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Stanford's goal: to understand protein folding, protein aggregation, and related diseases.

What are proteins and why do they "fold"? Proteins are biology's workhorses -- its "nanomachines." Before proteins can carry out their biochemical function, they remarkably assemble themselves, or "fold." The process of protein folding, while critical and fundamental to virtually all of biology, remains a mystery. Moreover, perhaps not surprisingly, when proteins do not fold correctly (i.e. "misfold"), there can be serious effects, including many well known diseases, such as Alzheimer's, Mad Cow (BSE), CJD, ALS, and Parkinson's disease.

What does Folding@Home do? Folding@Home is a distributed computing project which studies protein folding, misfolding, aggregation, and related diseases. Stanford uses novel computational methods and large scale distributed computing, to simulate timescales thousands to millions of times longer than previously achieved. This has allowed us to simulate folding for the first time, and to now direct Stanford's approach to examine folding related disease.

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Cervical cancer declining, but HPV-related cancers of anus and throat increasing
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Update on on-going software development in FAH
[H]ard|Folding Administrator

Posts: 102
Points: 2,813,787
Work Units: 6,596

Posted: Tue Nov 27, 2012 09:14 pm
Stanford posted an update on the next client and core releases.

We have several on-going software development efforts and I'd like to give donors an update.

v7 client. Joe Coffland and his team have been working hard on new client releases. 7.2.9 has just been released and a new version will be undergoing beta testing soon. Moreover, we are continuing work on improving the v7 client for windows and squashing the remaining bugs. Moreover, there's additional effort in OSX due to the hiring of a programmer (Kevin Bernhagen) just for the OSX client, as well as additional work for smoother OSX and linux installs.

Gromacs core. The Gromacs core team (Prof. Michael Shirts and Prof. Peter Kasson and their labs, at the University of Virginia) are working on the new cores based on the new version of gromacs (4.6).

New OpenMM core. The OpenMM team at Stanford (Dr. Peter Eastman and Yutong Zhao) are working on speed improvements for OpenMM (the basis of the FAH GPU core) in general, but in particular optimizations for Kepler and AMD (in coordination with engineers at NVIDIA and AMD, respectively). Yutong has a new FAH GPU core working in the lab and we are doing internal testing on it. Since openMM is full open source, you can see more details, including a commit and change log, at the openMM web site (

Full Article here
Life with Playstation ending, FAH team continuing to look to push the envelope.
[H]ard|Folding Administrator

Posts: 102
Points: 2,813,787
Work Units: 6,596

Posted: Sun Oct 28, 2012 04:04 am
For several years, we have worked closely with Sony to bring Folding@home to the PS3. We're excited about what we've been able to do. Since the PS3 started folding in 2007, we've done some really amazing things, with several announcements this year acknowledging advancements.

Full Article here.

Unified GPU/SMP benchmarking scheme: equal points for equal work

The current benchmarking calculations for SMP and GPU projects are performed on different machines since originally the SMP cores could not perform the calculations that the GPUs cores could and vice versa (GPUs were only for implicit solvent calculations and SMP only for explicit solvent calculations). With recent advances in both cores and completion of our testing of these capabilities to ensure agreement, we are now confident we can do the same work on both cores. Thus, we feel that it is time to unify GPU and SMP benchmarking, both for simplicity and fairness.

Full Article [url=[/url]
New Gromacs, new you.
[H]ard|Folding Administrator

Posts: 102
Points: 2,813,787
Work Units: 6,596

Posted: Thu Sep 27, 2012 07:16 am
A new version of Gromacs (4.6) is coming, and were working to bring it to Folding@home. The new code contains a number of improvements (more than youd expect for a minor version number!), and well post about some of the individual features as we go. Not all of them will be available on F@h immediately, as some will require substantial development work over the next few months. But some of the basics are new free energy methods from our very own Prof. Michael Shirts, new and slightly faster inner-loop code, and some important tweaks to parallelization. Free energy calculations allow us to calculate things like how tightly drugs bind to proteins and the strength of attraction between protein components when pulled apart. And you, of course, know what faster inner-loop code and better parallelization mean!

Full Article: here

New methods for analyzing FAH data

Two general objectives of the Folding@home project are (1) to explain the molecular origins of existing experimental data and (2) to provide new insights that will inspire the next generation of cutting edge experiments. We have made tremendous progress in both areas, but particularly in the first area. Obtaining new insight is even more of an art and, therefore, less automatable.

To help facilitate new insights, I recently developed a Bayesian algorithm for coarse-graining our models. To explain, when we are studying some processlike the folding of a particular proteinwe typically start by drawing on the computing resources you share with us to run extensive simulations of the process. Next, we build a Markov model from this data. As Ive explained previously, these models are something like maps of the conformational space a protein explores. Specifically, they enumerate conformations the protein can adopt, how likely the protein is to form each of these structures, and how long it takes to morph from one structure to another. Typically, our initial models have tens of thousands of parameters and are capable of capturing fine details of the process at hand. Such models are superb for making a connection with experiments because we can capture all the little details that contribute to particular experimental observations. However, they are extremely hard to understand. Therefore, it is to our advantage to coarse-grain them. That is, we attempt to build a model with very few parameters that is as close as possible to the original, complicated model. If done properly, the new model can capture the essence of the phenomenon in a way that is easier for us to wrap our minds around. Based on the understanding this new model provides, we can start to generate new hypotheses and then test them with our more complicated models and, ultimately, via experiment.

Full Article: here
New way to diagnose locations of diseases.
[H]ard|Folding Administrator

Posts: 102
Points: 2,813,787
Work Units: 6,596

Posted: Thu Aug 30, 2012 02:38 am
Not much news out of Stanford this month, but I did find a couple interesting articles.

New Ultraviolet Light Can Pinpoint Location Of Diseases

A new study published in the Online Early Edition of Proceedings of the National Academy of Sciences reveals that Johns Hopkins researchers have developed a synthetic protein, which, when activated under ultraviolet lighting, can show doctors exactly where certain medical disorders are located, such as arthritis and cancer.

Full Article here

New Computer Simulation Models Metastasis

Cancer metastasis, the escape and spread of primary tumor cells, is a common cause of cancer-related deaths. But metastasis remains poorly understood. Studies indicate that when a primary tumor breaks through a blood vessel wall, blood's "stickiness" tears off tumor cells the way a piece of tape tears wrapping paper. Until now, no one knew the physical forces involved in this process, the first step in metastasis. Using a statistical technique employed by animators, scientists created a new computer simulation that reveals how cancer cells enter the bloodstream. The researchers present their work in a paper accepted to the American Institute of Physics (AIP) journal Physics of Fluids.

Full Article here
Searching for new drug targets
[H]ard|Folding Administrator

Posts: 102
Points: 2,813,787
Work Units: 6,596

Posted: Sat Jul 28, 2012 12:22 am
Stanford has been busy this month, quite a bit of new news.

Searching for new drug targets

Most rational drug design efforts assume the target protein exists in a single structure and that the structure of one region of the protein--called the active site--allows the protein to perform some function. Once this assumption is made, the only way to manipulate a proteins activity is with inhibitors that bind the active site tightly enough to block it from performing its intended function. Unfortunately, this strategy only works for ~15% of proteins, greatly limiting the number of proteins we can manipulate for therapeutic purposes.

In a recent article published in the Proceedings of the National Academy of Sciences (link), I showed that simulations run on Folding@home can reveal new ways of manipulating a protein's activity. Specifically, I start off by recognizing that proteins are actually flexible and then use Folding@home to enumerate the different conformations a protein adopts. I then use statistical analysis to find parts of the protein that can communicate with the active site through a process called allostery. These regions--called allosteric sites--are attractive drug targets as the binding of small molecules to them can be communicated to the active site, ultimately affecting activity.

Full Article here.

Bonus for A4-core based projects

We've noticed a significant number of high priority projects are trailing behind existing projects. Newer projects are aimed at interpreting and guiding experiments where the full power of Folding@home (F@h) is essential to continue pushing the boundaries of scientific and medical discoveries.

The main cause of this issue is the core version needed to run these simulations. Many of our newer SMP projects use the A4 core, which has several scientific advancements, while existing projects use the still important A3 core. The A4 core is not compatible with Clients below version 6.34 and many donors are still running these older Client versions.

Full Article here.

New GPU-powered algorithms

Guest post from Dr. Xuhui Huang, Hong Kong University of Science and Technology

In this post, I want to introduce a new GPU-powered clustering algorithm we recently developed to analyze the large molecular dynamics simulation datasets generated by Folding@home. Folding@home can generate enormous sets of protein structures. A critical step in analyzing these large datasets involves some form of reduction in the dataset, usually in the form of clustering. We recently developed a GPU powered clustering algorithm using the intrinsic properties of a metric space to rapidly accelerate the clustering. Overall, our algorithm is up to two orders of magnitude faster than the CPU implementation, and holds even more promise with the ever increasing performance in GPU hardware.

Full Article here.

Slow unfolded-state structuring revealed by simulation and experiment

Guest post from Dr. Vincent Voelz, Temple University

Using protein folding simulations alongside experiments remains challenging because the two techniques often "see" very different things. Simulation trajectories "see" every atom in a single protein in microscopic detail, while experiments often "see" only bulk properties averaged over large ensembles of molecules. For example, in the last few years, we have built kinetic network models of ever larger and slower-folding proteins. These models can have huge numbers of states and many possible folding pathways, yet experimental folding kinetics can be fit to models having only two or three states.

Full Article here.
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