Topic Name: Biomolecular World : connections among biology and physics, and molecules and computers
Angel E. García Professor of Physics; Senior Constellation Chaired
Professor in Biocomputation and Bioinformatics Rensselaer Polytechnic Institute, Troy, NY
Location: 110 Eighth Street, Troy,New York 12180-3590, United States
How do you study molecules like proteins that change shapes in complex ways, and are too small to see? You need to get small and think fast.
To enter Angel Garcia’s world is to enter a virtual reality. Garcia uses computer simulation to model how proteins change shape in response to the forces of nature. While discussing his work, he moves easily between topics of basic physics, like molecules under pressure, to medical applications, like Alzheimer’s disease. “I am really a physicist, but I apply physics to understand biological systems,” he says.
Let’s start slowly. Physics is the study of matter, of the physical world, and Garcia is asking physical questions about the molecules of life. Biologists study proteins in their usual environment — in cells or in cell-like solutions — that is, in vivo. Garcia’s approach is different — call it in silico. In some sense, it is also tinier and more controlled. He deconstructs proteins to their elements, literally down to their atomic makeup, and then applies Newton’s laws of motion.
“The concepts that you would apply to trace the trajectory of a rice pack thrown from a helicopter, the same physical principles also apply at the molecular level,” says Shekhar Garde, the Elaine and Jack S. Parker Career Development Professor and a colleague of Garcia’s.
After spending most of his career at Los Alamos National Laboratory, Garcia came to Rensselaer last year to direct the Bioinformatics and Biocomputation constellation. “I saw in RPI a place that was changing, a place that was moving up with a very good plan,” he says.
“Angel Garcia is a world-renowned physicist,” says Omkaram Nalamasu, former vice president of research at Rensselaer, who admires Garcia’s intellectual rigor, his passion for science, and his ability to work at the frontiers of science. “We were delighted to have him come and lead this constellation,” which Nalamasu says, lies at the intersection of two focal points of The Rensselaer Plan — biotechnology and information technology.
Garcia already has made his presence felt at Rensselaer. In addition to publishing papers in top-ranked journals and getting grants ($947,000 over five years from the National Science Foundation), he has attracted graduate students and postdoctoral fellows, as well as sponsoring undergraduate research students over the summer (including a Howard Hughes Medical Institute minority fellowship). He also teaches undergraduate physics classes and is busy trying to recruit three new faculty members to the constellation.
Grounded in the fundamentals of physics, Garcia tackles complex biological problems like the study of protein aggregation. A hallmark feature of neurodegenerative diseases like Alzheimer’s and Parkinson’s is the clumps of proteins that clog up and kill nerve cells. It’s a hot research area in biomedical research, in part because understanding how this process happens may lead to therapeutic ways to arrest it.
“Under extreme conditions, any protein aggregates,” says Garcia. “Even myoglobin.” Myoglobin is not associated with clumping and disease in humans — the point is that physical properties of proteins contribute to aggregation. Beyond some initial attraction among two or more proteins that get them together, there must be some energy advantage to staying that way. In other words, aggregation may breed stability.
Garcia takes amyloid-beta — the protein that aggregates in Alzheimer’s disease — and pares it down to the essential segment responsible for clumping — a peptide 1/6 the size of the natural protein. Alone, this peptide can adopt a variety of different conformations. When Garcia adds more molecules to the simulation, they aggregate and the shape becomes less flexible. So with a hexamer, a clump of six peptide chains, only a few configurations were possible.
This simple system provided results important enough to publish in the Journal of the American Chemical Society. But the analysis was anything but simple. If you understand the difference between a photograph and movie — in terms of bits of information — you can appreciate one level of complexity inherent to Garcia’s research. To study protein behavior, to watch it react to different scenarios, requires the element of time. And the element of time means multiple frames of atomic-level description.
“The goal is to try to make a movie of the molecular world — to mimic it in all its detail, down to every atom on the protein,” says Garde. “In a computer.”
Very Small and Very Fast
Take a protein sitting in water, for a simple example. “The whole system is represented by coordinates of the atoms in a computer,” says Garde. We then use Newton’s laws to predict what will happen next. “That gives you the next frame, per se, in the movie. Using that frame, you predict the next frame, and so on.”
It’s super slow motion. Each tiny step represents two femtoseconds of time. (The prefix femto is 10-15; a femtosecond is one-millionth of one-billionth of a second.) “Only when we have a sufficiently large trajectory, we can say something intelligent and interesting about the system that we’re studying.”
With everything from drug design to understanding the process of aging, “the answer always seems to lie at the molecular level,” says Garde. The story of protein structure begins with the human genome, the body’s blueprint for making proteins. Every three-letter segment of a gene dictates a particular amino acid; a string of amino acids makes a protein.
After that, the plot gets more complicated. Based on the size and charge of individual amino acids, not to mention interactions with surrounding media, a jockeying for position occurs and the protein assumes a three-dimensional shape. Predicting what shape a given protein will assume, the protein-folding problem, remains a grand challenge to this day.
With simulation, the progress of knowledge comes from a balance between theory, computation, and experiments, says Garcia. “It’s a constant turnaround. But you don’t get dizzy because it can take a year to turn around.”
In a large basement room of the Low Center for Industrial Innovation, a wall of computer processors stand tall, cooled by more BTUs than required by most other buildings on campus. The 400 units are computing around the clock and they give Garcia more processing speed than he’s ever had before. And yet, even with 300 more — for which there’s space in the computer room — speed is the limiting factor in Garcia’s research.
To illustrate, Garcia uses an example from his group. Postdoctoral fellow Ryan Day is broaching a research question that would take 10 years of processing time with present computing power. “With supercomputing ability, we can do it in weeks,” says Garcia.
With the recent announcement of the Computational Center for Nanotechnology Innovations at Rensselaer, Garcia is getting more than he bargained for. Initially he was thrilled with the prospect of getting one BlueGene machine, a highly parallel computer, from IBM. Now the supercomputing center — a collaboration among Rensselaer, IBM, and New York state — may contain 20 BlueGene machines.
“So imagine. One was getting me excited,” says Garcia. “Twenty scares me.” He chuckles with something like glee, and then speculates on what it means. “It is, essentially, what’s there that hasn’t been tried? Because it’s a whole new scale.” Even if it’s a rough model, he says, that kind of computing power demands that he explore something new. “How a cell works for example. I don’t know if I will dare to do that, but that would be the kind of thing that should be explored somehow.”
It’s not like Garcia hasn’t taken on the unknown before, says Jose Onuchic, director of the Center for Theoretical Biological Physics at the University of California-San Diego. Onuchic collaborated with Garcia on the first simulation of fully folding a protein in a computer. “Angel is well known as the one of the leading figures in molecular dynamics simulation — both in obtaining new results and in creating new simulation tools,” he says.
Onuchic points out that Garcia also creates good scientists. He’s trained a great number of postdoctoral fellows, who now hold top positions in academia and government research labs.
“He was a fabulous mentor — always very positive, stimulating, and full of ideas,” says former postdoctoral fellow Gerhard Hummer, now a senior investigator at the National Institute of Diabetes and Digestive and Kidney Diseases. “And, of course, that enthusiasm is contagious.”
Garcia leads by example, says current postdoctoral fellow Henry Herce. “He’s a good adviser in the whole sense of the word. He doesn’t come with the answer, he comes with a good suggestion.” Appropriately so, he says, because there are no set pathways in research. “You have to let people fly. He teaches you how to fly.”
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