Rosetta-News

[cappy]

beitrag von mod.sense

For those that have not heard the term before, I believe that the "phase problem" Dr. Baker referred to in his last post is where specific atoms within the protein are in a sort of unstable or orbiting state where they aren't in a fixed position. This throws off the existing methods of prediction because they get a larger then expected fuzzy area and aren't certain what to make of it.

Perhaps Dr. Baker or others can expound on the basics of the problem being solved when they've dug through the influx of EMails their results have generated.

But, in laymen's terms, what he is saying is that the existing prediction methods of crystallizing and x-raying (or NMR) a protein failed to accurately predict the shape. I presume the Rosetta fold and dock protocol was using the scan data as one of it's inputs. And so the program was able to use the scan data and produce a more complete and accurate structure than the existing methods were able to do with the same data.

For some very interesting background, reading in layperson's language, here is a link to an article in Wried from 2001, on the existing x-ray methods of structure prediction. Understand that there is still several weeks of manual work required after the X-rays are taken to interpret the fuzzy blobs that result and reveal the structure. Also, the article was written with just a single protein in mind, but Dr. Baker is discussion a case where there are two of them bound together.

The ultimate goal of Rosetta is to predict the structure using only the chemical sequence of the protein as input. The chemical sequence is much more easily obtained then the X-ray crystallography. But using Rosetta to accurately interpret the X-ray data, and reliably eliminate the weeks of manual work done after the X-ray data is gathered will be a great stride forward.

Understanding how proteins bind together is how treatments will be made for disease. How a protein behaves in the body or it's environment has everything to do with it's shape. And if you bind another protein to it, you have changed it's shape and it will no longer be able to function in the way it did before. So if you bind a strand on to a flu virus, it will no longer behave like the flu virus. You still must study and understand what behavior it does have after binding with your agent, confirming that it doesn't become something somehow worse. But generally, if you can bind to it, you've disabled it and the person no longer has the flu, or HIV, or other disease.

This is why you don't often see statements from the Project Team about "we're working on cancer (or HIV, or the flu) today". Because most of what they are trying to do is master the fundamentals science required to combat all of these things. If you study just one protein to death (pardon the pun!) you will likely devise a program that does not accurately solve other proteins. This is why you study proteins from E. Coli, or snake venom and a suite of others that sound unrelated to HIV and cancer. And you do it each time you make revisions to the program. Others have already solved these protein structures, but they still don't know how to solve it using just the chemical sequence and a computer model. And they still don't have a complete and reliable understanding of how to use that knowledge to make a cure for these diseases.

There are probably some small inaccuracies, or better ways to make my statements here. I am not a protein scientist, just a very enthusiastic message board volunteer. I hope I've shed some light on why Rosetta@home is important. And perhaps part of why it is hard to explain sometimes. It's just not as simple as "we're working on cancer this week". I hope you find this information useful and others with more technical knowledge will use this as a basis for further clarification.

[cappy]

was neues aus dem journal

Today I was asked this question:

"Hi David, I am a user of the BOINC application and running Rosetta. I have searched the website and can't find any sort of overall status on how the entire mapping project is going.

I, and I think many others, would be very interested to seeing some sort of progress indicator on where the project is and some predictions on when the mapping process will be complete at Rosetta's current research/growth rate. (when every possible protein fold has been completely mapped and cross-checked)

Is this possible? Are we at 5%? 10%? Will the project be complete in 5 years? 10 years? This would be great info for the layman that doesn't know much about this subject but is happy to donate computer time for this research."

I thought I would answer this question here for other participants who might be interested as it highlights the different between rosetta@home and most other distributed computing projects.

The answer is that the problems we are tackling with rosetta@home--computing the structures of biological macromolecules and designing new molecules to try to cure diseases and improve human health generally--are long term problems that will not be completely "solved" any time soon. Much of our work is also aimed at improving our methods and algorithms so we can design new molecules and ultimately drugs more accurately.

In most distributed computing projects, a computer program that has been developed is run on large sets of data. the computer program doesn't change over the course of the project, and the progress toward completion can be assessed by determining what fraction of the data set the calculation has been run on and what fraction is left.

This estimate can't be done for rosetta@home because the scope of problems we are trying to solve is much larger, and because we are continually extending rosetta@home to try to solve new problems.

So we can't quantify our progress by giving you a % complete. Instead, the project's contributions and progress can be evaluated by the many scientific publications it has produced, some of which I've tried to summarize in these posts. (the current issue of Nature for example has the article I described below on designing new enzymes to ultimately repair disease causing mutations).

I often get asked about the progress we are making with the invaluable contributions all of you are making to our efforts. While we (unfortunately) have not yet succeeded in developing real world therapies for treating diseases, your contributions have been critical for our advances on the basic science side which should ultimately lead to the development of such therapies. These are documented in the scientific publications that have come out of the project; see http://boinc.bakerlab.org/rosetta/rah_publications.php for a recently updated list. Publication lists are one way you can assess the impact of your contributions to distributed computing projects--hopefully in not too long you will be able to see the impact in new disease treatments!

[Obi]

Besser spät als nie: die sinngemäße Übersetzung (des zweiten Beitrags)...

Ich werde oft gefragt welche Fortschritte wir durch die unschätzbar wertvollen Beiträge machen, die Sie unseren Bestrebungen beisteuern. Obwohl wir bislang (leider) keinen Erfolg beim Entwickeln von realen Therapien zur Krankheitsbekämpfung haben, waren Ihre Beiträge entscheidend für unsere Fortschritte auf Seiten der wissenschaftlichen Grundlagen, die letztendlich zur Entwicklung solcher Therapien führen sollten. Dies wurde in den wissenschaftlichen Publikationen die unser Projekt hervorbrachte belegt; für eine kürzlich aktualisierte Liste diesbezüglich siehe hier (externer Link!). Publikations-Listen sind ein Weg, über den sie die Bedeutung Ihres Beitrags zu Distributed-Computing-Projekten bemessen können -- In hoffentlich nicht allzulanger Zeit werden Sie in der Lage sein jene Bedeutsamkeit bei neuen Behandlungsmöglichkeiten für Krankheiten zu beobachten.

[cappy]

ein neuer Beitrag im Journal

Many of you I'm sure remember the protein folding calculations with the zinc atoms. Graduate student Chu Wang wrote a scientific paper describing the method he developed for predicting the structures of zinc containing proteins and the testing of the method with all of your help. The paper was just accepted for publication in the scientific journal Protein Science, and now scientists everywhere will be able to learn about Chu's method so they can predict structures of this important class of proteins also.

eine übersetzung aus unserem forum von chris bear

Viele von euch erinnern sich sicherlich an die Proteinfaltungsberechnungen mit den Zinkatomen. Aufbaustudent Chu Wang verfasste ein wissenschaftliches Dokument, in dem er die Methode beschrieb, die er entwickelt hatte um die Struktur von Proteinen mit Zinkinhalten vorauszusagen, sowie die Tests dieser Methode mit eurer Hilfe. Das Dokument wurde gerade zur Veröffentlichung im wissenschaftlichen Journal ?Protein Science? akzeptiert, und jetzt können Wissenschaftler von überall Chu?s Methode lernen, und damit ebenfalls Strukturen dieser wichtigen Proteinklasse voraussagen.

[cappy]

ein beitrag aus dem thread design of protein-protein interfaces

Hello,

Since I last wrote (a long time ago) we've made substantial progress in the design of protein interactions. We have developed a new methodology and tested it extensively in recapitulating known protein interactions as well as in the design of new interactions. In all of our tests the results have been very favourable, and most importantly, we now have a handful of designs that show specific binding towards their targets in experiments!

We're obviously very excited about the potential applications of this methodology for the design of inhibitors and binders of drug targets and other biomedically interesting proteins. The target that has most excited us is hemagglutinin from pandemic influenza strains, including of the swine and Spanish flu. This protein is a prime drug candidate and its structure with an antibody that neutralizes these strains of influenza has been solved. We are using our new methodology in order to compute binders of hemagglutinin that may be easier to mass produce than is the antibody. As might be expected, the complexity of designing a binder towards hemagglutinin surpasses that of other protein targets that we had previously attempted and therefore requires substantially more computational resources. We are hoping that with your contributions to ROSETTA@HOME we may obtain many putative inhibitors for experimental testing.

We have other targets in our pipeline that we are very excited about. The design simulations that we will send out will contain a description of the target protein, so for instance, the simulations pertaining to hemagglutinin will be labeled "design of an inhibitor of influenza hemagglutinin". We'll also update this thread with descriptions of other targets and the results of the simulations and experimental characterization. We're excited to see how many new solutions to this problem you will come up with!

[Norman]

gute neuigkeiten aber bekommt man da auch mal ne WU? haben die seit wochen probleme oder was ist da los ?!
ist ja echt schwierig mal ne WU zu bekommen ! im vergleich zu "früher" haben die einem die festplatte vollgehauen

[Dennis Kautz]

gute neuigkeiten aber bekommt man da auch mal ne WU? haben die seit wochen probleme oder was ist da los ?!
ist ja echt schwierig mal ne WU zu bekommen ! im vergleich zu "früher" haben die einem die festplatte vollgehauen

Die Server hatten in den letzten Tagen wohl ziemliche Probleme. Seit gestern bekomme ich aber sowohl bei Rosetta, als auch bei Ralph wieder WUs.

[Norman]

hab jetzt auch welche aber nicht viele.
naja, alles wird gut

[cappy]

ein beitrag aus dem thread design protein-protein interfaces

Hi all,

my name is Tim Whitehead and I've been a postdoc in the Baker lab for about a year. My projects deal with the prediction and design of protein-protein interfaces. In this thread, Sarel has explained the design problem for protein-protein interfaces. Progress in this area can drive the next generation of treatments against pathogens like Mycobacterium tuberculosis or Influenza.

I have been working with Sarel on the design and experimental characterization of designs that target disparate proteins on both pathogens. We have a couple of successes thus far, which we are extremely excited about, but need to have better and better designs to show the validity of our design approach.

In this thread, Sarel has explained why and how we are targeting Influenza by designing a protein inhibitor of a protein that is displayed on the surface of the virus.

For Tuberculosis, our task is slightly more difficult. TB is caused by a bacterium called Mycobacterium tuberculosis. The bacteria is encased in a waxy coating of mycolic acid, which hurts our body's ability to expell the bacterium (for more information, see http://en.wikipedia.org/wiki/Mycobacterium_tuberculosis).
Instead of designing an enzyme that could degrade the coating, we are targeting a protein that is involved in the `synthesis` of mycolic acid. You may have already seen BOINC descriptions that say "docking of designs against 2CGQ" - 2CGQ is the protein that we are trying to inhibit. I am hoping that your contribution from ROSETTA@HOME will give us the increased quality of our designs that we need. Thanks for the help!

[cappy]

ein beitrag aus dem thread design protein-protein interfaces

My name is Eva-Maria Strauch and I am one of the post-docs in the Baker lab working on the design of protein-protein interactions. I am very excited about having the possibility to run on R@H, thanks to your generous contributions! Protein design requires a lot of sampling since there are so many many different possibilities to be considered carefully. This will work out now faster with your help!

I am currently designing proteins to bind to a model system which we use to calibrate our computational protocols. This particular target is the back end of a human immune-system antibody (Fc fragment, which is what you will see under the work unit descriptions http://en.wikipedia.org/wiki/Fc_region). The cool thing about Fc is that nature provides us with several proteins that bind to it, hence it taught us very important lessons on how to design possible binding proteins to it. We now want to see whether we've figured this one out and can mimic nature's ability to come up with binders towards this target. Success will further contribute to the optimization of our protocols to generate several protein-based inhibitors to attack cancer and infectious diseases. Questions we want to answere with this design project are: How well do we perform in comparison with nature? and if we understood all the information we got out of those native interactions, are we able to produce several de novo binding proteins that mimic the atomic interactions seen with native interaction Fc has with different proteins? This project will contribute to our understanding of how proteins recognize one another. Promising designs will be experimentally verified and also further characterized to see how well we performed, and what was possibly missing that we should include into our computational design protocol.
Thanks again for your contribution!

[Torbjörn Klatt]

ein Versuch einer deutschen Übersetzung:

Ich bin Eva-maria Strauch und arbeite nun nach meiner Promotion im Baker Lab an der Anordnung von Protein-Protein Wechselwirkungen. Ich bin begeistert die Möglichkeit zu haben, mit R@H zu arbeiten - dank Ihrer großzügigen Spenden! Auf Grund so vieler verschiedener Möglichkeiten, die alle berücksichtigt werden müssen, verlangt die Proteinentwicklung viele Versuche. Durch Ihre Hilfe geht das jetzt schneller!

Derzeit entwickel ich Proteine, die an ein Modelsystem binden sollen, welches wir zur Kalibrierung unserer Berechnungen nutzten. Das ausdrückliche Ziel ist das hintere Ende eines Antikörpers des menschlichen Immunsystems (Fc Fragment, welches Sie in den Workunit-Beschreibungen sehen werden http://en.wikipedia.org/wiki/Fc_region). Das Tolle an Fc ist, dass uns die Natur verschiedene Proteine liefert die dort binden - und so uns lehrte, wie mögliche bindende Proteine auszusehen haben. Nun wollen wir sehen, ob wir die richtigen Schlussfolgerungen gezogen haben und die Natur nacheifern und Binder an dieses System entwickeln können. Der Erfolg hier wird es uns ermöglichen unsere bisherigen Protokolle zu optimieren und verschiedene proteinbasierende Inhibitoren erstellen, um Krebs und infektiöse Krankheiten zu bekämpfen. Fragen, die wir mit diesem Gestaltungsprojekt beantworten wollen, sind: Wir gut schlagen wir uns im Vergleich mit der Natur? Und wenn wir alle Informationen aus den natürlichen Wechselwirkungen verstanden haben, sind wir dann in der Lage verschiedene de novo Bindungsproteine zu kreieren, die die atomaren Wechselwirkungen haben, wie wir sie bei Fc mit verschiedenen Proteinen sehen? Dieses Projekt trägt dazu bei zu verstehen, wie Proteine einander erkennen. Vielversprechende Entwürfe werden experimentell bestätigt und genauer charakterisiert, um herauszufinden, wie gut wir mit dem Entwurf waren und was möglicherweise fehlte und wir in unser Berechnungsmodell einbauen sollten.
Danke noch einmal für Ihre Mitwirkung!

[cappy]

danke bohne für die übersetzung

ich hab mich jetzt auch im seti.germany forum angemeldet, vllt hilft der ein oder andere dort mit dem übersetzen, sowie das schon hier und bei uns passiert *leider manchmal doch etwas sporalisch nur* das könnte noch besser gehen