Out with the old psummary, reminder of the new

Our projects page is a key way for donors to see the most up to date information about what is running on Folding@home.

About a year and a half ago we updated our project summary page.  We posted about it here: https://folding.stanford.edu/home/new-psummary-page/ As promised in that post, we are now deprecating the old psummary.  For most people this change will be transparent.  However, if any 3rd party tools are still using the old psummary they will have to update their code.

The new psummary can now be found in the following places: http://fah-web.stanford.edu/psummary.html    and  http://fah-web.stanford.edu/new/psummary.html

You can also access this data in JSON format:   http://fah-web.stanford.edu/psummary.json

Where can I see more detail about what’s going on in FAH?

For those new to FAH, here’s a reminder for where the nitty gritty details can be found if people are interested to learn more than what’s on our web site.

In terms of new projects and more detailed (less high level) announcements, they’re here on the FCF:  https://foldingforum.org/viewforum.php?f=24

For code updates, our science code development is done in the open on github, open source:  https://github.com/pandegroup/openmm and our other mature projects are on github.

For updates on Pande Group papers, here’s a good link that’s automatically kept up to date:  http://www.ncbi.nlm.nih.gov/pubmed/?term=pande++vs .  And for a sense of the most important works, this one is useful: https://scholar.google.com/citations?user=cWe_xpUAAAAJ&hl=en

As you can see from those links, there’s a ton of activity going on behind the scenes.  Feel free to drop by the FCF or follow us on Github if you’re curious to learn more.

Closing in on 100 Petaflops

It’s been a while since we updated the FLOP per GPU report (since Tue, 26 Feb 2013 in fact).  A lot of progress has been made in 3+ years in GPU-land.  Over the last 3 years, FAH has seen a few trends, especially consolidation into people to GPUs and the increase in power from those GPUs.  The upshot is that today, FAH is running off the power of about 40,000 GPUs.  While that’s not a ton of donors, due to the power of GPUs, this is an immense amount of compute power.  In terms of FLOPs, we’re getting close to 100 Petaflops, which would be a major milestone in computing.  In terms of our ability to tackle complex systems, we’re simulating considerably bigger, more complex systems for longer timescales than ever before, which perhaps is the most important part of this beyond just the numbers.

Thanks to everyone who has helped make this possible.  And we’ll do a better job of more frequent updating of this page so these big jumps don’t catch people by surprise.

Planned server outage 2/28

The Stanford networking team will be upgrading some infrastructure this coming Sunday, February 28, 2016, from 12AM – 4AM. This work will be partially  disruptive to where some key FAH content is served from, namely the www.stanford.edu pages.  Our site will experience some downtime during this window. We cannot pinpoint when the downtime will occur, however it will last approximately 5-10 consecutive minutes sometime during this window.

New paper on non-native salt-bridge effects on folding

Salt−bridges are electrostatic interactions between positively charged residue side chains (like arginine or lysine) and negatively-charged side chains (like aspartic and glutamic acids).  They play an important role in stabilizing many protein structures, and have been shown to be designable features for protein design.

In this work, we study the effects of non-native salt bridges on the folding of a soluble alanine-based peptide helix (Fs peptide) using extensive all-atom molecular dynamics simulations performed on the Folding@home distributed computing platform. Using Markov State Models, we show how non-native salt-bridges affect the folding kinetics of Fs peptide by perturbing specific conformational states. Furthermore, we present methods for the automatic detection and analysis of such states.

Why are these results important to our understanding of protein folding? It used to be thought that protein unfolded states resembled a “random coil” with few structural features.  We now know that salt-bridges forming in unfolded states can significantly affect folding stability and dynamics. Our work shows that we can quantitatively predict these effects.  Eventually, we hope to be able to use such knowledge to engineer the folding properties of proteins.

Just Accepted manuscript: http://dx.doi.org/10.1021/acs.jpcb.5b11767

salt-bridge-toc

 

FAH’s achievements in 2015, with a glimpse into 2016

We’re reaching the end of 2015 and as in previous years, I’d like to summarize for donors what Folding@home (FAH) has done in 2015 and where we’re going.  In general, FAH has always tried to push the limits in terms of new software infrastructure (new clients, backend software), new algorithms, improving the accuracy of our approaches, and perhaps most importantly the disease areas we’re targeting and making advancement in.  Here’s a summary of what we’ve done this year and where we’re going.

As in 2015, key areas in 2016 will also be infrastructure, methods, accuracy, and targets.  Below, in each section, after the update for 2015, I’ll give my vision for where FAH is going.  As some of these items involve unannounced initiatives with partners, some of these will unfortunately be more in teaser form (sorry – as always, this part will be much more brief than the summary of the previous year’s results).  Much like I had to tease our mobile launch in January 2015 in my December 2014 summary, the good news is that hopefully donors won’t have to wait too long for some of these to be more formally announced in 2016.

For those who want to see all of the details, please check out our papers page, which includes abstracts, layman summaries, and links to the specific research results.

1) FAH Infrastructure

Philosophy and vision.  As in previous years, we have continued our push in developing new backend and frontend software.  This has allowed us to stay on the cutting edge, but does introduce more instability in FAH for donors, so this is often a difficult tradeoff.  But, my thinking here has been that we’re here to push the limits and hopefully we can communicate that FAH is more like a piece of scientific equipment than a BMW, always changing and improving, but at the cost of frequent change and upheaval.

New Assignment Server (AS).  We rolled out a new AS into production.  I’m personally a little wistful since the previous AS was the last piece of code I was the original and primary author.  However, in the 15+ years we’ve run Folding@home, the operation has grown significantly from me and a few students doing everything to a team of programmers, a large group of scientists, many affiliated labs, as well as sysadmins, designers, etc.

The new AS has some key features which are important in this “grown up” version of FAH: the old AS had some very onerous requirements for updating its configuration (which worked fine when FAH had 3 scientists) but now with 20+, we needed a whole new approach.  Also, the new AS allows for more efficient allocation of FAH donor machines to problems as well as setting the stage for more advanced analytics.

Mobile clientWith our launch of our mobile client (where mobile means phones and tablets), we’ve pushed into another type of infrastructure.  My vision for mobile is that the trend in computing is clear: people are often going without desktops or laptops in favor of mobile, so FAH needs to evolve with those trends as well.  While the current mobile phones do not have the compute power of modern desktop/laptop CPUs, the gap is narrowing quickly, especially with the power of mobile GPUs advancing very quickly as well.  So, our mobile client lays the foundation for much of FAH’s potential future.

It’s also worth noting in passing that in the early days of our GPU client (which I’m beaming with pride to say predates other GPU computing in our field by a wide margin, as we were running GPUs on Folding@home even before CUDA existed), the GPU client also didn’t have amazing performance.  But it was clear to me that it would take off and that bet has born out very well today.  It’s of course way too early to know if mobile will follow a similar path, but I feel that it’s an important bet to make.

Native Client (NaCl).  The NaCl client is another sort of experiment akin to mobile.  It takes easy installation to the ultimate level by making running FAH just going to our web site, with a Chrome browser.  The next steps here for growth is working on viral marketing campaigns to get the word out.

Work Server (WS) updates.  Finally, we have continued to make improvements to the WS.  These improvements are typically only visible to researchers in FAH, but this is very much the backbone of how FAH works and we continue to make the WS easier to use (simplifications to how researchers configure it), more powerful in features (in serving science calculations), as well as more powerful in its ability to serve up WUs (speed improvements from architectural overhauls to allow a given server to server more donors).

Stats system update.  Finally, we have made some updates to the stats system to make it much simpler on the backend.  This has led to fewer stats issues and a faster response in handling stats issues.

 

2016 Vision.  As detailed above, to keep FAH on the cutting edge and make the most out scientifically of donor’s contributions.

New clients.  We will look to expand the mobile clients more broadly.  (Sorry for not saying more here, but like many of our rollouts, we do combined announcements with our partners.)

New Gromacs core.  The Gromacs core continues to be a workhorse core for CPU clients and we are working on releasing an updated core with more broad hardware support, including support for AVX, which will give some donors an automatic performance boost.

WS to do more advanced science.  In conjunction with new methods below, we will be rolling out new WS features, such as high frequency adaptive sampling (see below).

More advanced analyticsWe’ve been working on this area for a while and it’s often pushed behind other priorities, but this is becoming a greater and greater need for us to monitor FAH in a much improved way.

New stats systemGiven all of the other infrastructure work, this is possibly the least likely to happen in 2016, but we recognize this as a key need.  This is the last part of FAH which is very old and has not been recently updated.

 

2) New methods

Philosophy and vision.  Since FAH has been doing this for so long, it’s now often taken for granted that one can use a huge, loosely coupled set of processors to do huge calculations.  But this is far from easy.  In fact, when I was first proposing FAH, many people said it was actually impossible.  And in a sense it was –– it would have been impossible to use many processors to do traditional parallelization of our calculations, as that requires a super high bandwidth, low latency network.  And in the early days of FAH, many people were even on modems (yes!  It’s easy to forget what life was like in 2000).

A key part of our success with FAH has been pushing the limits for how we can use a FAH like resource to do greater and greater things, i.e. more complex systems, more predictive models, etc.  And this year has been a particularly productive year in advancements in this area.  Moreover, the techniques that we develop here very naturally have been applied to cloud computing, further broadening the impact of FAH’s scientific impact.

Markov State Models (MSMs).  In the last 10 years, our lab (and a few others) have pioneered MSM methods and they in recent years have very much come of age, becoming in many cases a gold standard for distributed computation.  In particular, there were several key advances by Robert McGibbon and other researchers in my lab which have allowed MSM construction to be completely automated as well as to be more accurate.  Our MSM code is fully open source, available at http://msmbuilder.org .

OpenMM advancements.  Finally, the foundation of our newest clients (GPU and mobile) is OpenMM, our high speed molecular dynamics code.  OpenMM is derived from the original GPU code from Folding@home and has now gone to power many other researcher’s work as well.   This year’s advances to OpenMM have allowed us to run on modern GPUs, improve speed, and improve accuracy (change notes are available on the OpenMM github page).   OpenMM is fully open source, available at http://openmm.org .

 

2016 Vision.  As detailed above, to push our ability to do new science, especially given the extreme algorithmic challenges of a distributed architecture.

MSM advancesWe’re continuing to push advances in MSMs, especially in connection with modern machine learning advances.  This will also give us a big boost in our ability to understand the deeper meaning in FAH results.

Adaptive sampling.  Adaptive sampling has played a key role in many FAH calculations, but it’s been done with a great deal of manual work by the WS project managers.  In conjunction with new infrastructure, this will be now automatic.

OpenMM.  OpenMM’s development will continue to push into greater speed and functionality, with a particular push into much improved speed for the advanced accuracy AMOEBA force field and support for AMOEBA in Folding@home.  Both will come with OpenMM 7.0 which will roll into a new core (likely Core22).

 

3) More accurate models

Philosophy and vision.  A key challenge in simulation in general is the tension between accuracy and computability.  Here, I’m especially talking about the model we use to calculate the forces between atoms in our simulations.  One could choose a very accurate model, but then not be able to compute very much (in chemistry, the most accurate models, eg FCI, barely can handle a few atoms and in FAH we want to simulate hundreds of thousands of interacting atoms).  My vision here has been to have it both ways – to develop high performance models that have considerably improved accuracy, namely accuracy greater than what considerably more expensive approaches would afford.  This year has seen the first fruits of this initiative, which was started several years ago, and like many FAH initiatives, donors only see the results at the very end (sorry, that’s the nature of research – there’s nothing we can show until this is ready for publication).   But the first results of this initiative are very exciting, with great promise for years to come.

Water first.  Our first improvements in models comes with our force field for water, led by Dr. Lee-Ping Wang, who is now a professor in the Chemistry Department at UC Davis.  Water is very fundamental to all of our simulations, so it’s the natural place to start.  Our water model is considerably more accurate than previous ones due to a radically new approach, where we use heavy computation and statistical methods to make fundamental advances, not unlike how MSMs have helped as well, albeit in a different area.

 

2016 Vision.  Development of force fields more broadly.  Going beyond water, we expect to revamp force fields across the board, with broad impact in our calculations for FAH.  FAH will also play a key role in quantitatively understanding the improvements these force fields bring.

 

4) Disease targets

Philosophy and vision.  In choosing targets, i.e. disease areas where we use Folding@home to make advances, we have been picking areas where there would be a significant impact combined with where FAH can help the most.  In general, FAH projects run a very long time – it takes a lot of compute power to address many of the questions we tackle.  Therefore, I’ll cover the newer areas below.

Kinases (key to cancer) now becoming common place.  Kinases are key to many cancer therapies as in cancer, cells grow unchecked and inhibiting kinases can stop this.  In the past, kinases were a reach for FAH (and I like pushing the team to reach for hard targets) but now they are very much commonplace and even in many cases relatively “easy” targets.  This means we can aggressively go after many of them, especially in collaborations with key experimentalists.  We have had a key kinase paper published, with more on the way.

Channels.  Ion channels are also exciting targets, with very broad pharmacological and disease impact.  However, these are very large and challenging systems, a reach much like kinases used to be, so this is an area to watch particularly in 2016.

 

2016 VisionAs discussed above, we will continue push to bigger and more sophisticated systems, hopefully with channels becoming much easier as kinases did.  Also, I have been working on ways we can partner with other groups, including groups from pharma, for “open source” science in this space, making results fully available to improve the whole community’s ability to impact disease.  Sorry that this is more brief here – like major software announcements, we hold back details on future targets until we’re ready to roll them out.

Summary.  I’d like to thank all of our donors for their contributions to Folding@home.  It’s exciting to me to be discussing these amazing results and 15 years ago would not have imagined that we’ve been able to do what we do now and I’m excited in terms of how our vision and plans for the future will be able to push us even farther.

 

PS For those who want to see all of the details, please check out our papers page, which includes abstracts, layman summaries, and links to the specific research results.

 

 

New paper on peptoid helix folding from the Voelz Lab

Nature relies on the remarkable folding properties of proteins and nucleic acids to perform vital chemical work. Not surprisingly, synthetic chemists have long set their sights on developing non-natural molecules—called “foldamers”—that can harness similar folding properties for a number of diverse applications including nanomaterials, biotherapeutics, and chemical catalysts.

Peptoids (N-substituted oligoglycines) are bio-inspired synthetic heteropolymers that can fold into a number of diverse structural scaffolds. In a new paper by Mukherjee et al., we report on improved simulation potentials that better model peptoid helices, a key motif for peptide mimics. We then use a statistical mechanical helix-coil model to examine the thermodynamic forces that drive helix formation. We find that, unlike peptides, peptoid helices can increase their entropy upon folding, indicating that steric bulk of plays a large role in stabilizing helices. These findings will help future efforts in computational peptoid design.

Just Accepted manuscript: http://dx.doi.org/10.1021/acs.jpcb.5b09625

Issues with FahCore21

We’ve discovered that the new FahCore_21 is producing more errors than we consider acceptable for some clients.  The error rate seems to depend on several factors but most noticeable is that it doesn’t work well with second generation Maxwell GPUs.  A few projects have made their way through Advanced testing have been distributed to everyone under the default “FAH” client-type setting.  To allow donors to limit this exposure, those projects have been reclassified as “Advanced” which is appropriate for a FahCore that is still under development.

As has always been the case, the “Advanced” setting will give you access to newer projects which may have a higher error rate.  It is our intention to provide only the safest assignments with the default setting or you can choose to configure your system to run these advanced projects depending on how frequently you encounter these errors.

These conditions are expected to improve as new projects, new versions of that FahCore, or new versions of the drivers incorporate whatever fixes are required.  In the meantime, Work Units which are completed successfully allow scientific research to progress toward even more challenging projects than we’ve done so far.

mTOR: Projects 10491-10499

In projects 10491-10499, the Chodera lab takes a look at mTOR, a serine/threonine kinase. The MTOR gene was originally discovered in yeast in 1991 and named TOR1/2 because it was the target of rapamycin, an anti-fungal small molecule isolated from the soil of Easter Island in the 1970s. In 1994, the mammalian target of rapamycin (mTOR) was discovered by Drs. Sabatini, Snyder, Abraham, and Schreiber.

mtorpicmTOR integrates multiple signal inputs to control processes such as cell growth and metabolism, among others. Due to its role in controlling a number of cellular processes, mTOR has clinical significance in neurodegenerative diseases, diabetes and cancer. In the Chodera lab, we are working with the Hsieh lab at MSKCC to understand mTOR’s role in cancer and the development of new and better therapeutics that target it.

Currently, the FDA has approved treatment for metastatic clear cell Renal Cell Carcinoma (ccRCC) that includes mTOR inhibitors such as Everolimus and Temsirolimus. An effort to understand the patient-to-patient variation in response to these drugs by studying how extraordinary responders lead to the characterization of mTOR activating missense mutations in these patients. These mutants cluster in two domains of mTOR: the kinase and FAT domains. These projects will allow us to generate a model of the conformations available to mTOR and ultimately to investigate how these clinically relevant mutations might influence the protein’s structure.

Both the mTOR kinase domain and the larger construct including the FAT domain are very large systems, exploiting the latest OpenMM GPU core (0×21) and push the capabilities of latest-generation GPUs to their full extent.

mtormutant

Happy 15th Birthday, Folding@home!

I’m happy to announce that October 1, 2015 is Folding@home’s 15th birthday.  It’s amazing for me to look back at all we’ve done together, all we’ve built, and all that’s come out of it.  From CPUs to GPUs to PS3 to NaCl to Mobile.  And today we’re in the midst of an update of the FAH web site, rollout of new mobile and other (yet unannounced) clients, as well as new projects in cancer and Alzheimer’s.

And we look forward to celebrating the changes to come over the next 15 years!

Add your computer's power to over 327,000 others that are helping us find cures to Alzheimer's, Huntington's, Parkinson's and many cancers ...

... in just 5 minutes.

Step 1.

Download protein folding simulation software called

Folding@home

.

Step 2.

Run the installation. The software will automatically start up and open a web browser with your control panel.

Step 3.

Follow the instructions to Start Folding.

Stanford University

will send your computer a folding problem to solve. When your first job is completed, your computer will swap the results for a new job.

Download the protein folding simulation software that fits your machine.

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