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David E. Shaw and the Grand Challenge of Biology


BOSTON -- The word “enormous” kept popping up in David E. Shaw’s opening keynote at the Bio-IT World Conference & Expo on Monday: an enormous number of steps, enormous number of iterations, an enormous increase in processing speed. The challenge his research team faces in trying to reach the goal of millisecond-simulation of protein folding is, indeed, vast. But Shaw’s enthusiasm in meeting this “grand challenge” is also enormous.

Shaw, who heads the Center for Computational Biology and Bioinformatics at Columbia University, spoke on the challenges of simulating protein folding. A protein’s 3D structure determines its biological characteristics. But Shaw said we don’t know the structure of most proteins, such as G protein-coupled receptors, which account for about half of drug targets. Scientists also lack a detailed picture of what most proteins do and how they interact. At best, Shaw said, we have a “parts list,” but we don’t know what the individual parts look like, how they fit together, or how the whole machine works once assembled.

There are two approaches to studying proteins: the wet lab, which is tough because everything is so small, and computer simulation. Citing his background as a computer scientist, Shaw said he likes to figure out how things work at the low level and build computer models on top of that to simulate structure and dynamics.

The gold standard for computation, said Shaw, is molecular dynamics (MD) simulation: Begin with low-level mechanical laws and build to see what the simulation looks like and watch things as they evolve. His group, which includes computational chemists and biologists, computer scientists and applied mathematicians, and computer architects and engineers, is developing computer systems and algorithms to vastly speed up the simulation of protein folding.

Mulling the Millisecond
The “grand challenge of biology,” said Shaw, is to simulate protein folding at the millisecond scale. At this timescale, one can begin to see proteins’ 3D structure, and understand the underlying dynamics that control the process. Drugs, too, often bind to their molecular targets at the millisecond timescale. As an example, Shaw noted that Novartis’ cancer drug Gleevec does not work in everyone. If we could elucidate the reason through simulation, we could design a drug that evades that resistance.

If MD were perfectly accurate and infinitely fast, Shaw said, we would have enormous amounts of data – series of snapshots over a long time of how things evolve structurally – which could then be mined. But today, MD simulation is difficult and takes too long.

To illustrate, Shaw showed an example of protein folding over two nanoseconds of simulated time, which required 3.4 CPU days to simulate using state-of-the-art code and a single processor.

In MD, you must divide time into discrete 1-femtosecond time steps. If the time steps are too long, individual atoms run into each other, get higher energy configurations, and everything becomes unstable. For each individual step, you must compute the interaction between all pairs of particles, determined by molecular force fields. Then you must move each atom a tiny bit and repeat the process a huge number of times.

Two approaches to protein-folding simulation include simulating many short trajectories and simulating one very long MD trajectory. While both approaches are complementary, Shaw’s group practices the second approach, which requires enormous amount of parallelism. To reach their goal of simulating a full millisecond requires an enormous increase in speed -- 10,000 times more speed than single-processors, and 1000 times the speed of the best existing parallel implementations. “We are several orders of magnitude from where we need to be,” said Shaw.

Architecture and Algorithms
Shaw’s strategy for attacking this problem is to design new architectures and new algorithms. His group has designed a specialized, enormously parallel machine dubbed Anton, in honor of Anton van Leeuwenhoek, who built the first microscope and described life at the molecular level. Anton will run on application-specific integrated circuits (ASICs) designed in Shaw’s lab and built by Fujitsu.

Comparing Anton to IBM’s Blue Gene supercomputer, Shaw said Anton will be much faster for MD, but less flexible, because it is designed specifically for simulating MD. “Our machine will be orders of magnitude faster [than Blue Gene] – not because we’re orders of magnitude smarter, but because they have a much harder task, which is designing a general purpose scientific supercomputer.” The projected completion date is 2008.

Shaw’s lab has also created specialized software, dubbed Desmond, for MD. It’s developed to run on Anton but the algorithm can be adapted to run on computational clusters. It’s very scalable but not as accurate as IBM’s Blue Gene, and will be free for universities and non-profits. It will also be sold via a third party.

Anton running Desmond would result in a qualitative change from what’s possible today, and possibly result in qualitatively different set of results. But Shaw stressed: “All this rests on the assumption that speed is enough to give us interesting things.”

But since no one has been able to simulate these timescales, nobody knows what problems will be solved. Shaw cautioned that we don’t know enough about the accuracy of molecular force fields, and that maybe after 100 or so microseconds, a small inaccuracy in the force field calculation “would lead to a very fast way of getting the wrong answer.”

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