Yes, I definitely agree. We (I'm the head of the Folding@Home project) are actively working on this with the Brook group and other collaborators at Stanford. Stay Tuned!
For those that are curious, there as been some discussion on the Folding forum http://forum.folding-community.org/
Implicit solvation is a good question and deserves a long discourse. The bottom line in my mind is that while it's a pretty harsh approximation, it's still not too bad. Actually, our work shows that. However, we are moving to explicit solvation anyway to compare for sure.
I'm not sure how predictive the Nussinov work was considering the methodology.
Finally, I agree that we need to mine the trajectories more. We're doing that, but perhaps more importantly, we will release the data for others to mine too!
I think you're missing the point. This is not about structure prediction, it's about understanding HOW proteins fold -- info which will never come from Xray or NMR. In terms of practical applications, we're now running simulations of Alzheimer AB peptides to understand their misfolding properties.
Actually, we're in the experimental error. Keep in mind that folding time distributions are exponentially distributed (not Gaussian). This means that the std devs will be big just by their nature. 7.5 vs 6 are indistinguishable statistically.
sorry to hear you say that. Our emphasis has always been on the science (hence we have important results whereas the other "science" d.c. projects don't). What I would say to you is the results speak for themselves.
Slashdot Mirror
Yes, I definitely agree. We (I'm the head of the Folding@Home project) are actively working on this with the Brook group and other collaborators at Stanford. Stay Tuned! For those that are curious, there as been some discussion on the Folding forum http://forum.folding-community.org/
Implicit solvation is a good question and deserves a long discourse. The bottom line in my mind is that while it's a pretty harsh approximation, it's still not too bad. Actually, our work shows that. However, we are moving to explicit solvation anyway to compare for sure.
I'm not sure how predictive the Nussinov work was considering the methodology.
Finally, I agree that we need to mine the trajectories more. We're doing that, but perhaps more importantly, we will release the data for others to mine too!
V
I think you're missing the point. This is not about structure prediction, it's about understanding HOW proteins fold -- info which will never come from Xray or NMR. In terms of practical applications, we're now running simulations of Alzheimer AB peptides to understand their misfolding properties.
Actually, we're in the experimental error. Keep in mind that folding time distributions are exponentially distributed (not Gaussian). This means that the std devs will be big just by their nature. 7.5 vs 6 are indistinguishable statistically.
sorry to hear you say that. Our emphasis has always been on the science (hence we have important results whereas the other "science" d.c. projects don't). What I would say to you is the results speak for themselves.
in the latest clients, you can turn off the logos if you like. We've been pretty responsive to people's feature requests like that.