Hi all,

I don't quite get this:
If there is already a native shape ("experimentally determined true shape") why still do the calculations?

Especially (quoting from the FAQ)
"... When starting from a random conformation, however, we've observed that the native state is never sampled. By applying more computing power to the problem, we can sample many more conformations, and try different search strategies to see which is the most effective."
The first thing that comes to my mind is "well, then your algorithm does not work".

That would be fine if the purpose of the search is determining proper algorithms, but from everything I read the purpose is determining the lowest energy states of the proteins.

Thanks
Jan

Hi all,

I don't quite get this:
If there is already a native shape ("experimentally determined true shape") why still do the calculations?

Especially (quoting from the FAQ)
"... When starting from a random conformation, however, we've observed that the native state is never sampled. By applying more computing power to the problem, we can sample many more conformations, and try different search strategies to see which is the most effective."
The first thing that comes to my mind is "well, then your algorithm does not work".

That would be fine if the purpose of the search is determining proper algorithms, but from everything I read the purpose is determining the lowest energy states of the proteins.

Thanks
Jan

Hi Jan

The purpose of the project is to improve the search algorithms for a number of reasons, including being able to replace x-ray crystallography with virtual modeling, although I believe there are also some members of the Bakerlab working on specific targets.

HTH
Danny

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That would be fine if the purpose of the search is determining proper algorithms, but from everything I read the purpose is determining the lowest energy states of the proteins.

...it's about determining proper algorithms to find lowest energy state. By testing new variations of the algorithm against known proteins, you can quantify how much better your prediction is getting. If you run against unknowns, how can you define if your new approach is any better or worse then what you were doing before?

So yes, Rosetta@home is a research project, not a production machine. It seeks to learn how it all works, not to run against each one of 100,000 proteins in a list.

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Rosetta Moderator: Mod.Sense

The better a program can predict a protien shape. The better it will be at finding say a molecule that can dock to a cancer protien thus keeping the protien from growing.

In other words, it will design better drugs.

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The description of Rosetta leaves it easy to assume that the object of research is the protein rather than the algorithm. Thanks for clarifying that.

This is Lucas from the Baker lab, wanted to give a more detailed answer on this.

We are primarily engaged in a few tasks here, all of which we use boinc for:
1) Making better algorithms to predict structures. Mod.Sense pointed this out. Much of the use of boinc is to test out variants of algorithms, totally new ideas, etc.
2) Improving the scoring functions in those algorithms. This gets pretty technical, but you can think of Rosetta software as a search algorithm -- it needs to look around (sampling) and it needs to evaluate what it finds (scoring). Boinc is used to test new methods of scoring, aka new ways to evaluate structures. These methods help structure prediction (1, above) and sequence design (3) below.
3) Design of new proteins for new tasks. This is the inverse of problem (1) where we know the sequence and are predicting the structure. Here we have a structure, or multiple structure ideas, in mind and we want to design a protein that takes on that structure. The structure could be an influenza binder, or a new enzyme to treat a disease. We run Rosetta algorithms to design new sequences for a given structure, and often run that on boinc.
4) When we make a new design, how do we know that it will look the way we want it too? Well, we put it back in to step (1) on boinc, to test if it is at least self-consistent. If boinc doesn't give us back the structure we are trying to make, we might be in trouble.

The majority of folks here in the lab are working on (3) and some are doing (4), and many of us use boinc as a vital tool to make design and design evaluation possible.

Hello, new here and this is my first post. Not sure if this is the correct place to post this but, I'll give it a go.

1. Is it possible to have "twin" proteins? (i.e. 3D "twins"), For example the same sequence just distinct structures both equally lowest energy? How about "triplets" and so on? If so, are there examples of these?

2. I read about the example of a designed TOP7 protein compared to the x-ray crystal structure of TOP7, what accounts for the (RMSD of 1.2 Å) to the design model? I assume that X-ray crystallography has an inherent RMSD of what? How do the two compare?

Maybe too technical, just wondering. I studied Mechanical Engineering, just curious. Thanks.

Armando, those are both excellent questions. I'll answer the first one here.
It turns out that many natural proteins move as part of their natural function -- here the most obvious example would be muscle proteins, like myosin, that change shape in order to exert a pulling force and result in muscle contraction. Therefore any of these proteins have multiple shapes that they can adopt with the same sequence. Usually the change in shape is triggered by a small chemical change, like adding a phosphate group (a phosphorous atom surrounded by negatively charge oxygen atoms) to one of the amino acids in the protein.
Some good examples of proteins that change shape would be DNA polymerases (enzymes that copy DNA), motor proteins (myosin, kinesin), and pumps, like the F1F0-ATPase.
Here's a review on the topic of protein structural changes:
http://www.sciencedirect.com/science/article/pii/S1367593103001753
Here's a more general article on proteins that have very similar sequences but different shapes:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2673347/

However, given these examples of proteins that change shape, or similar-sequence proteins that have different shapes, it is still safe to say that most proteins have one most stable shape that they tend to adopt. This is the "lowest energy" state, and for almost all cases for which we use rosetta-at-home we know that the protein in question does actually fold in to one unique shape.