David E. Shaw

D.E. Shaw Research

Molecular Movies: Feature-Length Simulations of Protein Dynamics

Monday, February 29, 8:00-9:30 PM, Los Angeles Convention Center

Being named National Lecturer is the highest annual award bestowed by the Biophysical Society. In addition to presenting the National Lecture, the recipient provides a molecule or figure that depicts his/her research. That figure is used in the background design for that year’s Annual Meeting print and web announcements.

His featured image depicts active and inactive structures of the epidermal growth factor receptor (EGFR) within its membrane environment. EGFR plays a central role in regulating cell growth and survival, and in the development of many forms of cancer.

Get to Know the National Lecturer

David E. Shaw, Chief Scientist, D.E. Shaw Research, always believed that he would work as a scientific researcher; he never imagined the unexpected detour he would take into the world of finance, as a pioneer in quantitative trading. Shaw’s father was a theoretical plasma physicist, his mother a researcher in education, and his stepfather was an economist and professor at the University of California, Los Angeles (UCLA). “I was raised in Los Angeles, near UCLA, and my parents used to take me there so frequently that it was some time before I learned the difference between a university and a public park,” he recalls. “They looked pretty much the same to me, though the university had a wider range of interesting things going on, and was generally more entertaining.” 

Shaw attended the University of California, San Diego, where he double-majored in mathematics and in applied physics and information science. He then earned his PhD from Stanford University in 1980. Shaw wrote a doctoral dissertation that provided a theoretical framework for a new class of computer architectures and algorithms that could be shown to offer asymptotically superior performance for certain mathematical operations related to artificial intelligence and database management.

He joined the faculty of the Computer Science Department at Columbia University, conducting research on the design of massively parallel special-purpose supercomputers for various applications. “Although my thesis at Stanford hadn’t involved the construction of any actual hardware,” Shaw explains, “after arriving at Columbia, I received government funding to actually start building one of the weird supercomputers I’d designed on paper.” The machine could not be constructed using standard components, so Shaw and his students designed their own integrated circuits, and then connected them to assemble a small-scale working prototype. They wrote code for the machine that implemented some of Shaw’s algorithms. “We were thrilled when the whole thing actually started working,” Shaw recalls.

Hooked on the idea of designing and building these special-purpose super¬computers, Shaw saw that building full-scale machines would require a much larger budget than government grants could likely provide. He wrote a business plan for a proposed startup venture that would manufacture massively parallel supercomputers for commercial use, and began meeting with venture capitalists. 
It quickly became clear to Shaw that this venture would not take off, but in the course of seeking funding, he had a chance meeting with executives from Morgan Stanley that led him on a career detour. “The executives I met with at Morgan Stanley told me that someone there had discovered a mathematical technique for identifying underpriced stocks,” Shaw says. “A group of financial and technical people there had written some software that was using this technique to make investment decisions on a fully automated basis, and they were consistently earning an unusually high rate of return.” Shaw was intrigued that they were using quantitative and computational methods in the stock market, “and I couldn’t help wondering whether state of the art methods that were being explored in academia could be used to discover other investment opportunities that weren’t visible to the human eye,” Shaw, explains. Though he had no experience in finance, in June 1986, Shaw shaved his beard, put on a suit, and left academia for a stint on Wall Street.

In 1988, Shaw started his own investment firm, D.E. Shaw & Co., which initially focused ex¬clusively on the application of quantitative and computational methods to investment manage¬ment. For the first few years, Shaw was directly involved in much of the firm’s research, but as time went on and the company expanded, Shaw found himself spending less time on research and more on management. “I could feel my scientific and mathematical skills beginning to atrophy,” he says, “and I found myself missing the days when I solved technical puzzles for a living.”

Shaw wanted to return to full-time research, and hoped to contribute to the search for new, poten¬tially life-saving drugs. He also wanted to design algorithms and machine architectures, which he had always enjoyed. His sister, Suzanne Pfeffer, professor of biochemistry at Stanford University, brought Shaw by the office of Michael Levitt, who was sitting at his computer running a molecular dynamics (MD) simulation. Shaw had never seen one before. “I thought it was incredibly cool,” he says. He later connected with Rich Friesner, who tutored him on quantum chemistry, statistical mechanics, protein structure, and other relevant subjects. “Rich believed that MD simulations had the potential to provide important insights into the behavior of biologically significant molecules, but were so computationally demanding that many biological processes couldn’t be simulated long enough to yield such insights,” Shaw says. “I convinced myself that it might be possible to design special-purpose hardware and algorithms that could simulate the dynamics of biological macromolecules over periods a couple orders of magnitude longer than had been feasible on conventional supercomputers.”

With a research direction in mind, he founded D.E. Shaw Research in 2001, and put together an interdisciplinary team of researchers. “Since then, we’ve been working together on the design of novel algorithms and machine architectures for high-speed molecular dynamics simulation, and on the application of such simulations to biological research and computer-aided drug design,” Shaw explains. “Our research focuses on the structural changes associated with protein folding, protein-ligand binding, molecular signaling, ion transport, and other biologically significant processes. We don’t have our own wet lab, but we often collaborate with experimentalists, both to validate the phenomena we observe in our simulations and to exchange hypotheses and ideas for further studies.” 
One such experimentalist is Arthur Horwich, professor of genetics at Yale University School of Medicine, who met Shaw after the latter visited Yale for a seminar. The two discussed the possibility of working together on simulating the binding of a non-native polypeptide chain to the hydrophobic lining of a ring of GroEL. “That [first] conversation was just electrical,” Horwich says. “He immediately saw what we wished to do and suggested I come down to D.E. Shaw Research in New York for a day, to chat with his team and consider all of the aspects of such a simulation. […]We realized that this experiment was a little beyond reach, but we had a lot of fun together considering this. David is one of the most thoughtful and generous people I have ever met.”

Walter Englander, professor of biochemistry, biophysics, and medical science at the University of Pennsylvania and one of Shaw’s colleagues in the protein folding field, agrees with this assessment, “He is very smart, focused—but self-effacing— generous, hard-working, eager to give credit rather than take it. [Shaw and his group] freely share their results and make their detailed calculations available to whoever asks,” he says. 
Shaw has found great success by applying the skills and knowledge acquired in one field to others, approaching problems from a fresh vantage point. He recommends that young scientists consider an interdisciplinary path. “Milking an existing research paradigm to extend the frontiers of an existing research area can be important and gratifying,” Shaw says, “but the juiciest, lowest-hanging fruit is often found in interstitial research areas that haven’t yet been explored, and in the use of techniques and technologies borrowed from other fields. I also recommend flossing your teeth. You’ll thank me when you’re older.”