楼主: vmzy

[独立平台] [生命科学类] Folding@Home

 楼主| 发表于 2020-11-25 10:09:37 | 显示全部楼层
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:

Also to Axel Koolhaas:

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:

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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
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

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

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 楼主| 发表于 2023-12-1 10:59:07 | 显示全部楼层
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
作者 格雷格·鲍曼





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。


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 楼主| 发表于 2024-4-19 09:33:42 | 显示全部楼层
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







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 楼主| 发表于 2024-5-6 10:28:33 | 显示全部楼层
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.





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。



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 楼主| 发表于 2024-5-17 10:02:35 | 显示全部楼层
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.


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。






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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