Folding@home

New methods for computational drug design

A key aspect of Folding@home research has been using computational methods to design new drugs, especially for Alzheimer’s Disease.  At the University of Virginia, the Shirts lab is developing methods to leverage the power of Folding@home to develop new drugs to fight disease.  Generally, small molecules work as drugs by binding very specifically to certain locations on important proteins.  For example, an antibiotic works by binding to a protein on a bacteria, thus interfering with the pathogen's internal workings seriously enough to disable or kill it.  By targeting only protein sites that are unique to the pathogen, drugs can act extremely specifically, rather than harming the human body or desired microbes.  The exact same principles can toggle very specific parts of our own body's protein machinery on or off, allowing development of drugs that fight diseases of caused by breakdown, mutation, or malfunction our own cellular machinery, like Alzheimer’s Disease, heart disease, diabetes, and many other conditions. 

However, it is very hard to calculate exactly how tightly a given small molecule will bind to a target protein, or even exactly where and by what mechanism it will bind.  A number of computational methods are used in industry today to estimate the binding affinity of small molecules in the process of drug design, but they mostly rely on approximations that are computationally cheap and very approximate, rather than more expensive methods that have the potential to be much more accurate.  With Folding@home, we now have the capability to perform rigorous evaluations of these more complete methods, understand their limits, and make them more efficient and reliable.

We have been developing our methods working mostly with well-understood model systems, such as FKBP, a protein on the immune system signaling pathway.  Once the methods are well-understood, we will be moving on try to design small molecules to treat AIDS (the HIV reverse transcriptase enzyme, required for DNA to replicate) and influenza (various proteins involved in virus cell entry).  Such molecules will still require significant effort to make into drugs, since drugs also have to dissolve easily, penetrate cells, and not be broken down to quickly, but being able to predict more easily which molecules interact tightly with the intended targets will be a huge step in the right direction. 

As part of our efforts to improve Folding@home infrastructure, we are also working to port new versions of the Gromacs molecular simulation platform to Folding@home and improving the interface and integration between Gromacs and Folding@home. 

Protein folding and viral infection

Understanding protein folding has many possible areas of biological and biomedical impact.  For example, consider one of the major research areas of the Kasson lab at the University of Virginia, namely how the influenza virus infects cells.  In the past, Dr. Kasson and Dr. Pande have studied two aspects of this:  how the influenza virus recognizes cell-surface receptors so it infects the "right" cell types and how small vesicles fuse.  

Dr. Kasson's group is now looking at the function of the viral protein that controls cell entry, a protein called hemagglutinin.  The hemagglutinin protein interacts with cell membranes:  one piece inserts into the membrane, refolds, and alters the membrane in some unknown manner to promote viral entry.  Another piece links the viral and cell membranes and refolds to bring the two together.  We are running simulations on Folding@Home to examine each of these pieces.  Dr. Kasson's laboratory also looks at these processes experimentally.   

Both of these problems involve protein folding.  This extends the problem of understanding folding beyond the "canonical" model of an unstructured protein in water taking on a final shape but instead in the first case a small protein inserting into a lipid membrane and changing shape in response to its environment and in the second case a large protein changing from one shape to another in response to physiological cues.  One could consider these special cases of protein folding or how viruses use protein folding to infect cells. 

Future posts will address methods we have developed to assist in these studies as well as other important problems we work on.  We are also doing methodological work that will improve the efficiency of running Folding@Home simulations and analyzing the results.  The Folding@home community has made an important contribution in providing the computing power for these studies (you can see some of our published work on the FAH papers page), and we are grateful to all involved.

 

 

 

Folding Community Forum site

The FCF site has had some DNS changes and you may not be able to reach it from its normal url (http://foldingforum.org/).  If not, please try this one temporarily:  http://m202.sgded.com/~folding1/

 

Protein Folding and Molecular Recognition

Here's a guest post from Prof. Xuhui Huang's lab at Hong Kong University of Science and Technology, another collaborating labortory in the Folding@home consortium.  Prof. Huang and his lab have made several important methodological applications to FAH (for more details, see this review article) as well as important research into the molecular nature of Huntington's Disease.  Here's an update from Prof. Huang:

In the past a couple of years, the FAH has greatly helped us on our research on understanding the mechanisms of the molecular recognition processes. Molecular recognition, such as enzymes need to recognize their substrates and drugs have to be designed to specifically bind to certain receptors, is crucial to biology and medicine.  Experimentally probing the chemical details of molecular recognition events is challenging, while computer simulations have the potential to provide a detailed picture of such events.  With the help of the FAH donors, we are performing large-scale simulations on a group of Periplasmic Binding Proteins aiming to reveal the general relationships between protein structures, its intrinsic dynamics, and mechanism of recognition process. 

The FAH projects related to the above research are between 7700 and 7712.  We greatly appreciate the help from all the FAH donors, beta testers, and the rest of the FAH team to make our research on molecular recognition possible.

 

LTMD: Key new technology for accelerating folding and misfolding simulations in FAH

Here's an update one one of our key projects looking into protein folding, performed in collaboration with Prof. Jesus Izaguirre's lab at Notre Dame.  Below is an update from Prof. Izaguirre on the progress of this project.

The Izaguirre Lab at the University of Notre Dame (http://www.nd.edu/~lcls) has been collaborating with the Pande Lab at Notre Dame to produce a new GPU core that leverages the amazing speed of OpenMM implicit-solvent force calculations (the heart of the GPU core in Folding@home) with new Long Timestep Molecular Dynamics (LTMD). This combination currently allows nearly a 10-fold speedup over OpenMM for systems as small as the WW domain (35 residues, 544 atoms) up to the Lambda repressor (80 residues, 2000 atoms). This translates into about 10 microseconds per day of simulation, which brings single trajectory millisecond simulations closer to FAH. 

In collaboration with Cauldron Development (lead by Joseph Coffland, primary developer of the Folding@home client and also some cores), we hope to produce a GPU core that might be the first hybrid CPU-GPU core. There are technical questions on how to best do this, and we will engage our enthusiastic beta-tester GPU donors to discuss how to best approach this core when we are closer to production mode. 

Going forward, we will continue to improve the LTMD GPU technology to obtain larger speedups for ever larger and biomedically relevant systems. A particularly excitement development will be the extension of LTMD GPU technology to explicit solvent simulations. 

As far as scientific simulations, we are simulating the folding of about 80 mutants of the Pin1 WW domain, a protein implicated in some cancers and Alzheimer's disease. Understanding the role of mutations on misfolding can have important biomedical consequences, since many diseases have at least some component of misfolding of proteins. Another exciting project we are about to start is to simulate the dimerization during folding of proinsulin and proinsulin mutants, which results in some types of Type IA young and adult onset diabetes. 

Thanks to the FAH donors, testers, and to the Pande Lab for their generosity and leadership which has allowed our technological developments and simulations to come this far. 

 

An image of the Pin WW domain.

 

stats back up – we're looking into missing interval

The stats system went down last night and is now back up.  We are working on recrediting the WUs that came in last night.  WUs coming in now should be getting credit as always.

Update on "bigadv-16", the new bigadv rollout

As we've mentioned earlier, we have been preparing changes to the bigadv system –– both an increase in the number of cores required (and a shortening of deadlines to match) and the release of some new bigadv projects.  The motivation for the core changes is as follows:

Bigadv is intentionally intended for the most powerful machines, which makes it naturally a moving target.  Our goal with bigadv is to utilize the most powerful segment of (CPU-based) machines in the FAH project to work on projects that are particularly large (memory utilization, upload/download requirement) and require a large amount of computation.  We are all fortunate in that processors get faster over time, so the highest-performing tier of donor machines also gets faster over time.  We have a lot of exciting science being enabled by FAH donors, and it takes place at all levels of computational requirement and performance sensitivity.  So it wouldn't help the project to have 50% of machines running bigadv.  But it also wouldn't be a good match to have some of the older and/or bandwidth-limited machines running these most performance-sensitive projects.  

As previously announced, our plan is to shorten the deadlines of the BA projects. As a result, assignments will have a 16 core minimum.  We've been developing the new projects for the new "bigadv-16".  This development has taken a bit longer than we expected, but we are now completing internal testing and reading beta projects for bigadv-16.  We are bringing a new server online for bigadv-16.  It will start by offering a new class of bigadv projects, but we will soon add in a number of projects on the same server that are more similar to bigadv projects donors have already seen.  We want to make these work units available for testing, but at the same time we are still examining the points yield of these bigadv projects.  So the points valuation remains a work in progress; we may alter points, bonuses, and/or deadlines in the process of testing.

Please expect a beta announcement soon for testing these new bigadv-16 work units.  Then, after the new bigadv-16 projects stabilize, we will bring the bigadv-12 projects into line (points, deadlines) with the bigadv-16 projects and convert all projects to bigadv-16.  We are not sure of the timescale for this yet, as we'd like to test the new projects in a thorough manner.  We will endeavor to be as transparent as we can regarding upcoming changes in the bigadv program.  Bigadv-8 projects will likely be phased out (and indeed are mostly not being assigned at this time).

As a side note, we recognize that the number of cores is a somewhat crude measure for system performance.  Long-term, we have some ideas on how we'd like to improve this and use better metrics.  But in the near term, we are using this admittedly imperfect metric.

Thank you for folding and for your support of the bigadv program and FAH more generally.