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Using just an upgraded desktop computer
equipped with a relatively inexpensive graphics processing card, a team of
computer scientists and biochemists at the University of California, San Diego
has developed advanced GPU accelerated software and demonstrated, for the first
time, that this approach can sample biological events that occur on the
millisecond timescale.
These results have the potential to bring
millisecond scale sampling, now available only on a multimillion dollar
supercomputer, to all researchers, and could significantly impact the study of
protein dynamics with key implications for improved drug and biocatalyst
development.
With some innovative coding, a GPU
(graphics processing unit) that retails for about $500, and the widely used
software package of molecular simulations called Amber
(Assisted Model Building with Energy Refinement), the researchers were able to
run a simulation showing the same five long-lived structural states of a
specific protein as observed in a simulation conducted by D.E. Shaw
Research’s Anton,
a purpose-built molecular dynamics (MD) supercomputer. The Anton
simulation was conducted over a period of slightly more than one millisecond—or
100 times longer than the previous record.
“This work shows that using conventional,
off-the-shelf GPU hardware combined with an enhanced sampling algorithm, events
taking place on the millisecond time scale can be effectively sampled with
dynamics simulations orders of magnitude shorter (2000X) than those
timescales,” the researchers wrote in their paper, published in the Journal
of Chemical Theory and Computation.
The enhanced sampling algorithm refers to
the use of accelerated molecular dynamics, or aMD, a method that improves the
conformational space sampling of proteins when compared with conventional
molecular dynamics simulations, or cMD.
Specifically, the UC San Diego researchers
analyzed the bovine pancreatic trypsin inhibitor (BPTI), a small protein with
58 residues. BPTI was the first protein to be simulated, in 1977, with J.
Andrew McCammon, the Joseph E. Mayer Chair of Theoretical Chemistry at the UC
San Diego, as lead author on that milestone research.
“The breakthrough described in the new
paper was achieved by combining advances in theory and in computer technology,
but other types of resources such as SDSC’s new Gordon supercomputer are
also increasingly needed for large, data-intensive simulations,” said McCammon,
part of the research team on the latest findings. McCammon is also a chemistry
and biochemistry professor in UC San Diego’s Division of Physical Sciences, a
Distinguished Professor of Pharmacology at UC San Diego, Investigator of the
Howard Hughes Medical Institute, and a Fellow with the university’s San Diego
Supercomputer Center (SDSC).
While the team’s aMD simulation was only
500 nanoseconds long, or .0005 of a millisecond, the group was able to sample
all of the structural states seen in the longer timescale simulation
run on Anton.
“In just 500 nanoseconds, we saw the same
things as in the Anton simulations, which we used as an excellent
benchmark,” said Romelia Salomon-Ferrer, an SDSC postdoctoral research fellow
and member of the team who ported aMD to Amber. “We were able to cover that
same space faster. One could compare that to having a choice between taking a
train or a plane to San Francisco. The distance is the still the same; however
the plane would get there faster. But this would also be a very particular
plane in the sense that it is also relatively inexpensive.”
In addition to potentially broadening
access among researchers by enabling desktop simulations, the UC San Diego
research also marks the longest aMD simulation of a biomolecule to-date, as
well as the first “apples-to-apples” comparison of an aMD simulation versus a
very long cMD simulation.
“The key to this work has been to sit down
and rethink the problem from the beginning,” said Ross Walker, an assistant
research professor with the SDSC, principal investigator (PI) and corresponding
author of this research. “We had already massively accelerated conventional MD
on GPUs but even this was not going to be sufficient to allow us to routinely
sample conformational events taking place on the millisecond timescale. By
combining our experience with conventional MD on GPUs with the enhanced
sampling provided by accelerated MD methods, we were able to exploit both
providing, for the first time, the ability to routinely simulate events that
take place on the millisecond timescale.”
“Furthermore, GPUs offer the potential for
supercomputing performance on the average desktop computer, giving researchers
the ability to test multiple hypotheses in real time,” said Walker, who also is
an adjunct assistant professor in UC San Diego’s Department of Chemistry and
Biochemistry, and an NVIDIA CUDA Fellow. “The
NSF-funded work we are doing in the Walker Molecular Dynamics Lab at SDSC to
develop GPU accelerated software promises to transform how scientists approach
applying molecular dynamics techniques that may ultimately lead to the design
of new drugs and biological catalysts.”
“Running the entire MD simulation on the
GPU as opposed to other approaches has really allowed us to run them much more
efficiently, both in terms of conventional MD and now accelerated MD,” said
Levi C.T. Pierce, lead author of the paper and a postdoctoral research fellow
with SDSC and the university’s Department of Chemistry and Biochemistry. “The
conventional MD in Amber was completely rewritten to run on the GPU, while the
enhanced sampling method, aMD, has been coded into the GPU, allowing us to
access these long time scale dynamics.”
The researchers, however, cautioned that
while aMD is very useful for the exploration of conformational space—the
different structures explored as the protein fluctuates—it does not reproduce
the exact timescale of these fluctuations.
“Accelerated molecular dynamics may not
solve the entire problem, but it is a really good initial tool,” said
Salomon-Ferrer. “By using aMD along with Amber, we can lower the financial and
logistical barriers to research, and one particularly important characteristic
of aMD is that researchers don’t really need to know anything about the
complexities of a specific protein beforehand.”
