Why is the new Folding@home streaming infrastructure (FSI) such a big deal?

One major benefit of the new Folding@home streaming infrastructure (FSI) over the previous FAH “classic” infrastructure (FCI) is that FSI is streaming. So we get clients running on a trajectory 24×7, whereas FCI has timeouts which can take a while to meet, thus making long trajectories harder to calculate.

Longer trajectories are important to us because Markov State Models (MSMs) need some minimal trajectory length to be useful. The streaming approach gets us there faster and more efficiently (in wall clock time) with fewer wasted Work Units.

IP address for foldingforum.org changing Friday, 12/5

A quick heads up for Folding Forum users-

SiteGround, who hosts foldingforum.org will be changing the IPs of their cloud servers this Friday, December 5. The change is required in order to ensure better network maintenance and availability. The change should be invisible to most foldingforum.org users. If you get a “Not Found (404)” error message when trying to view the site, try emptying your browser’s cache or flush your computer’s DNS cache and foldingforum.org should show up.

A subreddit for Folding@home donors to discuss topics

At https://www.reddit.com/r/foldingathome, we’ve created a subreddit  dedicated to improving the bi-directional flow of communication between Folding@home donors, researchers, and developers. Although the official community-driven technical support forum is over in foldingforum.org, that board’s phpbb format makes it challenging for researchers and developers to quickly see what feedback topics are the most popular at a given time. Reddit has several advantages in this regard; it’s easy to upvote topics that are on-topic and helpful, and comments-on-comments feature allows for subtopics to exist without completely derailing the top-level responses.

In short, it’s a better system for certain types of communication than the forum. Although technical support is in the Folding Forum, you can submit topics on the subreddit for general discussions on Folding@home.

Exploring how mutations affect folding using Markov State Models

A powerful method to understand folding and conformational change in proteins has been the Markov State Model (MSM) approach, in which protein dynamics can be described as a network of conformational states connected by forward and backward transition rates. Folding@home has proved to be an extremely valuable tool for constructing MSMs, because large-scale simulations are needed to sample the relevant states (numbering in the tens of thousands or more) and estimate the rates (by observing how many transitions are made over the course of many trajectories).

The Voelz lab has been working on using MSMs to understand how small changes to a protein, like a single-point mutation or chemical modification, perturb the transition rates. Not surprisingly, perturbations affect some states more than others. It turns out that a particular statistical metric, called the surprisal, can be used to identify the states most affected by perturbations. Using this metric, we can reveal the mechanisms by which perturbations affect dynamics; in the future, this may help us better understand the role of disease-causing mutations. We can also use the surprisal metric to choose states on which to focus more simulations, to help us efficiently interrogate the consequences of different chemical modifications. In this way, we hope to be able to use molecular simulation to help design drugs that mimic stable protein folds. The work is described in a new article published in the Journal of Chemical Theory and Computation (http://dx.doi.org/10.1021/ct500827g).

Surprisal metrics help reveal the conformational states of an alpha-helix most perturbed by salt-bridge mutations

Surprisal metrics help reveal the conformational states of an alpha-helix most perturbed by salt-bridge mutations

Support from NVIDIA’s Compute the Cure

I’m happy to announce that Folding@home has been awarded a Compute The Cure Award from NVIDIA.  Their financial support will help build key new infrastructure in Folding@home as well as support our science efforts.   You can see more in their announcement.

Breast Cancer and Her2 Kinase: Projects 9104-9114

We are continuing to make a big push into studying cancer. Next up, is work relevant for breast cancer. Specifically, we have started to study the Her2 Kinase, a part of the EGFR family of Tyrosine kinases, responsible for initiating a host of biochemical pathways. These kinases are critical for regulating cell division and thus mutations within the EGFR family have been linked to various types of cancers, including breast and pancreatic cancer.

The aim of projects 9104 to 9114 is to understand the effect of certain mutations in the kinase domain of Her2. We are also hoping to find new druggable states within the system for creating the next generation of targeted cancer therapeutics, as well as to study the effect of mutations, which will give us insight into mutations present in breast cancer tumors.

her2

Model of ATP bound to Her2 Kinase

A discussion of recent FAH work on ab initio nanoreactor

What’s an ab initio nanoreactor for?

In an ab initio nanoreactor, molecules are allowed to react freely with each other over the course of the molecular dynamics simulation, and then we observe what products come out of it and how the products were formed. Besides obeying the fundamental laws of physics, no additional assumptions were imposed to the system, hence ab initio.

The number of reactant molecules used to seed the simulations was small (50-100 molecules) compared to the number of molecules typically used in experimental methods, but is nonetheless very large from the standpoint of quantum chemistry calculations. To make the reactions occur more rapidly, we periodically push the molecules to the center of the ab initio nanoreactor with a virtual piston. What this does is to make the molecules bump into one another more frequently, and also provide the energy required for certain reactions to take place.

The significance of ab initio nanoreator

Traditionally, experimental methods are heavily relied on to discover new molecules and reaction pathways, and computational methods mainly played a supportive role to complement experimental methods. The results of this study prove that computational methods can also play the leading role in discovery, and can help guide experimental methods by posing new hypotheses and suggesting which experiments to do. It’s especially useful for detecting complex chemical reactions where several things happen at the same step during the reaction process that’s hard to detect via experiments.

The potential applications of ab initio nanoreactors are broad. Because of the ab initio approach coupled with some refinement methods and automatic analysis, we can achieve the goal of discovering new molecules, new reaction pathways and mechanisms in many different settings and environments. For instance, it could contribute to out future understanding of the origin of life, birth of stars, means to increase the rate of chemical reactions, earth’s atmosphere, etc.

Results of the study

We carried out two ab initio nanoreactor simulations. The first simulation started with purely acetylene molecules, and we call it acetylene nanoreactor. The second simulation started with a mixture of chemicals postulated to exist in the early earth atmosphere. The second simulation is the computational version of a famous experiment conducted in 1952 (Urey-Miller experiment) that showed complex building blocks of life could form from simple inorganic molecules (1). We call the second simulation Urey-Miller nanoreactor.

For the acetylene nanoreactor, nearly 100 distinct products were formed after ~500 picoseconds simulation time. Many of these product molecules are large (up to over 70 atoms) due to the tendency of acetylene molecules to form long chains and 3D networks. These products are also diverse, for example some have rings some don’t; some are linear some are branched. After comparing our results with those of previous experiments, we found that the acetylene nanoreactor produced not only similar products, but also new products (2, 3).

For the Urey-Miller nanoreactor, the products were relatively small (up to 16 atoms). Among the discovered products, we have amino acids (which are what proteins consist of), urea (participating in metabolism, and the first byproduct of life to be synthesized in the lab) and a bunch of other molecules, all of which have also been detected in meteorites that may have delivered organic molecules to the early earth (4). Many of these molecules are also found in interstellar clouds (5). In addition to the high diversity of products, the Urey-Miller nanoreactor also identified a complex network of reactions (more than 700 distinct reactions). A significant fraction of these reactions are viable in the common environment we live in. Moreover, we found out that water and ammonia allow reactions to proceed faster with less energy for many of these reactions. Last but not least, hydrogen was found rarely involved in the synthesis of a naturally occurring amino acid, glycine, which supports previous proposals that molecules that tend to lose electrons (including hydrogen) don’t participate in biomolecule formations (4).

Method of analysis

To derive insight from a complex network of reactions, we focus on a particular molecule in the network and investigate the reactions it’s involved in, either it’s the product or the reactant. In this way, it allows us to trace the synthetic pathways that lead from the starting molecules. There could be several different pathways to get from the starting material to our molecule of interest. Some intermediate molecules are more common than the others among these distinct pathways.

References

(1) Miller, S. L. & Urey, H.C. Organic Compound Synthesis on the Primitive Earth. Science 130, 245-251 (1959). Doi: 10.1126/science.130.3370.245

(2) Trout, C.C. & Badding, J.V. Solid State Polymerization of Acetylene at High Pressure and Low Temperature. J. Phys. Chem. A 104, 8142-8145 (2000).

(3) Sakashita, M., Yamawaki H. & Aoki, K. FT-IR Study of the Solid State Polymerization of Acetylene Under Pressure. J. Phys. Chem. 100, 9943-9947 (1996).

(4) Danger, G., Plasson, R. & Pascal R. Pathways For the Formation and Evolution of Peptides in Prebiotic Environments. Chem. Soc. Rev. 41, 5416-5429 (2012).

(5) Menten, K. M. & Wyrowski, F. in Sterstellar Molecules: Their Laboratory and Interstellar Habitat (eds Yamada, K. M. T. & Winnewisser, G.) 27-42 (Springer Tracts in Modern Physics 241, Springer, 2011).

Everything else described here is from Wang, L. P., Titov, A. McGibbon, R., Liu, F., Pande, V. S. & Martinez, T. J. Discovering Chemistry With An ab initio Nanoreactor. Nature Chemistry. 2014. Doi: 10.1038/nchem.2099. The article can also be read about on Nov 10th issue of C&E News: http://cen.acs.org/articles/92/i45/Simulation-Technique-Finds-Reaction-Products.html

Ab initio nanoreactor discovers new reaction pathways

Some very exciting research by Pande Group members Lee-Ping Wang and Robert McGibbon in collaboration with the Martinez Lab was recently published in Nature Chemistry. They report the development and application of the ab initio nanoreactor—a highly accelerated first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms.

Using the nanoreactor, they showed new pathways for the amino acid glycine’s synthesis from primitive compounds proposed to exist on the early Earth, which provide new insight into the classic Urey–Miller experiment. These results highlight the emergence of theoretical and computational chemistry as a tool for discovery.

The nanoreactor simulations were made possible by GPUs and the TeraChem quantum chemistry software; these technologies accelerate the calculation over conventional CPU codes by 10-100x.

Below is the nanoreactor simulation of the classic Urey–Miller experiment.

Bryostatin and Projects 9000-9015

Steven Ryckbosch, a graduate student in the Pande Group recently presented his work on Bryostatin. Folding@home projects 9000-9015 are running simulations to help answer the questions he has about it’s structure and function.

Bryostatin is a naturally occurring marine molecule that shows promising and unique activity against several diseases (most notably, cancer, HIV/AIDS, HIV latency, and Alzheimer’s). Its main target, protein kinase C (PKC), is a signaling protein central to many cellular functions. In its active form, PKC binds its ligand and is associated with the cell membrane, but we currently lack structural information about this complex in its membrane microenvironment.

The simulations performed on FAH will help to provide a structure to the PKC-ligand-membrane complex. This is complicated by the fact that while other compounds such as the phorbol esters also bind to PKC, they exhibit extremely different effects in cells and organisms. The structure and dynamics of this complex would allow us to understand bryostatin and other ligands’ binding mode and thus how to modify and tune it’s structure to improve function or even create new functions as needed for new therapies in the clinic.

Some questions Steven and the group are trying to answer:

How can we use simulation to find protein-membrane structures?
How can ligands modulate protein-membrane interaction?
How are membranes affecting bryostatin function?
How can this inform our design of new bryostatin analogues?

Below is a molecular simulation model of bryostatin bound to PKC’s active site.

bryo3

Bowman lab update on vision

About four months ago, we started a new set of projects to understand the dynamics of some of the key proteins involved in vision.  Now, we have about 600 microseconds of simulation and have begun some preliminary analysis of the data.  Excitingly, it appears we may already have captured the conformational change we were targeting.  More data will be needed to improve the statistical significance of our results, but we are increasingly confident that we’ll be able to begin understanding some of the conformational changes required for vision and, eventually , how mutations lead to various blinding diseases.

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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Installation guide