楼主: vmzy

[独立平台] [生命科学类] Folding@Home

 楼主| 发表于 2016-11-11 09:14:17 | 显示全部楼层
Stats System Down Temporarily
November 9, 2016 by Carlos Hernández ·
We’re experiencing some technical issues with one of our client-side servers (  Stanford IT is working hard to get things working again, but in the meantime individual and team points data might not be accessible (however all work units should receive credit). We’ll post an update as soon as things are up and running again.


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 楼主| 发表于 2017-1-31 23:51:09 | 显示全部楼层
Release: New core 21 v.0.0.18 with support for Nvidia drivers 375.57+
January 30, 2017 by thynell ·
We are pleased to announce the release of core 21 v0.0.18 to full FAH. The updated core will be pushed out automatically for both windows and linux donors over the next week as projects are updated to require this min-core-version.

This release contains a workaround for a change made in the NVIDIA OpenCL driver introduced in drivers starting with 375.57, as well as some improvements to error handling codes.

While a slight reduction in performance may be observed, this performance regression should be eliminated when NVIDIA removes the hotfix in forthcoming driver updates.

There is a known issue that this release will, for some projects, print sporadic messages to the log due to a known visualizer bug:

05:34:52:WARNING:FS01:Size of positions 64914 does not match topology 19

This issue does not affect the quality of the science, and a workaround is already in testing for v0.0.19, which we hope to release in a week.

Thank you all for your patience.

Kindly report issues here:

~ The FAH Core 21 crew
发布v0.0.18新版GPU core。下周会随项目设置更新。
 楼主| 发表于 2017-5-25 10:06:09 | 显示全部楼层
本帖最后由 vmzy 于 2017-5-25 10:49 编辑

May 24, 2017
by Theresa Derner

Hello Everyone,


The Stanford Research Computing Facility hosting some of our key Folding@home servers will be carrying a maintenance to their electricity system on Thursday & Friday, July 6 &7th. We expect F@H to be down those days. So please prepare for outage. Sorry for the inconvenience it may cause!


Folding at Home team!


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 楼主| 发表于 2017-7-6 11:06:16 | 显示全部楼层
JULY 6th &7th, 2017 F@H servers Down all day
Postby TDPanda1*COStaf » Thu Jul 06, 2017 3:17 am

Hello Everyone,


The Stanford Research Computing Facility hosting some of our key Folding@home servers will be carrying a maintenance to their electricity system on Thursday & Friday, July 6 &7th. We expect F@H to be down those days. So please prepare for outage. Sorry for the inconvenience it may cause!

Plus we are working on the Member Certificate issues. Thanks for your patience.


Folding at Home team!


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  发表于 2017-7-7 17:09
发表于 2017-12-4 18:35:03 | 显示全部楼层
New (GRO A4) projects 14041 and 14042 to ADV
Postby yunhui » Wed Nov 29, 2017 6:22 pm

Releasing new SMP A4 (GRO_A4) projects p14041 and p14042 to ADV. These are exactly the same as projects p13717 and p13179, but adaptive sampling from those two old projects. These are being served from (
Description here:

Project: 14041
stats credit: 996
timeout: 15.2
deadline 33.3
k-factor v=0.75
number of atoms: 30404

Project: 14042
stats credit: 994
timeout: 15.2
deadline 33.2
k-factor v=0.75
number of atoms: 30347

Disease Type: Unspecified

Phenylalanine Hydroylase (PAH) functions to keep the essential amino acid phenylalanine (Phe) below levels that cause irreversible brain damage in infancy/childhood and behavioral problems throughout life.  Mutations to the PAH genes can disrupt this control function and cause phenylketonuria (PKU), which occurs in about 1:12000 live births.  Due to mandatory screening, PKU is typically diagnosed at birth and successfully treated with strict dietary control of protein intake.  However, adults living with PKU need a less restricted diet and new therapies are sought.  Such therapies can come from understanding how PAH works.

Until very recently, the way that PAH controls Phe was poorly understood.  However, new research reveals important details about how PAH changes its shape in order to control its enzymatic activity (1, 2).  Our goal in this project is to use molecular simulation to understand the PAH shape changes that accompany activation in response to elevated levels of Phe and how this process is perturbed in disease.

A4多核包14041、14042转移至ADV(参数下接包)。项目的研究目的:苯丙氨酸羟化酶(PAH)的基因突变会导致人体必备氨基酸苯丙氨酸(Phe)在婴儿及儿童时期的含量低下,造成不可逆的脑损伤,苯丙酮尿​​症的发病概率在1:12000。新的研究表明PAH会改变其形状以控制其酶活性,项目便研究 Phe 水平的升高对疾病的影响导致PAH的变化。


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发表于 2018-2-10 16:02:05 | 显示全部楼层
February 6, 2018
by Anton Thynell

Related Articles
Training and Validation of a Liquid-Crystalline Phospholipid Bilayer Force Field.

J Chem Theory Comput. 2016 Dec 13;12(12):5960-5967

Authors: McKiernan KA, Wang LP, Pande VS

We present a united-atom model (gb-fb15) for the molecular dynamics simulation of hydrated liquid-crystalline dipalmitoylphosphatidylcholine (DPPC) phospholipid bilayers. This model was constructed through the parameter-space minimization of a regularized least-squares objective function via the ForceBalance method. The objective function was computed using a training set of experimental bilayer area per lipid and deuterium order parameter. This model was validated by comparison to experimental volume per lipid, X-ray scattering form factor, thermal area expansivity, area compressibility modulus, and lipid lateral diffusion coefficient. These comparisons demonstrate that gb-fb15 is robust to temperature variation and an improvement over the original model for both the training and validation properties.

发表于 2018-3-25 10:23:56 | 显示全部楼层
March 12, 2018
by Anton Thynell

Transferable neural networks for enhanced sampling of protein dynamics.

J Chem Theory Comput. 2018 Mar 12;:

Authors: Sultan MM, Wayment-Steele HK, Pande VS

Variational auto-encoder frameworks have demonstrated success in reducing complex nonlinear dynamics in molecular simulation to a single non-linear embedding. In this work, we illustrate how this non-linear latent embedding can be used as a collective variable for enhanced sampling, and present a simple modification that allows us to rapidly perform sampling in multiple related systems. We first demonstrate our method is able to describe the effects of force field changes in capped alanine dipeptide after learning a model using AMBER99. We further provide a simple extension to variational dynamics encoders that allows the model to be trained in a more efficient manner on larger systems by encoding the outputs of a linear transformation using time-structure based independent component analysis (tICA). Using this technique, we show how such a model trained for one protein, the WW domain, can efficiently be transferred to perform enhanced sampling on a related mutant protein, the GTT mutation. This method shows promise for its ability to rapidly sample related systems using a single transferable collective variable, enabling us to probe the effects of variation in increasingly large systems of biophysical interest.

PMID: 29529369 [PubMed – as supplied by publisher]

发表于 2018-3-26 14:42:59 | 显示全部楼层


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发表于 2018-10-22 21:52:52 | 显示全部楼层
Simulations of the regulatory ACT domain of human phenylalanine hydroxylase unveil its mechanism of phenylalanine binding.

J Biol Chem. 2018 Oct 04;:

Authors: Ge Y, Borne E, Stewart S, Hansen MR, Arturo EC, Jaffe EK, Voelz VA

Phenylalanine hydroxylase (PAH) regulates phenylalanine (Phe) levels in mammals to prevent neurotoxicity resulting from high Phe concentrations as observed in genetic disorders leading to hyperphenylalaninemia and phenylketonuria. PAH senses elevated Phe concentrations by transient allosteric Phe binding to a protein-protein interface between ACT domains of different subunits in a PAH tetramer. This interface is present in an activated PAH tetramer (A-PAH) and absent in a resting-state PAH tetramer (RS-PAH). To investigate this allosteric sensing mechanism, here we used the GROMACS molecular dynamics simulation suite on the Folding@home computing platform to perform extensive molecular simulations and Markov state model (MSM) analysis of Phe binding to ACT domain dimers. These simulations strongly implicated a conformational selection mechanism for Phe association with ACT domain dimers and revealed protein motions that act as a gating mechanism for Phe binding. The MSMs also illuminate a highly mobile hairpin loop, consistent with experimental findings also presented here that the PAH variant L72W does not shift the PAH structural equilibrium toward the activated state. Finally, simulations of ACT domain monomers are presented, in which spontaneous transitions between resting-state and activated conformations are observed, also consistent with a mechanism of conformational selection. These mechanistic details provide detailed insight into the regulation of PAH activation and provide testable hypotheses for the development of new allosteric effectors to correct structural and functional defects in PAH.

PMID: 30287685 [PubMed – as supplied by publisher]

摘要:苯丙氨酸羟化酶(PAH)是哺乳动物体内的一种非常重要的酶,它能够调节动物体内的苯丙氨酸(Phe)含量,从而防止过高苯丙氨酸浓度产生的神经毒性对动物造成伤害(由基因失调导致的高苯丙氨酸血症以及苯丙酮酸尿症就是苯丙氨酸羟化酶异常的结果)。四聚体①苯丙氨酸羟化酶②可以通过迅速的变构③在两个亚基④的ACT结构域⑤之间形成一个蛋白质交互界面(protein-protein interface),并通过这个界面与苯丙氨酸的结合,从而感知苯丙氨酸浓度的变化。这个界面只存在于被激活的四聚体苯丙氨酸羟化酶(A-PAH)中,而在处于休眠状态的四聚体苯丙氨酸羟化酶(RS-PAH)却不存在。为了研究这种变构感知的机制,我们在这里使用了Folding@home 计算平台的GROMACS 分子动力学模拟工具,进行了广泛的分子模拟和对苯丙氨酸与ACT结构域二聚体结合过程的 Markov state model analysis (具体解释看不懂,这个词在wiki上被重定向到了“隐马尔科夫模型”页面)。这些模拟的结果强烈暗示着在苯丙氨酸与ACT结构域二聚体的反应中有一种蛋白质构象选择机制的存在;同时模拟结果也显示出,在与苯丙氨酸结合的过程中,蛋白质起到了类似闸门的作用。对Markov state model 的分析结果则发现了一条高度灵活的茎环(hairpin )⑥,实验结果表明苯丙氨酸羟化酶的异构体L72W 的结构稳定性并没有向着激活状态改变。最后,对于ACT结构域单聚体的模拟结果:我们观察到,在自发进行的激活-休眠形态转换过程中,同样存在一种构造选择机制。这些机制的发现,让我们对苯丙氨酸羟化酶的激活控制有了更深入的认识,并且为将来开发新变构效应因子(allosteric effectors )、用以修复苯丙氨酸羟化酶的结构与功能性缺陷提供了可试验的假设。

⑤ACT结构域:蛋白质结构域(英语:protein domain)是蛋白质中的一类结构单元,是构成蛋白质(三级)结构的基本单元。[引用4] ACT结构域是一种在蛋白质中非常常见的结构域,它与多种受氨基酸浓度调控的代谢酶有关。它得名于三种含有这种结构域的蛋白质: Aspartate kinase (天冬氨酸激酶), chorismate mutase (分支酸变位酶)和 TyrA (预苯酸脱氢酶)。[引用5]  这种结构能通过精确调整蛋白质的构象提供变构调节。

[引用1] 维基:Phenylalanine hydroxylase条目“Tetramerization domain”部分
[引用2]《苯丙氨酸羟化酶的研究进展》(《生命科学》第25卷 第4期 2013年4月)(实际上,引用的这篇发表于2013年的文章已经在相当程度上解释了这篇项目论文的研究背景)
[引用5]维基:ACT domain


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 楼主| 发表于 2019-10-8 10:12:41 | 显示全部楼层
October 7, 2019
by Greg Bowman

This past June, a server error led to a failure to assign bonus points to a number of work units. We corrected the error as soon as it was detected. Now we have identified the work units that were impacted. Recredits will take place over the next few days. To all those who were affected, thank you for your patience.
 楼主| 发表于 2020-1-3 17:44:11 | 显示全部楼层
December 30, 2019
by Greg Bowman

Thanks to everyone who has contributed to Folding@home over the past year! We greatly appreciate the computer time, human time, and financial resources you contribute to support our science.

With your help, we’ve been tackling a range of tough problems, including translational research that aims directly at improving clinicians’ ability to diagnose and treat diseases and basic research that provides a foundation for these advances. Some highlights from the past year and opportunities they create for the coming years include:

1、Discovery of new druggable sites for combating antibiotic resistance. Rational drug design is often based on a single snapshot of what a protein typically looks like. However, proteins have many moving parts. We (i.e. the Bowman lab) have been interested in uncovering these moving parts and exploiting these insights to devise new ways of targeting proteins with drugs. In one recent example, we examined the motions of a protein called β-lactamase, which is a key player in antibiotic resistant infections. As you may have heard in the news, antibiotic resistance is a growing health threat that currently costs our nation billions of dollars and tens of thousands of lives every year. One of the most common ways bacterial infections achieve antibiotic resistance is by producing β-lactamase proteins that chop up antibiotics, thereby preventing them from killing bacteria. Our simulations of this protein have revealed a portion of the structure that opens up, creating what we call a “cryptic” pocket because it is absent in known structures of the protein. In subsequent experiments, we showed that drug-like molecules can bind in this pocket and reduce β-lactamase’s ability to chop up antibiotics. An open access version of the paper is available here ( In the future, we plan to apply these methods to proteins that are currently considered ‘undruggable’ to find ways to render them viable drug targets.
2、Understanding the mechanisms of proteins whose malfunction causes cancer. The Chodera lab has made nice headway on understanding how a protein called SETD8 works. Mutations of this protein that increase (or decrease) its function can result in cancer or neurological diseases. Understanding how SETD8 normally works is an important step towards understanding how it malfunctions and how we can develop therapeutics. Towards this end, the Chodera lab and their experimental collaborators uncovered motions of SETD8 that are important for its natural function. An open access version of the paper is available here ( Future work will help uncover how mutations lead to cancer and how we can mitigate these effects with drugs.
3、Improved simulation algorithms. The calculations we perform on Folding@home are extremely demanding from a computational perspective. So, we’re always looking for ways to make the most effective use of the computing resources our volunteers provide. One general approach that remains of great interest is “adaptive sampling”, in which we iterate between running simulations and deciding which of the structures we have discovered so far it would be most useful to run more simulations from. The Voelz lab recently published a paper on how to do this effectively. An open access version of the paper is available here ( These methods will be useful for many of the simulations we perform in the coming years.
 楼主| 发表于 2020-2-29 11:38:16 | 显示全部楼层
原文地址: ... covid-19-2019-ncov/

February 27, 2020
by Greg Bowman

We need your help! Folding@home is joining researchers around the world working to better understand the 2019 Coronavirus (2019-nCoV) to accelerate the open science effort to develop new life-saving therapies. By downloading Folding@Home, you can donate your unused computational resources to the Folding@home Consortium, where a research team at Memorial Sloan Kettering is working to advance our understanding of the structures of potential drug targets for 2019-nCoV that could aid in the design of new therapies. The data you help us generate will be quickly and openly disseminated as part of an open science collaboration of multiple laboratories around the world, giving researchers new tools that may unlock new opportunities for developing lifesaving drugs.

2019-nCoV is a close cousin to SARS coronavirus (SARS-CoV), and acts in a similar way. For both coronaviruses, the first step of infection occurs in the lungs, when a protein on the surface  of the virus binds to a receptor protein on a lung cell. This viral protein is called the spike protein, depicted in red in the image below, and the receptor is known as ACE2. A therapeutic antibody is a type of protein that can block the viral protein from binding to its receptor, therefore preventing the virus from infecting the lung cell. A therapeutic antibody has already been developed for SARS-CoV, but to develop therapeutic antibodies or small molecules for 2019-nCoV, scientists need to better understand the structure of the viral spike protein and how it binds to the human ACE2 receptor required for viral entry into human cells.

Proteins are not stagnant—they wiggle and fold and unfold to take on numerous shapes.  We need to study not only one shape of the viral spike protein, but all the ways the protein wiggles and folds into alternative shapes in order to best understand how it interacts with the ACE2 receptor, so that an antibody can be designed. Low-resolution structures of the SARS-CoV spike protein exist and we know the mutations that differ between SARS-CoV and 2019-nCoV.  Given this information, we are uniquely positioned to help model the structure of the 2019-nCoV spike protein and identify sites that can be targeted by a therapeutic antibody. We can build computational models that accomplish this goal, but it takes a lot of computing power.   

This is where you come in! With many computers working towards the same goal, we aim to develop a therapeutic remedy as quickly as possible. By downloading Folding@home here [LINK], you can help provide us with the computational power required to tackle this problem. One protein from 2019-nCoV, a protease encoded by the viral RNA, has already been crystallized. Although the 2019-nCoV spike protein of interest has not yet been resolved bound to ACE2, our objective is to use the homologous structure of the SARS-CoV spike protein to identify therapeutic antibody targets.

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at

Structures of the closely related SARS-CoV spike protein bound by therapeutic antibodies may help rapidly design better therapies. The three monomers of the SARS-CoV spike protein are shown in different shades of red; the antibody is depicted in green. [PDB: 6NB7]

(post authored by Ariana Brenner Clerkin)


PDB 6lu7 structure summary ‹ Protein Data Bank in Europe (PDBe) ‹ EMBL-EBI (accessed Feb 5, 2020).

Tian, X.; Li, C.; Huang, A.; Xia, S.; Lu, S.; Shi, Z.; Lu, L.; Jiang, S.; Yang, Z.; Wu, Y.; et al. Potent Binding of 2019 Novel Coronavirus Spike Protein by a SARS Coronavirus-Specific Human Monoclonal Antibody; preprint; Microbiology, 2020.

Walls, A. C.; Xiong, X.; Park, Y. J.; Tortorici, M. A.; Snijder, J.; Quispe, J.; Cameroni, E.; Gopal, R.; Dai, M.; Lanzavecchia, A.; et al. Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion. Cell 2019, 176, 1026-1039.e15.
 楼主| 发表于 2020-3-11 16:14:04 | 显示全部楼层
March 10, 2020
by John Chodera

This is an update on Folding@home’s efforts to assist researchers around the world taking up the global fight against COVID-19.

After initial quality control and limited testing phases, Folding@home team has released an initial wave of projects simulating potentially druggable protein targets from SARS-CoV-2 (the virus that causes COVID-19) and the related SARS-CoV virus (for which more structural data is available) into full production on Folding@home. Many thanks to the large number of Folding@home donors who have assisted us thus far by running in beta or advanced modes.

This initial wave of projects focuses on better understanding how these coronaviruses interact with the human ACE2 receptor required for viral entry into human host cells, and how researchers might be able to interfere with them through the design of new therapeutic antibodies or small molecules that might disrupt their interaction.

In the coming days, we hope to take advantage of some of the new structural biology and biochemical data that is being rapidly released by researchers around the world who are working to understand these viruses and strategies for defeating them. This work has been largely disseminated by preprint servers such as bioRxiv and chemRxiv, which aim to make research rapidly available to both other researchers and the public for other scientists to broadly evaluate and immediately start building on. We have also forged several new collaborations with other laboratories where we hope Folding@home will provide valuable support in COVID-19 research efforts.

While we will rapidly release the simulation datasets for others to use or analyze, we aim to look for alternative conformations and hidden pockets within the most promising drug targets, which can only be seen in simulation and not in static X-ray structures. We hope that these structures—once validated by emerging compound screening data—could help direct the virtual screening campaigns or the targeting of new pockets for which atomistic structures were not yet available.

Below, we provide short descriptions of the projects. Note that all input files are being made available on GitHub here for other researchers to take advantage of:

This repository will evolve over the coming days as we add more projects and documentation. We will start posting datasets with structures on publicly available servers as soon as we have useful data to report.

All projects are using the new GPU-accelerated Core22 based on the open source OpenMM biomolecular simulation engine.

SARS-CoV-2 RBD domain in complex with human ACE2 receptor (PDBID: 6vsb, 6acg) [10.1126/science.abb2507, 10.1371/journal.ppat.1007236]
11741: Coronavirus SARS-CoV-2 (COVID-19 causing virus) receptor binding domain in complex with human receptor ACE2. atoms: 165550, credit: 15396

11746: Coronavirus SARS-CoV-2 (COVID-19 causing virus) receptor binding domain in complex with human receptor ACE2 (alternative structure to 11741). atoms: 182699, credit: 16615

SARS-CoV-2 main protease in complex with an inhibitor N3 (PDBID: 6lu7) [Not yet published]
11742: Coronavirus SARS-CoV-2 (COVID-19 causing virus) protease in complex with an inhibitor. atoms: 62227, credit: 9405

11743: Coronavirus SARS-CoV-2 (COVID-19 causing virus) protease – potential drug target. atoms: 62180, credit: 9405

SARS-CoV-2 RBD domain in complex with human neutralizing S230 antibody Fab fragment (PDBIDs: 6nb7, 6nb8, 2ghv) [ 10.1016/j.cell.2018.12.028 (for both 6nb7 and 6nb8), 10.1074/jbc.M603275200]
11744: Coronavirus SARS-CoV (SARS causing virus) receptor binding domain trapped by a SARS-CoV S230 antibody. atoms: 109578, credit: 7608

11745: Coronavirus SARS-CoV (SARS causing virus) receptor binding domain mutated to the SARS-CoV-2 (COVID-19 causing virus) trapped by a SARS-CoV S230 antibody. atoms: 110370, credit: 7685

To contact us to discuss collaborations or data, please email us at

Special thanks to TBCP graduate student Rafal Wiewiora and CBM graduate student Ivy Zhang for their work in modeling these structures from existing experimental data and preparing these projects, and to all the Folding@home donors who help make this work possible!

~ The Chodera lab SARS-CoV-2 team and Folding@home Consortium ~
 楼主| 发表于 2020-4-14 14:49:46 | 显示全部楼层
April 13, 2020
by Xuhui Huang

Coronavirus (CoV) has a capsid that envelops the single-stranded RNA genome. Three structural proteins are shown to be associated with the capsid: membrane, envelope, and the spike protein. Chemical compounds and monoclonal antibodies that target SARS-CoV-2 surface proteins have been under investigation for treating COVID-19.  In addition, new vaccines targeting the viral surface antigens are under intense development for the prevention of COVID-19.  Unfortunately, drugs and vaccines with inhibitory mechanisms targeting surface receptors may not be effective due to the constant evolution of surface receptors to acquire drug resistance and evade host immune response.  In contrast, viral RNA-dependent-RNA polymerase (RdRp) is a protein that is deeply buried inside the viral capsule and is responsible for viral replication. Furthermore, the functional conservation of the RdRp renders it highly resistant to mutations.  Hence, RdRp serves as a promising drug target.

Developing drugs targeting SARS-CoV-2 RdRp is a challenging task due to its intrinsic proof-reading exonuclease (ExoN) function.  The replicase polyprotein of coronaviruses is a multifunctional protein that contains 16 non-structural proteins (nsp).  Among them, nsp12 is the core catalytic subunit responsible for the nucleotide addition (see Fig. 1a).  If nsp12 made an error to incorporate a mismatched nucleotide, nsp14-ExoN will correct the mistake by cleaving this mis-incorporated nucleotide (see Fig. 1b).  NTP analogs can inhibit the catalytic core subunit of nsp12, but to counteract NTP analogs incorporation into the nascent RNA, nsp14’s intrinsic proofreading function removes the incorporated NTP analogue.  Remdesivir is  a promising drug candidate to treat COVID-19 infection largely due to its ability to inhibit both nucleotide addition (nsp12) and cleavage (nsp14).

Figure 1: Viral replication enzymes in SARS-CoV-2. (a) nsp12 for nucleotide addition, (b) nsp14 for proofreading via nucleotide cleavage.
The Huang lab from the Hong Kong University of Science and Technology has released a series of Folding@Home projects to simulate the nsp12 and nsp14 complex.  We will screen existing FDA-approved NTP analog drugs. We aim to repurpose drugs that can inhibit SARS-CoV-2 replication.
发表于 2020-4-21 20:31:08 | 显示全部楼层
April 17, 2020
by Greg Bowman

In response to popular demand, we have created an update to the Folding@home software that allows you to prioritize COVID-19 projects. We encourage you to upgrade as the new software includes important bug fixes and security updates. Downloads are available here. Please also join me in thanking the Center for the Science and Engineering of Living Systems (CSELS) at Washington University in St. Louis for funding the development of this software update.

Our top priority for this release was to add the COVID-19 option as quickly as possible. We also took the opportunity to fix many of the issues raised by our volunteers, but did not address those that would have caused significant delays in the release of the new software.  To better address important bugs in the future, we’ve organized a team of volunteer developers who are sorting through and prioritizing our issue tracker on GitHub.  They are already making huge strides.

Looking towards the future, we are also working on a new Open-Source Folding@home software.  This new software will improve the performance of Folding@home and make it easier to get the community involved in its development.  By tapping into the massive amount of technical talent available in the Folding@home community, we believe we can produce better software with a more engaging and productive user experience and update that software more often.  More information about the availability of this new software will soon be announced on this blog.


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能开源就太好啦  发表于 2020-4-23 10:17


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