The computer performed two types of simulations of well-studied systems: In the first, Anton's 100-microsecond simulations of the protein domain FiP35 show that the domain folds and unfolds in the same progression of steps each time—a surprise to some researchers.
I wonder just how many Runs/Clones they ran to verify the statistical validity of the claim that this is always true, or just worked for the particular case(s) that they ran.
The second simulation involved a longer, one-millisecond study of the dynamics of the already folded protein bovine pancreatic trypsin inhibitor (BPTI), which explored the intricate motions of protein in water. Results were published in Science (2010, 330, 341).
See also
http://folding.stanford.edu/English/Papers#ntoc4
Anton was built solely for the purpose of molecular dynamics (MD) simulations of the behavior of large biological molecules. The machine, which was completed more than a year ago, stakes its claim to fame on its ability to perform lengthy simulations that aren't possible on current supercomputers. The new results mark the first significant test of Anton's prowess.
Note that he carefully compares their system to other supercomputers, thereby excluding virtual supercomputers like FAH. That doesn't minimize what he has actually achieved but I still don't see that we have a scietific "record" for MD. Where did that work come from? Maybe I should remove it from the title.
MD simulations, which track the motions of every atom in a large molecule, are limited in their time scales by computer power and architecture. It can take months for a supercomputing system to simulate only tens of microseconds of a protein's dynamics. But many proteins fold on the millisecond time scale.
That's an extremely important statement. We have to keep reminding the FAH donors of what that means from the perspective of a Donor.
Anton, its creators report, is capable of performing millisecond-long simulations of systems containing tens of thousands of atoms in 100 days—100 times faster than with current supercomputers.
Again comparing to current supercomputers.
I suspect that he might be using GROMACS but that's not stated. He does seems to be using explicit solvents.
Once a simulation starts, it can't be adjusted. "You cannot change conditions on the fly; it has to be hands off," Schulten says. Only about 10% of protein-folding problems can be done this way, he adds.
So it's hard for him to generate good statistics ... one Run/Clone at at time. If a protein folds correctly 99% of the time, FAH has to analyze enough different possible folding sequences to have some of them show up in the 1% ways that it might actually mis-fold.
Recently, Shaw Research donated another Anton machine to the Pittsburgh Supercomputing Center for research use by universities and other nonprofit institutions through the National Resource for Biomedical Supercomputing.
Schulten's group is one of 45 that were allotted time on the machine by the National Science Foundation. His group began simulations last week, and "it's working very well," he says.
Collaborating with FAH might be a benefit to both projects. In fact, all 45 might do well to collaborate. Presumably some of that happens at the scientific conferences that they attend.
I'll bet some of the PG researchers would be happy to get their hands on some supercomputer time to augment what the donors contribute though FAH. I'm sure that the NSF considers many researchers, though, and might choose to allocate their money to somebody who DOESN'T have access to FAH.
