛋白质结构之间动态相互作用的重要性。这种知识有可能为更精确的药物设计打开大门,使我们能够针对特定的异形体设计药物,并提高治疗干预的有效性。随着精密医学领域的发展,像本研究中所使用的那样的计算建模和模拟为针对个别患者的治疗提供了有希望的机会,并以前所未有的特异性来治疗广泛的疾病。
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 楼主| 发表于 2024-4-19 09:33:42 | 显示全部楼层
NEW FOLDING@HOME SOFTWARE IN BETA
April 18, 2024
by Greg Bowman

The beta release of our new client software is going well. Thanks to everyone who has tried it out and given us feedback! If you haven’t already, you can download the software here.


This new client, called Bastet after the Egyptian goddess believed to protect the home from disease, is entirely open sourced! You can access the code and make contributions here.

This software has all the features of the previous client and adds the option to securely monitor your Folding machines from anywhere, including your cell phone!

You can learn more about the software and how to download/install/use it from our updated docs here.

The security of your folding machines is of the utmost importance. Online operations are optional and are limited to starting, stopping, monitoring, and configuring folding. Join the security discussion for more information


大意:
新的FOLDING@HOME软件开始公测
2024年4月18日
作者:格雷格·鲍曼

我们新客户端软件的测试版进展顺利。感谢所有参与测试并向我们提供反馈的人!如果您还没有下载该软件,可以在此处下载。

这个新客户端名为Bastet,以埃及女神的名字命名,据信可以保护家庭免受疾病侵害,它是完全开源的!您可以在此处访问代码并做出贡献。

该软件具有以前客户端的所有功能,并添加了从任何地方(包括您的手机)安全监控您的客户端的功能!

您可以从我们的更新文档中了解有关该软件如何下载/安装以及使用它的更多信息。

客户端的安全性至关重要。在线操作是可选的,仅限于启动、停止、监控和配置客户端。加入安全讨论以获取更多信息。
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 楼主| 发表于 2024-5-6 10:28:33 | 显示全部楼层
ALPHAFOLD OPENS NEW OPPORTUNITIES FOR FOLDING@HOME
May 2, 2024
by Greg Bowman

Folding@home has long sought to understand how proteins self-assemble, or fold, into their functional structures and what the functional implications of dynamics within the context of a folded protein are. As such, many may wonder what it means for the project when they hear that a new software algorithm called AlphaFold has “solved” the protein folding problem.

For background, AlphaFold is a machine learning algorithm that was trained to predict the structure of a protein from the sequence of chemicals, called amino acids, that the protein is made of. The algorithm was trained on the protein databank (PDB),(7) which is a publicly available repository of over 200K protein structures that has been accumulated over decades by requiring structural biologists to deposit their structures during peer review of their work. Many other algorithms had been developed to predict protein structures using a combination of physics and machine learning based on available structures. For decades, the performance of these methods was regularly tested through blind predictions via the critical assessment of protein structure prediction (CASP) competition. While the field made great progress over time, it had hit somewhat of a plateau in recent years. AlphaFold broke this trend, making a substantial stride in accuracy. Its predictive power is one of the most compelling examples of the enormous power that computational methods have to offer biomedical research.

While AlphaFold is an amazing advance, it does not solve the problems that Folding@home focuses on. A key tenet of much of our work at Folding@home is that individual protein structures are enormously valuable but are just the tip of the iceberg. A single structure does not tell us how a protein folds up into that structure, nor does it tell us what the moving parts of a protein are that allow it to function.

The upshot is that AlphaFold has created many new opportunities for Folding@home. Our work on the dynamics of folded proteins generally depends on having at least one high-resolution structure from experiments. For many proteins, no such structure is available, so we at Folding@home have had little to contribute to better understanding such proteins. Now, however, structures predicted with AlphaFold are sufficiently accurate that we can use them as starting points for our work even when no experimental structure is available. In one recent example, we used the AlphaFold-predicted structure of an important drug target called PPM1D to understand how some mysterious inhibitors of the protein likely work.

If you’d like to learn more, I recently wrote a perspective piece on this topic here.

大意:
ALPHAFOLD为FAH带来了新的机遇。
2024年5月2日
格雷格·鲍曼

一直以来,人们都在寻求理解蛋白质是如何自我组装或折叠到它们的功能结构中的,以及在蛋白质折叠的背景下,动力学的功能含义是什么。因此,当很多人听说一个新的ALPHAFOLD软件算法已经"解决"了蛋白质折叠问题时,他们可能想知道这对项目意味着什么。

就背景而言,ALPHAFOLD是一种机器学习算法,它被训练用来从蛋白质的化学物质的序列(即氨基酸)预测蛋白质的结构。该算法是在蛋白质数据库(pdb)(7)上进行的训练,该数据库是一个超过20万个蛋白质结构的公开存储库,几十年,要求结构生物学家在同行审查他们的工作时保存他们的结构,才积累了这么多的结构。利用物理学和基于现有结构的机器学习相结合的方法,开发了许多其他的预测蛋白质结构的算法。几十年来,通过对蛋白质结构预测(CASP)竞赛的严格评估,通过盲预测对这些方法的性能进行了定期测试。这个领域随着时间的推移取得很大进展时,近年来,它的发展有些停滞不前。AlphaFold打破了这一趋势,在准确性上迈出了一大步。它的预测能力是计算方法提供生物医学研究的巨大力量中最引人注目的例子之一。

虽然AlphaFold是一个惊人的进步,但它并不能解决FAH所关注的问题。FAH的大部分工作的一个主要原则是,单个蛋白质结构非常有价值,但只是冰山一角。一个单一的结构并不能告诉我们一个蛋白质是如何折叠到这个结构中的,也不能告诉我们一个蛋白质的运动部分如何发挥作用。

结果是,AlphaFold创造了许多FAH的新机会。我们对折叠蛋白动力学的研究通常依赖于至少有一个来自实验的高分辨率结构。对于许多蛋白质来说,没有这样的结构,所以我们在FAH对更好地理解这些蛋白质没有什么贡献。然而,现在,用AlphaFold预测的结构足够精确,即使没有实验结构,我们也可以把它们作为工作的起点。在 最近的一个实验中,我们利用一个叫ppm1d的重要药物靶点的AlphaFold结构来了解某些神秘的蛋白质抑制剂是如何工作的。

如果你想了解更多,我最近写了一篇关于这个话题的观点文章。

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 楼主| 发表于 2024-5-17 10:02:35 | 显示全部楼层
HOW DOES APOE CAUSE ALZHEIMER’S DISEASE?
May 16, 2024
by Greg Bowman

A number of active projects on Folding@home right now aim to understand how different forms of the protein apolipoprotein E (ApoE) determine one’s risk of developing Alzheimer’s disease.

Alzheimer’s disease is the 6th leading cause of death in the USA and there are no effective treatments. Moreover, the prevalence of this age-related neurodegenerative disease is likely to increase as the population ages. Therefore, there is a great need to understand Alzheimer’s disease and develop therapeutics.

ApoE is an appealing target for treating Alzheimer’s disease because which form of this lipid transporter a person has is one of the best predictors of how likely they are to develop Alzheimer’s. People with the ApoE4 form are up to 15-fold more likely to develop Alzheimer’s disease than people with the more common ApoE3 form. Meanwhile, people with ApoE2 appear to have a lower risk of developing Alzheimer’s. However, the mechanism coupling ApoE and Alzheimer’s disease remains unclear.

Understanding the structural differences between the different forms of ApoE could enable the design of ‘structure correctors’ that combat Alzheimer’s disease by stabilizing non-toxic conformations. However, characterizing these differences remains challenging. ApoE is extremely dynamic, so it can’t be studied with most experimental techniques for determining a protein’s structure.

That’s where Folding@home comes in. We are currently simulating with various forms of ApoE to understand what makes them different.

Our results on ApoE4 are quite surprising. It has long been believed that the two ends of ApoE4 interact with each other in a way that ApoE3 and ApoE2 do not. This unique interaction in ApoE4 has been proposed to somehow trigger other processes in the brain that lead to Alzheimer’s disease. However, we don’t see any evidence for this interaction! Moreover, experiments on single ApoE proteins are consistent with our computational predictions. Hopefully, the simulations that are currently running on Folding@home will shed light on what’s really going on.


A)         A proposed structure of ApoE3 and B) a structure of ApoE4 from our simulations with the N-terminal domain (NTD, gray), receptor-binding site (yellow), hinge (orange), C-terminal domain (CTD, blue). The red bits are where the chemical composition of ApoE2, ApoE3, and ApoE4 differ from each other. The green bits are the parts that were previously thought to touch each other in ApoE4 but not the other forms of ApoE.

大意:
APOE是如何导致阿尔茨海默病的?
2024年5月16日
作者:格雷格·鲍曼

目前Folding@home上的许多进行中项目旨在了解不同形式的载脂蛋白E(ApoE) 如何决定一个人患阿尔茨海默病的风险。

阿尔茨海默病是美国第六大死因,目前尚无有效的治疗方法。此外,随着人口老龄化,这种与年龄相关的神经退行性疾病的患病率可能会增加。因此,迫切需要了解阿尔茨海默病因并开发治疗方法。

ApoE是治疗阿尔茨海默病的一个有吸引力的靶点,因为一个人拥有哪种形式的载脂蛋白是他们患阿尔茨海默病的可能性的最佳预测因素之一。具有ApoE4形式的人患阿尔茨海默病的可能性是具有更常见的ApoE3形式的人的15倍。与此同时,携带ApoE2的人患阿尔茨海默病的风险似乎较低。然而,ApoE与阿尔茨海默病的耦合机制仍不清楚。

了解不同形式的ApoE之间的结构差异可以设计“结构校正器”,通过稳定无毒构象来对抗阿尔茨海默病。然而,描述这些差异仍然具有挑战性。ApoE具有极高的动态性,因此无法使用大多数确定蛋白质结构的实验技术对其进行研究。

这就是Folding@home的用武之地。我们目前正在模拟各种形式的ApoE,以了解它们的不同之处。

我们对ApoE4的结果非常令人惊讶。长期以来,人们一直认为ApoE4的两端以ApoE3和ApoE2不存在的方式相互作用。ApoE4中的这种独特相互作用被认为会以某种方式触发大脑中导致阿尔茨海默病的其他过程。然而,我们没有看到任何这种相互作用的证据!此外,对单个ApoE蛋白的实验与我们的计算预测一致。希望目前在Folding@home上运行的模拟能够揭示到底发生了什么。

图A)ApoE3的拟议结构和 图B)我们模拟的 ApoE4 结构,包括 N 端结构域(NTD,灰色)、受体结合位点(黄色)、铰链(橙色)、C 端结构域(CTD,蓝色) )。红色部分是 ApoE2、ApoE3 和 ApoE4 的化学成分彼此不同的地方。绿色部分是之前被认为在 ApoE4 中相互接触的部分,但在 ApoE 的其他形式中则不然。
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