Great to see more communication from the Baker Lab! This is what excites me the most:

In response to requests from many of you, we will be posting descriptions of the many scientific problems currently being tackled with Rosetta@Home on the Science message boards in the next couple of weeks--stay tuned! I also want to describe a new research direction we are now embarking on aimed at future cancer therapies. There are a small set of proteins which are frequently found at much higher levels than normal on the surface of cancer cells. We are starting to design small proteins which bind tightly to these tumor cell markers. If we are successful, we have collaborators who will be testing these proteins for their ability to target cancer cell killing agents to the tumors.

You'll be getting my CPU power soon :D

Dear Rosetta@home contributors,

Here is an update on using sparse data to model protein-protein complexes. The ability to predict protein complexes structures has direct impact on computationally designing protein binders. There are several exciting disease-related protein design projects in the Rosetta community. Improvement in the computational methods can increase protein design quality and speed up the design cycle.

Modeling protein complexe structure is equivalent to model two monomeric protein modeling protein and their docking to each other. Considering the challenges in modeling monomer protein structures (CASP), it is not surprising that this is a very difficult problem. In the past year, we have developed a new method to tackle this problem by using sparse experimental data and evolutionary information. Both approaches have been successfully applied to monomeric protein modeling (http://www.sciencemag.org/content/327/5968/1014.short and CASP10). We are combining this to model protein complexes.

Inherent to this problem, we need large amount of computational resources to sample protein-complex conformational spaces. The resources from Rosetta@Home are invaluable and efficient for us to develop, test and apply our methods.

I sincerely appreciate your donation of computer resources to power Rosetta@Home. I hope to update you soon when these results get published.

Best,
Lei

Thank you for the update. It helps to keep us excited to provide CPU time.

Dear Rosetta@home contributors,

Here is an update on using sparse data to model protein-protein complexes. The ability to predict protein complexes structures has direct impact on computationally designing protein binders. There are several exciting disease-related protein design projects in the Rosetta community. Improvement in the computational methods can increase protein design quality and speed up the design cycle.
......

Best,
Lei

Hello Lei,

thank you for your explanations, it keeps many people motivated. Are there any news after first three months to share or are you waiting with everything for the publication? :)

Best Regards,
a.m.

Still no real updates here? No news seems to indicate an aloofness to us mere mortals.

Still no real updates here? No news seems to indicate an aloofness to us mere mortals.

I know what you mean, however it could also be that they're really busy doing science....

Still no real updates here? No news seems to indicate an aloofness to us mere mortals.

I know what you mean, however it could also be that they're really busy doing science....

That's their story and they are sticking to it.

Even as a lay-person, I've had technical discussions with a post-graduate friend working on her phd in mechanical engineering. Sometimes even giving her useful suggestions for her lab experiments.

The advantage of grid computing is the sheer volume of processing. Wouldn't it also be cool to sometimes get some more mind-sharing as well? "Just saying".

Hi Rosetta@home,

I am a new graduate student in the Baker lab, also working on protein-protein docking.
Most recently, I submitted some docking runs with the tag "UN_top154_hboligomer."

My project is more focused on utilizing the docking tools created by previous lab members to validate designs of two-component protein symmetric oligomers. Think, for example, of a clover leaf, which has 3-fold symmetry. That would be an example of a C3 symmetric oligomer. By two-component, I mean that there is a base C3, for example, that has single proteins binding/docking to the outside and "growing" outwards from it.

The docking runs will help me computationally validate designs to decide which ones to order. These designs will serve as building blocks for protein materials that could have applications in general research techniques, health, energy, and environment.

For example, a recent publication from the lab (http://www.bakerlab.org/Accurate-design-of-co-assembling-multi-component-protein-nanomaterials/) highlights symmetric oligomers being used to design protein "nanocages," which could be used for drug delivery or for scaffolding biofuel production.

Thank you very much for donating your computer time for these protocols. Despite not always being in touch with the Rosetta@home community, we do sincerely appreciate what you are doing. I will try my best to continue watching the forum.

Please let me know if you have any questions about what I have said or other older projects that you are still curious about.

Best, natteruw

Dear Rosetta@home contributors,

I am here to give some much overdue updates of "using sparse data to model protein-protein complexes". With the resources from Rosetta@home, I thoroughly tested several computational protocols to dock two proteins guided by sparse data from a benchmark set over 100 naturally occurring protein complexes. In the meantime, I participated partially in CAPRI experiments to blindly predict protein complexes.

With two years of my research and many of your computational resources, we developed an improved workflow to use Rosetta for modeling protein complexes, such as the methodology for doing sampling, and criteria for model selection. I presented this work as a talk in the 54th Experimental Nuclear Magnetic Resonance Conference meeting in 2013. Unfortunately, we did not make enough breakthroughs to publish in a peer-reviewed journal. I learned that more sophisticated types of sampling algorithms to account for protein flexibility are needed to solve this problem.

However, the new protocol turns out to be very useful in another more exciting applications. We currently use this as a tool to validate protein binder designs. Those designed protein binders have many practical applications. We are leveraging Rosetta global docking at R@H to validate in sillico protein designs. This method can improve protein design quality and speed up the design cycle.

I am disappointed not be able to tackle this modeling project. But I am very glad that the protocol we developed are being used for protein engineering validations.

I have moved on to work on protein design in industry since 2014. I sincerely appreciate your donation of computer resources to power Rosetta@Home during the time I have access to it. If you want to learn more about R@H, you can find more from Baker lab at University of Washington. I am sure my colleagues will be happy to tell you how much they have benefit from R@H.

Best,
Lei